Setting up access to a headless Raspberry Pi is one of those tasks that should take a few minutes, but for some reason always seems to take much longer. The most common method is to configure Wi-Fi access and an SSH service on the Pi before starting it, which can go wrong in many different ways. This author, for example, recently spent a few hours failing to set up a headless Pi on a network secured with Protected EAP, and was eventually driven to using SSH over Bluetooth. This could thankfully soon be a thing of the past, as [Paul Oberosler] developed a package for SSH over USB, which is included in the latest versions of Raspberry Pi OS.
The idea behind rpi-usb-gadget is that a Raspberry Pi in gadget mode can be plugged into a host machine, which recognizes it as a network adapter. The Pi itself is presented as a host on that network, and the host machine can then SSH into it. Additionally, using Internet Connection Sharing (ICS), the Pi can use the host machine’s internet access. Gadget mode can be enabled and configured from the Raspberry Pi Imager. Setting up ICS is less plug-and-play, since an extra driver needs to be installed on Windows machines. Enabling gadget mode only lets the selected USB port work as a power input and USB network port, not as a host port for other peripherals.
An older way to get USB terminal access is using OTG mode, which we’ve seen used to simplify the configuration of a Pi as a simultaneous AP and client. If you want to set up headless access to Raspberry Pi desktop, we have a guide for that.
Raspberry Pis are popular because they're small, don't consume much power, and are extremely versatile. However, the default method of booting one—from the microSD card—is best avoided if possible. Here's how you can do it.
Raspberry Pi devices are popular among retro enthusiasts looking to emulate old computers and consoles, but this usually only goes as far as software. What you might not have considered is that the Raspberry Pi can also play a role in reviving old hardware.
From top left, clockwise: Axel AI CEO Bobby Figueroa; Eluum CEO Bilkay Rose, DrunR CEO Yaya Ali, and profileAPI CEO Wissam Tabbara.
New year, new Startup Radar.
We’re back with our regular spotlight on early stage startups sprouting up in the Seattle region. For this edition, we’re featuring Axel AI, DrunR, Eluum, and profileAPI.
Read on for brief descriptions of each company — along with pitch assessments from “Mean VC,” a GPT-powered critic offering a mix of encouragement and constructive criticism.
Check out past Startup Radar posts here, and email me at taylor@geekwire.com to flag other companies and startup news.
The business: A self-described “reasoning layer” for retail media sales teams that aims to translate messy data into commercial narratives and proposals. The idea is to help sales teams spend less time on manual analysis and preparation. The bootstrapped company officially launched its MVP at CES and NRF 2026 earlier this month.
Leadership: CEO and co-founder Bobby Figueroa previously founded Gradient, another Seattle-based commerce insights company that was acquired by Criteo. He was also an exec at Amazon. Axel’s leadership and advisory team includes former sales and advertising leaders at Amazon, Google, and Microsoft.
Mean VC: “You’re targeting a real friction point — sales teams juggling fragmented data with limited time to craft a compelling narrative. The pedigree helps, but long-term success will hinge on whether your product drives actual revenue lift, not just cleaner decks. I’d focus on embedding directly into the sales team’s existing workflow — don’t make users open another tool, make yours the one that quietly does the heavy lifting behind the scenes.”
The business: A nutrition app that provides personalized guidance based on users’ goals and preferences, particularly while dining out or ordering food online. DrunR is running a closed beta in Seattle with restaurants and users, including people using GLP-1 medication. The startup is part of the WTIA Founder Cohort 13 program.
Leadership: Founder and CEO Yaya Ali is a financial analyst at Perkins Coie and previously worked for King County and Amazon. He also has food operations experience. David Greene, the company’s CTO, is a software engineer at Capital One and previously worked at Moody’s.
Mean VC: “The intersection of nutrition, personalization, and GLP-1s is timely — especially as eating habits shift alongside new weight-loss drugs. The challenge will be making the app feel essential day-to-day, not just ‘nice to have’ after a restaurant meal or clinic visit. I’d zero in on a high-frequency use case — something that keeps users opening the app daily, not just when they’re thinking about dinner.”
The business: A new take on social media with a product that helps people organize their personal memories, stories, and digital artifacts into one user-controlled system. Built on community-driven moderation and works across different platforms. The bootstrapped company is onboarding early users and plans to launch a MVP later this year.
Leadership: CEO and co-founder Bilkay Rose was a VP at tax software company Avalara and a director at Clearwire. Other co-founders include CTO Dale Rector, who spent three decades at Microsoft, and Jennifer Gianola, also a former exec at Avalara.
Mean VC: “The concept taps into a real emotional need — people are overwhelmed by digital clutter and increasingly skeptical of algorithm-driven feeds. The key will be showing how your platform earns daily use without relying on dopamine loops. I’d push to define a sharp use case first — memory curation is broad, so lead with one thing people urgently want to preserve, then expand once you’ve earned their trust.”
The business: A business data layer for developers building AI-native chat, copilot, and agentic tools for go-to-market. Its platform tracks more than 10,000 signals across more than 10 million companies and 500 million professionals. The company, which was previously a sales AI agent product called Truebase, has raised $2 million in funding.
Leadership: Founder and CEO Wissam Tabbara has sold two startups and spent more than six years at Microsoft in the 2000s.
Mean VC: “The shift from product to platform is smart — selling infrastructure to power GTM copilots has stronger upside than building another agent. But you’ll need to show that your data isn’t just broad, but relevant and timely enough to drive meaningful in-app decisions. I’d focus on becoming the plug-and-play GTM brain — make integration dead simple, and let other tools build magic on top of your stack.”
PI is down 1.6% in the last 24 hours, reversing some of its Thursday gains.
The bearish performance comes despite Pi Network announcing a creator event and new updates to support easy Pi payment integration.
PI dips below $0.19 as bearish trend resumes
PI, the native coin of the Pi network, has lost 1.6% of its value in the last 24 hours and is now trading above $0.18.
The bearish performance comes despite Pi Network announcing plans on Wednesday to boost the ecosystem, including a creator event, integration of the PI payments system into apps built on the network, and extended access to app creation.
The team revealed that the PI payments support is limited to Test-Pi, and new or non-migrated Pioneers can now deploy app iterations by watching ads instead of paying fees.
Furthermore, Pi Network believes that the ad-supported application building on Pi App Studio could reduce the financial burden of creating Pi applications.
In addition to that, retail demand continues to increase despite PI’s price decline over the past few days. Data obtained from PiScan shows that the users have removed 1.17 million PI tokens from CEXs over the past 48 hours.
The removal from central exchanges will decrease selling pressure on PI as the tokens are transferred to long-term wallets.
PI remains bearish and could dip lower
The PI/USDT 4-hour chart is bearish and efficient as Pi has lost 1.6% of its value in the last 24 hours. PI failed to maintain its rally above the $0.1919 support-turned-resistance level, marked by the October 11 low.
At press time, PI is trading at $0.1839. If the selloff continues, PI could retest the October 10 and January 19 lows at $0.1533 and $0.1502, respectively.
Technical indicators on the 4-hour chart suggest that the bears remain in control. The Relative Strength Index (RSI) is 40, below the neutral 50, while the Moving Average Convergence Divergence (MACD) extends below the signal line.
However, if the bulls regain control and PI closes its daily candle above $0.1919, it could further extend the rally, potentially targeting the December 19 high at $0.2177.
Are you ready for a couple of challenging (and one more simple) Raspberry Pi projects? This weekend’s Raspberry Pi projects will put your hardware skills to the test with tasks like handling stepper motors and drivers, programming LEDs, and spinning up Docker containers.
Raspberry Pi is continuing its expansion into hardware accessories and components. In addition to SD cards and SSDs, you can now get an official Raspberry Pi Flash Drive. It's intended to be a boot drive for Pi boards, but you can use it like any other flash drive as well.
The ability to swap operating systems on a whim is one of the best things about single-board computers like the Raspberry Pi. You can write a new operating system to a memory card whenever you fancy, and keep a small collection of cards handy whenever you want to change.
Raspberry Pi boards have supported USB gadget mode for years, allowing you to connect a Pi to a computer with one cable for power and SSH/desktop access. Now, there's a much easier way to try it out, and it's built into Raspberry Pi OS.
Raspberry Pi single-board computers are awesome for so many reasons—they're low-power, extremely capable, and have small footprints. I love all those things about my Raspberry Pi, but my favorite feature is one that I don’t hear talked about nearly often enough.
PI, the native coin of the Pi Network, is up 1% in the last 24 hours and is now trading at $1.91 per coin. The positive performance comes despite the broader cryptocurrency market recording losses in the last few hours.
According to PiScan, the reserves of centralized exchanges have decreased by 4.24 million PI tokens, indicating large withdrawals over the last 24 hours. The decline in exchange reserves reflects strong buying pressure, allowing PI to recover above $0.19.
Will PI hit $0.20 soon?
The PI/USDT 4-hour chart is bearish and efficient despite the coin adding 1% to its value in the last 24 hours. At press time, PI is trading at $0.191, roughly 30% up from Monday’s low at $0.1502. The recovery aligns with the strong buying pressure and could push PI’s price higher in the near term.
The RSI of 33 means that PI is slowly escaping the oversold region as buyers step in. The MACD lines are still within the negative territory, indicating that the sellers have yet to fully relinquish control.
If the recovery continues and PI hits the $0.1919 resistance level, it could rally towards the $0.2060 psychological zone. An extended bullish run would allow PI hit the previous weekly high of $0.2116.
However, a daily candle close below $0.1919 could see PI give up some of its recent gains and retest the support levels at $0.1835 and $0.1632 in the near term.
In the previous article, we lifted the curtain on tracking technologies and showed how much information the internet collects from you. Many people still believe that privacy tools such as VPNs completely protect them, but as you are now learning, the story goes much deeper than that. Today we will explore what else is hiding behind the code. You will discover that even more information can be extracted from your device without your knowledge. And of course, we will also walk through ways to reduce these risks, because predictability creates patterns. Patterns can be tracked. And tracking means exposure.
Beyond Visuals
Most people assume fingerprinting is only about what you see on the screen. However, browser fingerprinting reaches far beyond the visual world. It also includes non visual methods that silently measure the way your device processes audio or stores small website assets. These methods do not rely on cookies or user logins. They do not require permission prompts. They simply observe tiny differences in system behavior and convert them into unique identifiers.
A major example is AudioContext fingerprinting. This technique creates and analyzes audio signals that you never actually hear. Instead, the browser processes the sound internally using the Web Audio API. Meanwhile favicon based tracking abuses the way browsers cache the small icons you see in your tab bar. Together, these methods help trackers identify users even if visual fingerprints are blocked or randomized. These non visual fingerprints work extremely well alongside visual ones such as Canvas and WebGL. One type of fingerprint reveals how your graphics hardware behaves. Another reveals how your audio pipeline behaves. A third records caching behavior. When all of this is combined, the tracking system becomes far more resilient. It becomes very difficult to hide, because turning off one fingerprinting technology still leaves several others running in the background.
Everything occurs invisibly behind the web page. Meanwhile your device is revealing small but deeply personal technical traits about itself.
AudioContext Fingerprinting
AudioContext fingerprinting is built on the Web Audio API. This is a feature that exists in modern browsers to support sound generation and manipulation. Developers normally use it to create music, sound effects, and audio visualizations. Trackers, however, discovered that it can also be used to uniquely identify devices.
Here is what happens behind the scenes. A website creates an AudioContext object. Inside this context, it often generates a simple sine wave using an OscillatorNode. The signal is then passed through a DynamicsCompressorNode. This compressor highlights tiny variations in how the audio is processed. Finally, the processed audio data is read, converted into numerical form, and hashed into an identifier.
The interesting part is where the uniqueness comes from. Audio hardware varies greatly. Different manufacturers like Realtek or Intel design chips differently. Audio drivers introduce their own behavior. Operating systems handle floating point math in slightly different ways. All of these variations influence the resulting signal, even when the exact same code is used. Two computers will nearly always produce slightly different waveform results.
Only specific privacy protections can interfere with this process. Some browsers randomize or block Web Audio output to prevent fingerprinting. Others standardize the audio result across users so that everyone looks the same. But if these protections are not in place, your system will keep producing the same recognizable audio fingerprint again and again.
You can actually test this yourself. There are demo websites that implement AudioContext fingerprinting.
Favicon Supercookie Tracking
Favicons are the small images you see in your browser tabs. They appear completely harmless. However, the way browsers cache them can be abused to create a tracking mechanism. The basic idea is simple. A server assigns a unique identifier to a user and encodes that identifier into a specific pattern of favicon requests. Because favicons are cached separately from normal website data, they can act as a form of persistent storage. When the user later returns, the server instructs the browser to request a large set of possible favicons. Icons that are already present in the cache do not trigger network requests, while missing icons do. By observing which requests occur and which do not, the server can reconstruct the original identifier.
This is clever because favicon caches have traditionally been treated differently from normal browser data. Clearing cookies or browsing history often does not remove favicon cache entries. In some older browser versions, favicon cache persistence even extended across incognito sessions.
There are limits. Trackers must maintain multiple unique icon routes, which requires server side management. Modern browsers have also taken steps to partition or isolate favicon caches per website, reducing the effectiveness of the method. Still, many legacy systems remain exposed, and clever implementations continue to find ways to abuse caching behavior.
Other Methods of Identification
Fingerprinting does not stop with visuals and audio. There are many additional identifiers that leak information about your device. Screen fingerprinting gathers details such as your screen resolution, usable workspace, color depth, pixel density, and zoom levels. These factors vary across laptops, desktops, tablets, and external monitors.
Font enumeration checks which fonts are installed on your system. This can be done by drawing hidden text elements and measuring their size. If the size changes, the font exists. The final list of available fonts can be surprisingly unique.
Speech synthesis fingerprinting queries the Web Speech API to discover which text to speech voices exist on your device. These are tied to language packs and operating system features.
The Battery Status API can reveal information about your battery capacity, charge state, and discharge behavior. This information itself is not very useful, but it helps illustrate how deep browser fingerprinting can go.
The website may also detect which Chrome plugins you use, making your anonymous identity even more traceable.
And this is still only part of the story. Browsers evolve quickly. New features create new opportunities for fingerprinting. So awareness is critical here.
Combined Threats and Defenses
When audio fingerprinting, favicon identifiers, Canvas, WebGL, and other methods are combined, they form what is often called a super fingerprint. This is a multi-layered identity constructed from many small technical signals. It becomes extremely difficult to change without replacing your entire hardware and software environment. This capability can be used for both legitimate analytics and harmful surveillance. Advertisers may track behavior across websites. Data brokers may build profiles over time. More dangerous actors may attempt to unmask users who believe they are anonymous.
Fortunately, there are tools that help reduce these risks. No defense is perfect. But layered protections can improve your privacy. For example, Tor standardizes many outputs, including audio behaviors and cache storage. But not everything, which means some things can expose you. Firefox includes settings such as privacy.resistFingerprinting that limit API details. Brave Browser randomizes or blocks fingerprinting attempts by default. Extensions such as CanvasBlocker and uBlock Origin also help reduce exposure, although they must be configured with care.
We encourage you to test your own exposure, experiment with privacy tools, and make conscious decisions about how and where you browse.
Conclusion
The key takeaway is not paranoia. Privacy tools do not eliminate fingerprinting, but defenses such as Tor, Brave, Firefox fingerprint-resistance, and well-configured extensions do reduce exposure. Understanding how non-visual fingerprints work allows you to make informed decisions instead of relying on assumptions. In modern browsing, privacy is not about hiding perfectly. It is about minimizing consistency and breaking long-term patterns.
Awareness matters. When you understand how you are being tracked, you’re far better equipped to protect your privacy.
Home Assistant is a Linux-based smart home operating system with a very low barrier to entry. In addition to being completely free, its minimum system specifications mean it works on most computers produced within the last 10 to 15 years (plus humble single-board computers).
Not all Raspberry Pi projects require a display, like home servers and DNS sinkholes. But not all projects are alike, and sometimes your choice of display can greatly influence the end result.
Recently, [Vinnie] aka [vinthewrench] moved from Oregon to Arkansas to start a farmstead. This is a style of farming that focuses not just on a profitable farm where produce is sold at market, but also on a homestead where much of one’s own food is grown on the farm as well. Like any farm, though, it’s extremely hard work that takes a tremendous amount of time. Automation and other technology can make a huge impact in these situations, and [Vinnie] is rolling out his own software stack to help with this on his farm.
He calls his project the Pi Internet of Things, or PioT, and as its name suggests is based around the Raspberry Pi. Since this will all be outdoors and exposed to the extremes of Arkansas weather, everything built under the auspices of this project prioritizes ruggedness, stability, and long-term support, all while avoiding any cloud service. The system also focuses on being able to ride through power outages. The server side, called piotserver, uses a REST API to give the user access to the automation systems through a web interface
[Vinnie] also goes into detail about why existing systems like Home Assistant and Open Sprinkler wouldn’t work in his situation, and why a ground-up solution like this is more appropriate for his farm. This post is largely an overview of his system, but some of his other posts go into more detail about things like integrating temperature sensors, rainfall monitoring, controlling irrigation systems, and plenty of other farm automation tasks that are useful for any farmer or gardener.
Nexo, a crypto lending platform, agreed to pay a $500,000 penalty after California regulators said it made thousands of loans without the proper state license.
According to the California Department of Financial Protection and Innovation, the actions involved loans backed by crypto assets and raised concerns about how the company evaluated borrowers.
California Action On Unlicensed Loans
The DFPI found that Nexo issued at least 5,456 consumer and commercial loans from July 2018 through November 2022 to residents in California.
Reports have disclosed that the company did not adequately check whether borrowers could repay the loans, leaving consumers exposed to risky lending. The agency called those practices unlawful under state consumer finance rules.
Nexo Must Move California Funds To Licensed Affiliate
As part of the remedy, Nexo will be required to transfer funds held for Californians to its US-based affiliate that holds a valid license, Nexo Financial LLC, within 150 days.
The move is meant to ensure customers’ money is under a properly regulated entity. The DFPI also required other compliance steps to prevent similar problems in the future.
A Pattern Of Regulatory Scrutiny
This is not the first time Nexo has faced enforcement. Based on reports, the firm previously reached settlements that included roughly $45 million in penalties during actions taken in 2023.
Regulators around the country have been paying closer attention to crypto lending, and this decision signals they expect the same consumer protections that apply to traditional lenders to apply to platforms using digital assets.
Consumers who took loans secured with crypto may now see their accounts handled differently while the transfer takes place. Some borrowers might face changes in terms or servicing.
Industry observers say this kind of oversight can push companies to tighten underwriting and documentation. At the same time, some users worry that more rules could limit their access to certain crypto services.
Regulators Emphasize Borrower Protections
According to the DFPI, California law requires lenders to assess a borrower’s capacity to repay loans and to hold the right licenses before they are allowed to do business with state residents.
By labeling the conduct unlawful, the agency signaled that loan decisions driven primarily by crypto collateral do not exempt a lender from basic checks on repayment capacity. The penalty and the corrective measures aim to close gaps that might have allowed risky loans to go through.
A Cautious Road Ahead
The $500,000 fine is modest compared with the scale of the broader crypto market, yet regulators say penalties are only one tool. They added that transfers to licensed entities and stronger internal controls are key to protecting consumers.
Featured image from unsplash, chart from TradingView
Crypto analyst TARA has predicted that the Bitcoin price will still rally despite bearish signals that have surfaced. She highlighted why the flagship crypto could reach this level and what could happen once it touches the price target.
Analyst Predicts Bitcoin Price Surge To $99,000
In an X post, TARA opined that the Bitcoin price will reach $99,300, even though the flagship crypto is printing a bearish candlestick. She stated that BTC wants to touch this price target before it retraces deeper so that the correction does not break the critical support at $90,000. The analyst added that retracement levels for BTC will continue to be adjusted, with the new 2026 high above $97,000, while revealing subwaves on the way to the full target at $103,000.
Notably, crypto traders are currently betting on the Bitcoin price rallying past the $99,000 level and reaching the psychological $100,000 level. Polymarket data shows a 48% chance that BTC will rally to $100,000 this month. This follows the flagship crypto’s recent rally from around $92,000 to above $97,000 following the release of the soft CPI inflation data earlier this week.
The spot Bitcoin ETFs have also contributed to the Bitcoin price surge to start the year. In an X post, Bloomberg analyst Eric Balchunas highlighted that ETFs recorded net inflows of $843 million on January 14 and now boast 1-week net inflows of $1 billion and $1.5 billion year-to-date (YTD). With BTC rallying to $97,000 after trading sideways towards the end of last year, Balchunas opined that the buyers may have exhausted the sellers.
Arthur Hayes Predicts Bitcoin Rally On Rising Liquidity
In his latest blog post, BitMEX co-founder Arthur Hayes predicted that the Bitcoin price could sustain this rally as dollar liquidity rapidly increases. Hayes expects dollar liquidity to increase as U.S. President Donald Trump finds more ways to inject liquidity into the economy. The BitMEX co-founder highlighted how Trump plans to lower mortgage rates, which could cause Americans to borrow more.
Hayes also mentioned that the liquidity in 2025 didn’t support crypto portfolios, which is why the Bitcoin price underperformed. He urged market participants not to draw wrong conclusions from the 2025 underperformance, as it was always a liquidity story rather than a cyclical bear market, as some analysts suggested.
More liquidity could also flow into the market as Trump nominates a rate-cut advocate to replace Fed Chair Jerome Powell. This could lead to larger rate cuts, which would be bullish for the Bitcoin price and the broader crypto market.
At the time of writing, the Bitcoin price is trading at around $95,300, down in the last 24 hours, according to data from CoinMarketCap.
The 7 Best Real-Time Stock Data APIs for Investors and Developers in 2026 (In-Depth Analysis & Comparison)
Many believe that to access high-quality financial data, you need to pay thousands of dollars for a Bloomberg terminal or settle for limited platforms like Yahoo Finance. The truth is different: today, there are powerful, affordable, and even free real-time stock data APIs you can integrate into your Python scripts, interactive dashboards, or algorithmic trading systems.
As W. Edwards Deming said:
“Without data, you’re just another person with an opinion.”
In this article, I present a practical comparison of the 7 best financial APIs on the market (with a focus on real-time stock data). I include:
Pros and cons of each API
Pricing plans (free tiers and paid options)
Key features and data coverage
Recommendations by profile (analyst, trader, developer, or enterprise)
Concrete use cases demonstrating each API
Comparison table (quick selection guide)
Frequently asked questions to address common doubts
Let’s dive in.
1. EODHD API (End-of-Day Historical Data)
Best for: Fundamental analysis, backtesting, and financial reports Website: eodhd.com
Key features:
Historical end-of-day (EOD) prices and intraday data (1m, 5m, 1h intervals)
Fundamental data (financial ratios, balance sheets, income and cash flow statements)
Corporate actions: dividends, stock splits, earnings, IPO data
Macroeconomic indicators and earnings calendars
Financial news API (with sentiment analysis)
Broad coverage: stocks, ETFs, indices, forex, and cryptocurrencies
Highlights:EODHD provides clear documentation with plenty of Python examples, and it combines both quantitative price data and fundamental data in one service. This makes it great for building dashboards or predictive models that require both historical prices and financial metrics. Its data consistency (handling of splits, ticker changes, etc.) is also highly regarded.
Pricing:
Free: 20 API requests per day (limited to basic end-of-day data) — useful for testing or small-scale scripts
Pro: Plans from ~$17.99 per month (for individual market packages) up to ~$79.99 per month for an all-in-one global data package. The paid tiers offer generous limits (e.g. 100,000 API calls/day) and full access to historical and real-time data.
Cons:
The free plan’s 20 calls/day is very limited, suitable only for trial runs or simple prototypes. Serious projects will need a paid plan.
Some advanced features (like extensive options data or certain international markets) may require higher-tier subscriptions.
Use case: Extract Apple’s dividend history over the past 5 years and calculate the dividend yield trend. (EODHD’s API can provide historical dividend payouts which you can combine with price data for this calculation.)
Personal recommendation: If you need a single comprehensive API for global stocks (prices + fundamentals + news), EODHD is an excellent choice. ✨ Get 10% off here to try it out.
Time series data for equities (daily, intraday down to 1-minute)
Technical indicators built-in (e.g. RSI, MACD, Bollinger Bands) — you can query indicator values directly via the API.
Crypto and Forex data support
Some sentiment and macroeconomic data (e.g. sector performance, economic indicators)
Highlights: Alpha Vantage is known for its ease of use and generous free tier for beginners. It’s one of the most popular starting points for developers learning to work with financial data. Uniquely, Alpha Vantage is an official vendor of Nasdaq market data, which speaks to its data reliability. The API responses are JSON by default, and the documentation includes examples that integrate well with Python and pandas.
Pricing:
Free: Up to 5 API calls per minute (approximately 500 calls per day). This is sufficient for small applications or learning purposes, though heavy use will hit the limits quickly. (Note: Alpha Vantage’s standard free limit is actually 25 calls per day as of late 2024, enforced alongside the 5/minute rate.)
Premium: Paid plans starting from $29.99/month for higher throughput (e.g. 30+ calls/minute) and no daily cap. Higher tiers (ranging up to ~$199/month) allow dozens or hundreds of calls per minute for enterprise needs.
Cons:
Strict rate limits on the free tier. Hitting 5 calls/min means you often have to throttle your scripts or batch requests. For example, pulling intraday data for many symbols or calling many technical indicators will quickly require a paid plan.
Limited depth in some areas: fundamental data coverage is basic (company overviews, a few ratios) and not as extensive globally as some competitors.
Use case: Build an email alert system that triggers when a stock’s 14-day RSI drops below 30 (an oversold signal). Alpha Vantage’s technical indicators API can directly return the RSI for a given symbol, making this straightforward to implement without calculating RSI manually.
3. Intrinio
Best for: Enterprise projects, advanced fundamental data, and large-scale financial applications Website: intrinio.com
Key features:
Extensive financial statement data: Intrinio provides detailed fundamentals — standardized and as-reported financials (income statements, balance sheets, cash flows) for thousands of companies. It’s very useful for deep fundamental analysis and modeling.
Real-time and historical stock prices: Access to real-time equity quotes (for supported exchanges) and long historical price data (often decades back). Intrinio also offers options data, ETFs, Forex, and other asset classes through various packages.
Data marketplace model: Intrinio has a variety of data feeds and endpoints (e.g., US stock prices, global equities, options, ESG data, etc.). You subscribe only to the feeds you need, which can be cost-efficient for specific use cases.
Developer tools: Clean REST API with robust documentation, SDKs in multiple languages, and even a real-time data streaming option for certain feeds. They also provide a sandbox environment and live chat support to help during development.
Highlights: Intrinio is known for high data accuracy and quality. It’s the go-to for many fintech startups and even institutions when building platforms that require reliable and up-to-date financial data. The breadth of APIs and endpoints is massive — from stock screeners to data on insider transactions. Intrinio’s website and product pages are very informative, and they even include an AI chatbot to help you find the data you need.
Pricing:
Free trial: Intrinio offers a free trial period for new users to test out the API with limited access. This is great for evaluating their data before committing.
Paid packages: Pricing is segmented by data type. For example, a US equities core package starts around $200/month (Bronze tier) for end-of-day prices and fundamentals. Real-time stock price feeds and expanded data (Silver/Gold tiers) cost more — e.g., U.S. equities Gold (with real-time quotes and full history) is about $800/month. Similarly, options data packages range from ~$150 up to $1600/month for real-time options feeds. Intrinio’s model is pay for what you need, which scales up to enterprise-level contracts for wide coverage.
Cons:
Not ideal for small projects or beginners: Intrinio’s offerings can be overkill for hobbyist use — the range of data is immense and the pricing is relatively high. There is no unlimited free tier, so after the trial you must budget for at least a few hundred dollars per month to continue using their data at any scale.
Complex pricing structure: Because of the package system (separate feeds for stocks, options, etc.), it may be confusing to figure out exactly which plan(s) you need, and costs can add up if you require multiple data types. It’s geared more toward startups, fintech companies, or professionals with a clear data strategy (as opposed to one-size-fits-all simple pricing).
Website account required: You’ll need to go through account setup and possibly consultation for certain datasets. It’s not as plug-and-play as some other services for quick experiments.
Use case: An investor relations platform could use Intrinio to automate financial report analysis — pulling in several years of standardized financials for dozens of companies to compare ratios and performance. Intrinio’s high-quality fundamentals and wide historical coverage make it ideal for such an application.
4. Polygon.io
Best for: Real-time market data (especially U.S. stocks) and high-frequency trading apps Website:https://massive.com/
Key features:
Real-time price feeds: Polygon provides live tick-by-tick price data for U.S. stocks, options, forex, and crypto. It supports streaming via WebSockets, so you can get quotes and trades in real time with low latency.
Historical data down to ticks: You can access granular historical data, including full tick data and minute-by-minute bars for equities (often used for backtesting trading algorithms).
WebSockets & Streaming: Excellent WebSocket API for streaming live quotes, trades, and aggregates. This is crucial for building live dashboards or trading bots that react to market movements instantly.
Reference data & tools: Polygon also offers comprehensive reference data (company info, financials, splits/dividends, etc.) and endpoints like news, analyst ratings, and more. However, its core strength is market price data.
Highlights: Polygon.io stands out for performance and depth in the U.S. markets. If you need real-time stock prices or even need to stream every trade for a given stock, Polygon can handle it. Their documentation is well-structured and they have a developer-friendly interface with interactive docs. They also offer community resources and example code which make integration easier. Polygon’s pricing page clearly separates plans for different asset types, so you can pick what you need.
Pricing
Free: The free tier allows 5 API requests per minute and limited historical data (e.g., 2 years of daily data). Real-time streaming might be restricted or delayed on the free plan (often 15-minute delayed data for stocks). This tier is good for trying out the API or basic apps that don’t require extensive data.
Paid: Plans start at $29/month for higher call limits and more data access. For instance, Polygon’s “Starter” or “Developer” plans (around $29-$79/month) provide live data with certain limitations (like delayed vs real-time) and a cap on how far back you can fetch history. More advanced plans can go up to a few hundred per month for full real-time tick data and larger rate limits. (Polygon has recently rebranded some offerings under “Massive” but the pricing remains in this range for individual developers.)
Cons:
Primarily U.S.-focused: Polygon’s strength is U.S. stocks and options. If you need comprehensive data for international markets, you’ll need other APIs. Its coverage outside the U.S. (for equities) is limited, so it’s not a one-stop solution for global portfolios.
Costly for full real-time access: While entry plans are affordable, truly real-time professional data (especially if you need full tick data or entire market streaming) can become expensive. Higher-tier plans for real-time data (with no delay and high rate limits) can run into the hundreds per month, and certain data (like entire market breadth or entire options chains in real time) might require enterprise arrangements.
Limited fundamentals/news: Polygon has some fundamental data and news, but it does not offer the depth in these areas that more fundamentally-oriented APIs (like EODHD or FMP) do. It focuses on pricing data.
Use case: Stream live quotes for AAPL and MSFT using Polygon’s WebSocket API and display a live updating chart in a web app. With just a few lines of code, you can subscribe to the ticker feed and get real-time price updates that drive an interactive chart (great for a day-trading dashboard or a demo of live market data).
5. Alpaca Markets
Best for: Building trading bots and executing live trades (with data included) Website: alpaca.markets
Key features:
Commission-free stock trading API: Alpaca is actually a brokerage platform that provides APIs, so you can place real buy/sell orders for U.S. stocks with zero commissions via their API. This sets it apart from pure data providers.
Real-time and historical market data: Alpaca offers real-time price data (for stocks on the US exchanges) and historical data as part of its service. When you have a brokerage account, you get access to stock quotes and minute-level bars, etc., through the API.
Paper trading environment: For developers, Alpaca’s paper trading is a big plus — you can simulate trading with virtual money. You get the same API for paper and live trading, which is ideal for testing your algorithmic strategies safely.
Brokerage integration: You can manage orders, positions, and account info via API. This means you not only get data but can also automate an entire trading strategy (from data analysis to order execution) with Alpaca’s platform.
Highlights: Alpaca is a favorite for DIY algorithmic traders and hackathon projects because it lowers the barrier to entry for trading automation. With a few API calls, you can retrieve market data and send orders. It’s essentially an all-in-one trading service. The documentation is developer-centric, and there are official SDKs (Python, JS, etc.) as well as a vibrant community. Alpaca integrates with other tools (like TradingView, Zapier) and supports OAuth, making it easier to incorporate in different applications.
Pricing:
Free tier: You can use Alpaca’s core API for free. Creating an account (which requires U.S. residency or certain other country residencies for live trading) gives you access to real-time stock data and the ability to trade with no monthly fee. Alpaca makes money if you trade (through other means like payment for order flow), so the API and basic data are provided at no cost to developers.
Premium data plans: Alpaca does have optional subscriptions for more advanced data feeds. For example, the free data might be SIP consolidated feed with a small delay or only IEX data; if you need full real-time consolidated market data or extended history, they offer Data API subscriptions (like $9/month for more history, or higher for things like real-time news, etc.). These are add-ons; however, many users find the free data sufficient for starting out.
Cons:
Limited to U.S. stock market: Alpaca’s trading and data are focused on U.S. equities. You won’t get direct access to international stocks or other asset classes (except crypto, which Alpaca has added in a separate offering).
Requires KYC for live trading: If you plan to execute real trades, you must open a brokerage account with Alpaca, which involves identity verification and is only available in certain countries. Paper trading (demo mode) is available globally, but live trading has restrictions.
Data not as extensive as dedicated providers: While Alpaca’s included data is decent, it may not be as comprehensive (in terms of history or variety of technical indicators) as some standalone data APIs. It’s primarily meant to support trading rather than be a full analytics dataset.
Use case: Create a Python trading bot that implements a simple moving average crossover strategy (e.g., buy when the 50-day MA crosses above the 200-day MA, sell on the reverse crossover). The bot can use Alpaca’s data API to fetch the latest prices for your stock, compute moving averages, and Alpaca’s trading API to place orders when signals occur. You can even run this in paper trading first to fine-tune the strategy.
6. Finnhub
Best for: A mix of data types (real-time prices, fundamentals, news, crypto) in one service Website: finnhub.io
Key features:
Real-time market data: Finnhub provides real-time quotes for stocks (free for US stocks via IEX), forex, and cryptocurrencies through its API. It’s a solid choice if you need live pricing across multiple asset classes.
Financial news with sentiment: There’s a news API that returns the latest news articles for companies or markets, including sentiment analysis scores. This is useful for gauging market sentiment or doing news-driven strategies.
Corporate and economic calendar data: Endpoints for earnings calendars, IPO schedules, analyst earnings estimates, and economic indicators are available. This variety helps investors and analysts stay on top of upcoming events.
Fundamental data: Finnhub offers some fundamentals (e.g., company profiles, financial statements, key metrics), as well as alternative data like COVID-19 stats, and even ESG scores. However, some of these are limited in the free tier.
Highlights: Finnhub is like a Swiss Army knife — it covers a broad range of financial data in one API. Many startups use Finnhub to power their apps because it’s relatively easy to use and the free tier is generous in terms of number of calls. Developers also appreciate that Finnhub’s documentation is straightforward and they have examples for how to use each endpoint. It’s particularly notable for its news and social sentiment features, which not all finance APIs offer.
Pricing:
Free: 60 API requests per minute are allowed on the free plan, which is quite high compared to most free plans. This includes real-time stock prices (US markets) and basic access to many endpoints. The free tier is for personal or non-commercial use and has some data limits (like certain endpoints or depth of history may be restricted).
Pro: Paid plans start from $49–50 per month for individual markets or data bundles. Finnhub’s pricing can be a bit modular; for example, real-time international stock feeds or more historical data might each be priced separately (often ~$50/month per market). They also have higher plans (hundreds per month) for enterprise or for accessing all data with fewer limits. For many users, the $50/month range unlocks a lot of additional data useful for scaling up an application.
Cons:
Limited free fundamentals: The free plan, while generous with call volume, does not include all data. For instance, certain fundamental data endpoints (like full financial statements or international market data) require a paid plan. This can be frustrating if you expect all features to work out of the box with the free API key. Essentially, you might hit “Access denied” for some endpoints until you upgrade.
Pricing can add up: If you need multiple data types (say US stocks real-time, plus international stocks, plus in-depth fundamentals, etc.), Finnhub’s costs can increase quickly because each component may be an add-on. In comparison, some competitors’ bundled plans might be more cost-effective for broad needs.
Website/UI is basic: Finnhub’s website isn’t the slickest and occasionally the docs have minor inconsistencies. This isn’t a huge issue, but it’s not as polished as some others like Alpha Vantage or Twelve Data in terms of user interface.
Use case: Pull the latest news headlines and sentiment for Tesla (TSLA) and display a “sentiment gauge”. With Finnhub’s news API, you can get recent news articles about Tesla along with a sentiment score (positive/negative). A developer could feed this into a simple app or dashboard to visualize how news sentiment is trending for the company.
7. Twelve Data
Best for: Quick visualizations, simple dashboards, and spreadsheet integrations Website: twelvedata.com
Key features:
Historical & real-time data for stocks, forex, crypto: Twelve Data covers many global markets, offering time series data at various intervals (intraday to daily) for equities, FX, and cryptocurrencies.
Built-in visualization tools: Uniquely, Twelve Data provides a web UI where you can quickly generate charts and indicators from their data without writing code. It’s useful for non-developers or for quickly checking data visually.
Easy integration with Python, Excel, etc.: They have a straightforward REST API and also provide connectors (like an Excel/Google Sheets add-in and integration guides for Python, Node, and other languages). This makes it appealing to analysts who might want data in Excel as well as developers.
Technical indicators and studies: Twelve Data’s API can return technical indicators similar to Alpha Vantage. They also support complex queries like retrieving multiple symbols in one call, and even some fundamentals for certain stocks.
Highlights: Twelve Data markets itself as very user-friendly. For someone who is building a simple web app or learning to analyze stock data, Twelve Data’s combination of an intuitive API plus a pretty interface for quick tests is attractive. Another highlight is their freemium model with credits — this can be flexible if your usage is light. They also have educational content and a responsive support team. Many users praise the quality of documentation, which includes example requests and responses for every endpoint (so you can see what data you’ll get).
Pricing:
Free (Basic): 8 API requests per minute (up to ~800/day). This free plan gives real-time data for US stocks, forex, and crypto, which is quite useful for small projects. However, certain features (like WebSocket streaming or extended history) are limited on the free tier.
Paid plans:Grow plan from $29/month, Pro plan from $79/month, and higher tiers up to Enterprise. The pricing is based on a credit system: each API call “costs” a certain number of credits (e.g., 1 credit per quote, more credits for heavier endpoints). Higher plans give you more credits per minute and access to more markets. For example, the Pro plan (~$79) significantly raises rate limits (e.g. 50+ calls/min) and adds a lot more historical data and international market coverage. Enterprise ($1,999/mo) is for organizations needing very high limits and all data. The credit system is a bit complex to grasp at first, but effectively the more you pay, the more data and speed you get.
Cons:
Free plan limitations: The Basic plan is fine for testing, but serious usage will bump into its limits (both in call volume and data depth). Also, some endpoints require higher plans, and real-time WebSocket access is mostly for paid users. In short, Basic is more of a trial.
Credit-based pricing confusion: As noted, the concept of “API credits” and each endpoint having a weight can be confusing. For instance, an API call that fetches 100 data points might consume more credits than one that fetches 1 data point. New users may find it hard to estimate how many credits they need, compared to providers with simple call counts.
Fewer specialty datasets: Twelve Data covers the essentials well, but it doesn’t have things like in-depth fundamentals or alternative data. Its focus is on price data and basic indicators. Large-scale applications needing extensive financial statement data or niche data (like options, sentiment) would need an additional source.
Use case: Build a lightweight crypto price dashboard that updates every 5 minutes. Using Twelve Data’s API, you could fetch the latest price for a set of cryptocurrencies (e.g., BTC, ETH) at a 5-min interval and display them in a Streamlit or Dash app. Twelve Data’s ease of integration means you could have this running quickly, and if you use their built-in visualization components, you might not need to code the charting yourself.
Quick Selection Guide by User Profile:
If you’re an investor/analyst needing both fundamentals and price history:EODHD or FMP are excellent due to their rich fundamental datasets and broad market coverage
If you’re a trader focused on real-time data and execution:Polygon.io (for raw real-time feeds) or Alpaca (for trading with built-in data) are tailored to your needs. Polygon for pure data speed; Alpaca if you also want to place trades via API.
If you’re a developer or student learning the ropes,Alpha Vantage or Yahoo Finance via yfinance are very beginner-friendly. They have free access, simple endpoints, and plenty of examples to get you started in Python or JavaScript.
If you need global market coverage in one service:EODHD, Finnhub, or FMP will give you international stocks, forex, crypto, and more under a single API — useful for broad applications or multi-asset platforms.
If you prefer no-code or Excel integration:EODHD, FMP, and Twelve Data offer Excel/Google Sheets add-ons and straightforward no-code solutions, so you can fetch market data into spreadsheets or BI tools without programming.
Bonus: Financial Modeling Prep (FMP)
Best for: Advanced fundamental analysis and automated financial statement retrieval Website:financialmodelingprep.com
Key features:
Extensive financial statements coverage: FMP provides APIs for detailed financial statements (balance sheets, income statements, cash flows) for many public companies, including quarterly and annual data. They also offer calculated financial ratios and metrics, making it a favorite for equity analysts.
Real-time and historical stock prices: You can get real-time quotes as well as historical daily and intraday price data for stocks. FMP covers stocks worldwide, plus ETFs, mutual funds, and cryptocurrencies.
Specialty endpoints: There are unique APIs for things like DCF (Discounted Cash Flow) valuation, historical dividend and stock split data, insider trading information, and even ESG scores. This breadth is great for those building sophisticated models.
News and alternative data: FMP includes a financial news feed, earnings calendar, and economic indicators. While not as deep on news sentiment as Finnhub, it’s a well-rounded data source for market context.
Highlights: FMP has gained a lot of traction as a developer-friendly alternative to more expensive data platforms. Its documentation is clear, with examples in multiple languages. One big plus is the Excel/Google Sheets integration — even non-coders can use FMP by installing their Google Sheets add-on and pulling data directly into a spreadsheet. The combination of fundamentals + market data in one API, along with affordable pricing, makes FMP very appealing for startups and students. In my personal experience, FMP’s fundamental data depth is excellent for building valuation models or screening stocks based on financial criteria.
Pricing:
Free tier: FMP offers a free plan with a limited number of daily requests (e.g., 250 per day). The free tier gives access to basic endpoints — you can get some real-time quotes, key financial metrics, and historical data for a few symbols to test it out.
Pro plans: Paid plans start at around $19.99/month, which is quite affordable. These plans increase the daily request limit substantially (into the thousands per day) and unlock more endpoints. Higher tiers (on the order of $50-$100/month) offer even larger call volumes and priority support. For most individual developers or small businesses, FMP’s paid plans provide a lot of data bang for the buck. Enterprise plans are also available if needed, but many will find the mid-tier plans sufficient.
Cons:
Free plan restrictions: The free plan is mainly for trial or very light use — serious users will quickly find it inadequate (in terms of both request limits and available data). If you have an app in production, you’ll almost certainly need a paid plan, though fortunately the entry cost is low.
Data normalization quirks: Because FMP aggregates data from various sources, you might notice slight inconsistencies or formatting differences across certain endpoints. For example, some lesser-used financial metrics might have different naming conventions or units. These are minor issues and FMP continually improves them, but it’s something to be aware of if you encounter an odd-looking field.
Not focused on real-time streaming: FMP provides real-time quotes on paid plans, but it’s not a streaming service. If you need tick-by-tick streaming or ultra-low-latency data, a specialized API like Polygon or a broker feed would be necessary. FMP is more geared towards snapshots of data (which is fine for most analysis and moderate-frequency querying).
Why we include FMP: Lately, many developers (myself included) have been testing FMP for projects because of its rich fundamental dataset and solid documentation. It’s a strong alternative if you want advanced company metrics or need to automate financial statement analysis directly into your Python scripts or dashboards. For example, you could pull 10 years of financials for dozens of companies in seconds via FMP — something that’s invaluable for quantitative investing or academic research. FMP combines flexibility, affordability, and depth of data that few APIs offer in one package.
Frequently Asked Questions (FAQs)
❓ What’s the most complete API that combines fundamentals, historical prices, and news? ✅ If you need everything in one service, EODHD, FMP, and Alpha Vantage stand out. They each offer a balance of broad market coverage, reliable data, and depth. EODHD and FMP in particular have extensive fundamental and historical datasets (with news feeds) alongside real-time data, making them all-in-one solutions.
❓ Is there a free API with real-time stock data? ✅ Polygon.io provides limited real-time access on their free plan — you can get real-time quotes for U.S. stocks (with some delays or limits). Additionally, Finnhub’s free tier offers real-time data for U.S. markets (60 calls/min) which is quite generous. If you’re open to paid plans, FMP offers real-time quotes in its affordable paid tiers as well. And for an unofficial free route, Yahoo Finance data via the yfinance library can give near-real-time quotes (with no API key needed), though it’s not guaranteed or supported.
❓ I’m new to programming and want to learn using stock data. Which API is best? ✅ Alpha Vantage or Yahoo Finance (yfinance) are excellent for beginners. Alpha Vantage’s free tier and straightforward endpoints (plus a ton of community examples) make it easy to get started. The yfinance Python library lets you pull data from Yahoo Finance without dealing with complex API details – perfect for quick prototypes or learning pandas data analysis. Both integrate seamlessly with Python for learning purposes.
❓ Which API has the best global market coverage? ✅ EODHD, Finnhub, and FMP are known for their international coverage. EODHD covers dozens of exchanges worldwide (US, Europe, Asia, etc.) for both stock prices and fundamentals. Finnhub includes international stock data and forex/crypto. FMP also has a global equity coverage and even macro data for various countries. If you need data beyond just U.S. markets, these providers will serve you well.
❓ Can I use these APIs in Excel or Google Sheets without coding? ✅ Yes, several of them offer no-code solutions. EODHD, FMP, and Twelve Data all provide add-ins or integrations for Excel/Sheets. For example, EODHD and FMP have official Google Sheets functions after you install their add-on, letting you fetch stock prices or financial metrics into a spreadsheet cell. Twelve Data has an Excel plugin as well. This is ideal for analysts who prefer working in spreadsheets but still want live data updates.
Final Thoughts and Action Plan
You don’t need to be a big firm to access professional-grade financial data. Today’s landscape of financial APIs makes it possible for anyone — from a solo developer to a small startup — to get quality real-time stock data and more.
Follow these steps to get started:
Choose the API that best fits your profile and project needs. (Review the comparisons above to decide which one aligns with your requirements and budget.)
Sign up and get your free API key. Every platform listed offers a free tier or trial — take advantage of that to test the waters.
Connect the data to your tool of choice: whether it’s a Python script, an Excel sheet, or a custom dashboard, use the API documentation and examples to integrate live data into your workflow. Start with small experiments — e.g., pull one stock’s data and plot it.
By iterating on those steps, you’ll quickly gain familiarity with these APIs and unlock new possibilities, from automated trading bots to insightful financial dashboards.
Looking for a single API that does it all (fundamentals, historical prices, and news)? My recommendation is EODHD for its all-around strength in data coverage and value. It’s a one-stop shop for investors and developers alike.
Pro tip: You can try EODHD with a 10% discountusing the link above, to kickstart your project with some savings. Happy data hunting, and may your analyses be ever insightful!
Sources: The information above is gathered from official documentation and user reviews of each platform, including their pricing pages and features as of 2025. For example, Alpha Vantage’s free call limits, Intrinio’s pricing tiers, and Twelve Data’s rate limits are based on published data. Always double-check the latest details on each provider’s website, as features and pricing can evolve over time.
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