Tech
Open source projects draw equity-free funding from corporates, startups, and even VCs
A dearth of funding for vital open source technologies is leading to a swath of support from startups, unicorns, corporations, and even venture capital firms.
Last year, Bloomberg launched its FOSS (free and open source software) fund, committing up to $90,000 per year to various projects. And in October, Indian financial services company Zerodha launched a similar initiative dubbed FLOSS/fund, pledging $1 million annually to open source projects. The reason? “A significant portion of our success and growth is owed to FOSS,” Zerodha CTO Kailash Nadh said at the time.
“It goes without saying that this holds true for nearly every technology company founded in the last decade, whether it is publicly acknowledged or not,” Nadh added.
While there is no shortage of companies building businesses and raising money off the back of open source software, not every community-driven project lends itself to becoming a commercial entity. Some open source tools are more akin to Lego blocks: key components of a software stack, for sure, but difficult to monetize directly — particularly in the early days.
And this is why we’ve seen a steady rise in funding initiatives come to the fore. This includes reactive programs, such as 2022’s Big Tech-driven $30 million pledge to bolster open source security in the wake of the Log4Shell security flaw that wreaked havoc on the software supply chain. But we’re also seeing more proactive efforts, driven from all corners of industry.
Silicon Valley VC Sequoia Capital launched an open source fellowship in 2023 to support project maintainers with equity-free capital to cover living expenses for up to 12 months. Its inaugural fellow was Colombian software developer Sebastián Ramírez Montaño, creator of FastAPI, an open source web framework for building APIs.
In February, Sequoia revealed it would start accepting applications from any developer leading an open source project, with plans to provide funding for up to three qualifying projects annually. Nine months on, and the first two fellows from Sequoia’s expanded program have now been revealed: Chatbot Arena, a popular open source AI model benchmarking tool used by many of the industry’s biggest names, including OpenAI, Meta, and Google; and vLLM, an open source library focused on memory management to power faster and cheaper LLM serving.
Jolly good fellows
Chatbot Arena, which spun out of a broader research organization called LMSYS, is the handiwork of doctorate students Wei-Lin Chiang and Anastasios Angelopoulos from Berkeley’s Sky Computing Lab. With north of 1 million monthly users, Chatbot Arena is all about helping LLM developers validate claims around their models’ performance, while anyone can test these models and vote for their preferences. Companies such as OpenAI often share versions of their models with the Chatbot Arena team ahead of the models’ release to help fine-tune things before their formal launch.
While Chatbot Arena receives financing as part of the creators’ doctorate research work at the Sky Computing Lab, the Sequoia fellowship award of $100,000 will help fund further technical development, including building a better interface.
“The Sequoia grant supports the development of Chatbot Arena’s website, covering full-stack development and server maintenance costs,” Chiang told TechCrunch. “This is a gift to support the open source project, with no future obligations.”
Sequoia isn’t the only VC firm to lend equity-free support to Chatbot Arena; Andreessen Horowitz launched an open source AI grant program last August, and Chatbot Arena’s umbrella outfit LMSYS was among the second cohort of recipients.
Chiang said that there are no plans to evolve the project into a commercial entity, underscoring the need for alternative sources of financing — now, and perhaps long into the future.
“As part of our long-term vision, we may establish a nonprofit organization to host the leaderboard, keeping our focus on broad accessibility and community impact,” Chiang said.
In tandem, Berkeley’s Sky Computing Lab also birthed vLLM in 2022, spearheaded by researchers Zhuohan Li, Woosuk Kwon, and Simon Mo, who started the project after developing a system to distribute complex processes across multiple GPUs more efficiently. vLLM leans on a new “attention algorithm” dubbed PagedAttention, which helps reduce memory waste and is already being used by developers at companies such as AWS, Cloudflare, and Nvidia.
Similar to Chatbot Arena, vLLM serves as the focal point of its creators’ PhD research work, and future commercialization is not currently on the agenda.
“At the moment, we do not have a plan to transition it into a stand-alone company — we are solely focused on making the open source project useful and widely adopted,” Mo said.
In addition to Sequoia’s $100,000 contribution for the year, other public sponsors include Andreessen Horowitz, which donated as part of its inaugural open source AI grant program last year, while the likes of AWS, Nvidia, and others have collectively helped vLLM cover its compute resources — which are not insignificant.
“For vLLM, we intend to use the fund to cover our continuous integration testing and benchmark suite,” Mo said. “These suites, running on GPUs, are expensive to maintain but critical to ensure the performance and correctness of vLLM for production usage.”
One clear message emerges from all this: AI and data infrastructure might be driving demand for open source technologies, but this demand creates significant costs for the project maintainers. Ion Stoica, professor of the computer science division at Berkeley and a Sky Computing Lab adviser, says that the funding pressure on open source project maintainers is “at least an order of magnitude higher” with the advent of LLMs.
“You have multiple kinds of GPUs, you have all of these other accelerators, and there’s also a difference in scale,” he said. “Ten years ago, most of the funding for a new startup would go to adding people; today, it’s going to infrastructure.”
Alignment
Digging a little deeper, and it’s clear that Sequoia’s involvement isn’t quite as altruistic as it might seem, owing to the fact that its two new fellows intersect with startups in its existing investment portfolio. By way of example, vLLM is used by Replicate, which Sequoia (and Andreessen Horowitz) backed across its Series A and Series B rounds.
Elsewhere, Sequoia last year co-led a $5 million seed round into an AI startup called Factory, with the startup’s founder and CTO Eno Reyes confirming that his company uses Chatbot Arena to “keep close track” of the top LLM options.
“They’re a key input to make sure we have the best product for our users,” Reyes said.
Similarly, Sequoia’s first fellowship award last year, FastAPI, leans heavily on Pydantic, the popular data validation library created by the eponymous startup in Sequoia’s portfolio.
However, Sequoia Capital partner Lauren Reeder told TechCrunch that this cross-pollination between fellows and portfolio isn’t a strict condition of its funding decisions, merely a “nice bonus” when things do align. And in truth, when an open source project is genuinely popular, there’s every chance that it will be picked up by one of Sequoia’s portfolio companies, which is a good way for the VC firm to hear about worthwhile projects.
In terms of how the funding is dispersed, Reeder says it’s open to whatever suits the team in question. For FastAPI, this involved making a direct payment to Montaño himself, which was simpler given that it was just the one individual. But where teams are involved, it makes sense to use a third-party fundraising platform such as Open Collective, which also comes with added transparency.
“For the two most recent fellows, we were supporting small groups rather than a single individual and Open Collective made it easier to manage the funds,” Reeder said. “Similarly, we’ve done both up-front payments as multi-install payments, depending on the needs of the project. Open Collective is more transparent, but the fees are not insignificant.”
Taking a pledge
There have been numerous other efforts to formalize open source project financing in the past five years alone, including dedicated FOSS funds from Indeed and Salesforce, a tacit acknowledgment that critical components of the tech stack are crying out for support.
One of the biggest efforts of late, however, hails from developer tooling unicorn Sentry, which itself has been donating to open source projects for many years. In 2021, Sentry adopted a more systematic program with firmer and more transparent commitments, and last month the company officially launched the Open Source Pledge to encourage other companies to get involved — either by donating directly through platforms such as GitHub Sponsors or Thanks.dev, or indirectly via foundations.
“We’ve run our program successfully for three years, but it’s not enough by itself to solve the open source sustainability crisis. So this year David [Sentry co-founder David Cramer] asked me to get other companies on board to make a bigger impact,” Sentry’s head of open source, Chad Whitacre, told TechCrunch.
The rules of engagement are thus: Commitments should amount to at least $2,000 per year for each developer the member company has on staff, which for Sentry itself translated to around $500,000 last year — $3,704 for each of its 135 developer headcount. Beneficiaries through the years have included Django, Python, Rust, and Apache. This year, Sentry has upped its own budget to $750,000, and with some two dozen additional members signed up to the Pledge at the time of writing, Whitacre is hopeful that open source software developers will see a little more compensation in the future.
“The primary intent with the Pledge is no-strings-attached payments to open source maintainers,” Whitacre said. “We vet companies when they join to ensure they’re complying with our guidelines, but there’s a fair amount of latitude within those guidelines.”
Aside from corporate members contributing cash, the Pledge has also attracted “ecosystem partners” to endorse the pledge, which includes foundations, individuals, and even storied VC firm Accel, which itself has invested in numerous open source startups through the years (including Sentry, both before and after it transitioned away from an open source license in 2019).
Accel partner Dan Levine said that if something is “truly critical,” then it should, in theory, be able to sustain itself as a business. The reality, though, is that if enough companies and developers are deriving value from a given open source project, there shouldn’t be any issues in getting financial support — at least in the early days, before the maintainers can forge a path to commercialization.
“In the case of open source software, while it can be used for free, users who find it essential are inherently motivated to ensure its sustainability,” Levine told TechCrunch. “The open source community, particularly on the demand side, needs to reassess its strategies and make more informed decisions to support critical projects. The Pledge is an excellent effort to galvanize the demand side to do what’s in their own best interests.”
Tech
Volkswagen’s cheapest EV ever is the first to use Rivian software
Volkswagen’s ultra-cheap EV called the ID EVERY1 — a small four-door hatchback revealed Wednesday — will be the first to roll out with software and architecture from Rivian, according to a source familiar with the new model.
The EV is expected to go into production in 2027 with a starting price of 20,000 euros ($21,500). A second EV called the ID.2all, which will be priced in the 25,000 euro price category, will be available in 2026. Both vehicles are part of the automaker’s new of category electric urban front-wheel drive cars that are being developing under the so-called “Brand Group Core” that makes up the volume brands in the VW Group. And both vehicles are for the European market.
The EVERY1 will be the first to ship with Rivian’s vehicle architecture and software as part of a $5.8 billion joint venture struck last year between the German automaker and U.S. EV maker. The ID.2all is based on the E3 1.1 architecture and software developed by VW’s software unit Cariad.
VW didn’t name Rivian in its reveal Wednesday, although there were numerous nods to next-generation software. Kai Grünitz, member of the Volkswagen Brand Board of Management responsible for Technical Development, noted it would be the first model in the entire VW Group to use a “fundamentally new, particularly powerful software architecture.”
“This means the future entry-level Volkswagen can be equipped with new functions throughout its entire life cycle,” he said. “Even after purchase of a new car, the small Volkswagen can still be individually adapted to customer needs.”
Sources who didn’t want to be named because they were not authorized to speak publicly, confirmed to TechCrunch that Rivian’s software will be in the ID EVERY1 EV. TechCrunch has reached out to Rivian and VW and will update the article if the companies respond.
The new joint venture provides Rivian with a needed influx of cash and the opportunity to diversify its business. Meanwhile, VW Group gains a next-generation electrical architecture and software for EVs that will help it better compete. Both companies have said that the joint venture, called Rivian and Volkswagen Group Technologies, will reduce development costs and help scale new technologies more quickly.
The joint venture is a 50-50 partnership with co-CEOs. Rivian’s head of software, Wassym Bensaid, and Volkswagen Group’s chief technical engineer, Carsten Helbing, will lead the joint venture. The team will be based initially in Palo Alto, California. Three other sites are in development in North America and Europe, the companies have previously said.
“The ID. EVERY1 represents the last piece of the puzzle on our way to the widest model selection in the volume segment,” Thomas Schäfer, CEO of the Volkswagen Passenger Cars brand and Head of the Brand Group Core, said in a statement. “We will then offer every customer the right car with the right drive system–including affordable all-electric entry-level mobility. Our goal is to be the world’s technologically leading high-volume manufacturer by 2030. And as a brand for everyone–just as you would expect from Volkswagen.”
The Volkswagen ID EVERY1 is just a concept for now — and with only a few details attached to the unveiling. The concept vehicle reaches a top speed of 130 km/h (80 miles per hour) and is powered by a newly developed electric drive motor with 70 kW, according to Volkswagen. The German automaker said the range on the EVERY1 will be at least 250 kilometers (150 miles). The vehicle is small but larger than VW’s former UP! vehicle. The company said it will have enough space for four people and a luggage compartment volume of 305 liters.
Tech
The hottest AI models, what they do, and how to use them
AI models are being cranked out at a dizzying pace, by everyone from Big Tech companies like Google to startups like OpenAI and Anthropic. Keeping track of the latest ones can be overwhelming.
Adding to the confusion is that AI models are often promoted based on industry benchmarks. But these technical metrics often reveal little about how real people and companies actually use them.
To cut through the noise, TechCrunch has compiled an overview of the most advanced AI models released since 2024, with details on how to use them and what they’re best for. We’ll keep this list updated with the latest launches, too.
There are literally over a million AI models out there: Hugging Face, for example, hosts over 1.4 million. So this list might miss some models that perform better, in one way or another.
AI models released in 2025
Cohere’s Aya Vision
Cohere released a multimodal model called Aya Vision that it claims is best in class at doing things like captioning images and answering questions about photos. It also excels in languages other than English, unlike other models, Cohere claims. It is available for free on WhatsApp.
OpenAI’s GPT 4.5 ‘Orion’
OpenAI calls Orion their largest model to date, touting its strong “world knowledge” and “emotional intelligence.” However, it underperforms on certain benchmarks compared to newer reasoning models. Orion is available to subscribers of OpenAI’s $200 a month plan.
Claude Sonnet 3.7
Anthropic says this is the industry’s first ‘hybrid’ reasoning model, because it can both fire off quick answers and really think things through when needed. It also gives users control over how long the model can think for, per Anthropic. Sonnet 3.7 is available to all Claude users, but heavier users will need a $20 a month Pro plan.
xAI’s Grok 3
Grok 3 is the latest flagship model from Elon Musk-founded startup xAI. It’s claimed to outperform other leading models on math, science, and coding. The model requires X Premium (which is $50 a month.) After one study found Grok 2 leaned left, Musk pledged to shift Grok more “politically neutral” but it’s not yet clear if that’s been achieved.
OpenAI o3-mini
This is OpenAI’s latest reasoning model and is optimized for STEM-related tasks like coding, math, and science. It’s not OpenAI’s most powerful model but because it’s smaller, the company says it’s significantly lower cost. It is available for free but requires a subscription for heavy users.
OpenAI Deep Research
OpenAI’s Deep Research is designed for doing in-depth research on a topic with clear citations. This service is only available with ChatGPT’s $200 per month Pro subscription. OpenAI recommends it for everything from science to shopping research, but beware that hallucinations remain a problem for AI.
Mistral Le Chat
Mistral has launched app versions of Le Chat, a multimodal AI personal assistant. Mistral claims Le Chat responds faster than any other chatbot. It also has a paid version with up-to-date journalism from the AFP. Tests from Le Monde found Le Chat’s performance impressive, although it made more errors than ChatGPT.
OpenAI Operator
OpenAI’s Operator is meant to be a personal intern that can do things independently, like help you buy groceries. It requires a $200 a month ChatGPT Pro subscription. AI agents hold a lot of promise, but they’re still experimental: a Washington Post reviewer says Operator decided on its own to order a dozen eggs for $31, paid with the reviewer’s credit card.
Google Gemini 2.0 Pro Experimental
Google Gemini’s much-awaited flagship model says it excels at coding and understanding general knowledge. It also has a super-long context window of 2 million tokens, helping users who need to quickly process massive chunks of text. The service requires (at minimum) a Google One AI Premium subscription of $19.99 a month.
AI models released in 2024
DeepSeek R1
This Chinese AI model took Silicon Valley by storm. DeepSeek’s R1 performs well on coding and math, while its open source nature means anyone can run it locally. Plus, it’s free. However, R1 integrates Chinese government censorship and faces rising bans for potentially sending user data back to China.
Gemini Deep Research
Deep Research summarizes Google’s search results in a simple and well-cited document. The service is helpful for students and anyone else who needs a quick research summary. However, its quality isn’t nearly as good as an actual peer-reviewed paper. Deep Research requires a $19.99 Google One AI Premium subscription.
Meta Llama 3.3 70B
This is the newest and most advanced version of Meta’s open source Llama AI models. Meta has touted this version as its cheapest and most efficient yet, especially for math, general knowledge, and instruction following. It is free and open source.
OpenAI Sora
Sora is a model that creates realistic videos based on text. While it can generate entire scenes rather than just clips, OpenAI admits that it often generates “unrealistic physics.” It’s currently only available on paid versions of ChatGPT, starting with Plus, which is $20 a month.
Alibaba Qwen QwQ-32B-Preview
This model is one of the few to rival OpenAI’s o1 on certain industry benchmarks, excelling in math and coding. Ironically for a “reasoning model,” it has “room for improvement in common sense reasoning,” Alibaba says. It also incorporates Chinese government censorship, TechCrunch testing shows. It’s free and open source.
Anthropic’s Computer Use
Claude’s Computer Use is meant to take control of your computer to complete tasks like coding or booking a plane ticket, making it a predecessor of OpenAI’s Operator. Computer use, however, remains in beta. Pricing is via API: $0.80 per million tokens of input and $4 per million tokens of output.
x.AI’s Grok 2
Elon Musk’s AI company, x.AI, has launched an enhanced version of its flagship Grok 2 chatbot it claims is “three times faster.” Free users are limited to 10 questions every two hours on Grok, while subscribers to X’s Premium and Premium+ plans enjoy higher usage limits. x.AI also launched an image generator, Aurora, that produces highly photorealistic images, including some graphic or violent content.
OpenAI o1
OpenAI’s o1 family is meant to produce better answers by “thinking” through responses through a hidden reasoning feature. The model excels at coding, math, and safety, OpenAI claims, but has issues deceiving humans, too. Using o1 requires subscribing to ChatGPT Plus, which is $20 a month.
Anthropic’s Claude Sonnet 3.5
Claude Sonnet 3.5 is a model Anthropic claims as being best in class. It’s become known for its coding capabilities and is considered a tech insider’s chatbot of choice. The model can be accessed for free on Claude although heavy users will need a $20 monthly Pro subscription. While it can understand images, it can’t generate them.
OpenAI GPT 4o-mini
OpenAI has touted GPT 4o-mini as its most affordable and fastest model yet thanks to its small size. It’s meant to enable a broad range of tasks like powering customer service chatbots. The model is available on ChatGPT’s free tier. It’s better suited for high-volume simple tasks compared to more complex ones.
Cohere Command R+
Cohere’s Command R+ model excels at complex Retrieval-Augmented Generation (or RAG) applications for enterprises. That means it can find and cite specific pieces of information really well. (The inventor of RAG actually works at Cohere.) Still, RAG doesn’t fully solve AI’s hallucination problem.
Tech
Not all cancer patients need chemo. Ataraxis AI raised $20M to fix that.
Artificial intelligence is a big trend in cancer care, and it’s mostly focused detecting cancer at the earliest possible stage. That makes a lot of sense, given that cancer is less deadly the earlier it’s detected.
But fewer are asking another fundamental question: if someone does have cancer, is an aggressive treatment like chemotherapy necessary? That’s the problem Ataraxis AI is trying to solve.
The New York-based startup is focused on using AI to accurately predict not only if a patient has cancer, but also what their cancer outcome looks like in 5 to 10 years. If there’s only a small chance of the cancer coming back, chemo can be avoided altogether – saving a lot of money, while avoiding the treatment’s notorious side effects.
Ataraxis AI now plans to launch their first commercial test, for breast cancer, to U.S. oncologists in the coming months, its co-founder Jan Witowski tells TechCrunch. To bolster the launch and expand into other types of cancer, the startup has raised a $20.4 million Series A, it told TechCrunch exclusively.
The round was led by AIX Ventures with participation from Thiel Bio, Founders Fund, Floating Point, Bertelsmann, and existing investors Giant Ventures and Obvious Ventures. Ataraxis emerged from stealth last year with a $4 million seed round.
Ataraxis was co-founded by Witowski and Krzysztof Geras, an assistant professor at NYU’s medical school who focuses on AI.
Ataraxis’ tech is powered by an AI model that extracts information from high-resolution images of cancer cells. The model is trained on hundreds of millions of real images from thousands of patients, Witowski said. A recent study showed Ataraxis’ tech was 30% more accurate than the current standard of care for breast cancer, per Ataraxis.
Long term, Ataraxis has big ambitions. It wants its tests to impact at least half of new cancer cases by 2030. It also views itself as a frontier AI company that builds its own models, touting Meta’s chief AI scientist Yann LeCun as an AI advisor.
“I think at Ataraxis we are trying to build what is essentially an AI frontier lab, but for healthcare applications,” Witowski said. “Because so many of those problems require a very novel technology.”
The AI boom has led to a rush of fundraises for cancer care startups. Valar Labs raised $22 million to help patients figure out their treatment plan in May 2024, for example. There’s also a bevvy of AI-powered drug discovery firms in the cancer space, like Manas AI which raised $24.6 million in January 2025 and was co-founded by Reid Hoffman, the LinkedIn co-founder.
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