Tech
Ecosia and Qwant, two European search engines, join forces on an index to shrink reliance on Big Tech

Qwant, France’s privacy-focused search engine, and Ecosia, a Berlin-based not-for-profit search engine that uses ad revenue to fund tree planting and other climate-focused initiatives, are joining forces on a joint venture to develop their own European search index.
The pair hopes this move will help drive innovation in their respective search engines — including and especially around generative AI — as well as reducing dependence on search indexes provided by tech giants Microsoft (Bing) and Google. Both currently rely on Bing’s search APIs while Ecosia also uses Google’s search results.
Rising API costs are one clear motivator for the move to shrink this Big Tech dependency, with Microsoft massively hiking prices for Bing’s search APIs last year.
Neither Ecosia nor Qwant will stop using Bing or Google altogether. However, they aim to diversify the core tech supporting their services with their own index. It will lower their operational costs, and serve as a technical base to fuel their own product development as GenAI technologies take up a more central role in many consumer-facing digital services.
Both search engines have already dabbled in integrating GenAI features. Expect more on this front, although they aren’t planning to develop AI model development themselves. They say they will continue to rely on API access to major platforms’ large language models (LLMs) to power these additions.
The pair is also open to other European firms joining in with their push for more tech stack sovereignty — at least as fellow customers for the search index, as they plan to license access via an API. Other forms of partnership could be considered too, they told TechCrunch.
“The door is open and we are ready to talk to anyone,” said Qwant CEO Olivier Abecassis. “But we also want to focus and really secure the capacity to invest with our existing shareholders.”
“We know that we will fuel the company for the next years, and we know that our shareholders are ready to support it, and really expect us to move fast,” he added. “We will discuss with investors to speed up the developments and to do more — and with others to join in the partnership. So the plan is really to move as fast as possible.”
AI generating risks and opportunities
AI is driving a dual sense of urgency for both parties, as it rapidly creates a landscape of new opportunities and potential pitfalls.
“With the emergence of AI tools there is a different demand now for a search index,” Ecosia CEO Christian Kroll suggested. “The two providers, Bing and Google, are basically getting more reluctant to make their index accessible. And of course, as a search engine, we need an index. So that’s partially why we want to make sure we have access.”
“But also there is now a unique moment where you can use that type of index to build a very different experience — using generative AI to create a different experience — and we don’t want to be restricted in using that technology.”
Kroll also pointed to a regulatory environment in Europe that is keen to foster homegrown tech innovation in order to bolster the bloc’s strategic autonomy as another reason for making a bet on a homebrew search index now.
“The opportunity just has gotten a lot better,” he said. “With the [EU’s] Digital Markets Act, for the first time, ‘click and query data,’ for example, is going to be shared by other search engines — so we have access to that. Also access to platforms is different than it used to be. So we’ve been thinking about this for a long time, but now it’s the right moment to actually do it.”
“We believe that if we want to get a meaningful GenAI user experience, we need access to LLM models,” added Abecassis. “But we also need access to search tech.”
The combination of GenAI models with up-to-date information pulled in through search queries will be key to advancing search product utility, he argued.
“We believe that the combination of the two will be the next user experience for search,” he said. “Search and GenAI are not exactly the same. We believe that both will take benefit from the other, and the mix will be unique.”
“Google decided to have two strong products, but not mix them. And I can understand when I look at the legacy business model of Google. But in the future, something will happen between [these technologies] and that’s what we want to experience. And for that, any player on the market will need access to a search technology. That’s why we want to propose [this] to the market.”
Towards a European perspective
The pair’s new joint venture, which is being called European Search Perspective, is being set up with a 50:50 ownership split. (Note: EUP is their chosen acronym, rather than ESP.)
Ecosia and Qwant are not disclosing how much they’re each investing but said their shareholders are supportive. Plus, as a separate entity, EUP will sit outside the former’s not-for-profit business model — allowing it to raise external capital (assuming investors can be persuaded to get on board).
The index is expected to start serving France-based search engine traffic for Ecosia and Qwant by the first quarter of next year. It will then expand to include a “significant portion” of traffic in Germany by the end of 2025.
English would be the third language they’d look to add, the pair said, adding that more European languages could follow in the future if momentum builds.
On the operational side, Qwant’s engineering team will be moving to EUP, while Abecassis — who took on the CEO role at the search engine just over a year ago — will be CEO of the joint venture, too.
Qwant was acquired by a cloud technology group called Synfonium last year, which is backed by the founders of French cloud computing firm OVHcloud, with a goal of building a “European champion” for cloud services.
Discussing the plan for EUP in a call with TechCrunch, Abecassis explained Qwant had been working on developing its own search index even before it was acquired by Synfondium. Those efforts will now move over to EUP, he confirmed, with both team and IP assets transferring over.
Joining forces with Ecosia bolsters the chance of success, he suggested, as it expands the pool of data available for developing the index, as well as increasing investment in the project and enabling faster development, such as by being able to hire more engineers.
Ecosia has around 20 million monthly users globally, while Qwant has some 6 million users in France.
“If we want to be really efficient, we have to involve more people… and be more ambitious,” said Abecassis, recounting how Qwant approached Ecosia to ask it to consider a partnership on developing the search index.
“For Qwant, it’s a major opportunity to build better tech — because search technologies are good if they are used… So the more tech is used, the more money you can invest, but also the more data you get. One of the reasons why Google is so strong is it’s based on tons of data.”
The two firms share a few characteristics that make a partnership look like a good cultural fit, with both search alternatives being developed in Europe and having business models that seek to do something different compared to Big Tech’s standard surveillance capitalism playbook. EUP, meanwhile, will be headquartered in Paris.
“Building such a technology from scratch is almost impossible,” added Abecassis. “The more user[s] we have and the more data sets we have will make the technology more valuable.”
Kroll said Ecosia is bringing expertise, data and financing to the partnership — noting that as well as developing the search engine there will be other technologies that EUP will need to develop, such as widgets that can be served as part of search results.
The pair expects the partnership to boost the efficiency of search results they can deliver their respective users, as EUP hones its ranking algorithms — even as each search engine will continue to develop its own distinctive user experience.
Search ranking alternatives
Rival search engine Brave, which much like Qwant has a sales pitch that foregrounds privacy, has already built its own search index. It even removed the last API calls for text-based searches to Bing in April last year when it touted its service as a “real alternative to Big Tech search.”
Asked about this, Abecassis suggested Brave’s index cleaves closer to Google and Bing in the technical approach. Whereas he emphasized that EUP is being built from scratch, claiming it will be “very different” and will deliver more diverse search results.
“We don’t just copy Google or Microsoft and learn from them,” he stressed. “We really index all the documents that are available. We understand the documents, and then we have a team that works to find the best match between a document and the [search] query.”
“So it is true that there are probably some shortcuts to build such a tech by copying the main guys. We decided to go in a different direction and build everything from scratch. It’s harder but, we believe, it’s more sustainable.”
One big difference compared to Big Tech search is that EUP’s search index will serve up “privacy-first” results. What does that mean in practice? Abecassis said this is a result of tech developed by Qwant that does not personalize search results based on the user (as Google does).
“We’re going to continue to work without any [user] data [personalizing results],” he said. “Then we will improve our algorithm based on the data that are available.”
“I think it’s a big win — big privacy win,” added Kroll of the choice of technical approach. But he also emphasized the strategic value of having search infrastructure that’s made in Europe at a time of increasing geopolitical instability.
“From a European perspective… what does [search infrastructure reliance] mean for the dependency of the European Union? Especially considering [the U.S] election results… If the U.S. government decided that they would not want to provide search results to Europeans anymore, we in Europe would have to go back to phone books.”
“There is a privacy element, but then there’s also an element of data sovereignty, which I think is very important,” he added. “I of course hope that the U.S. and Europe will always stay strong allies. But I don’t know where the U.S. is heading, and I also don’t know where Europe is heading. So this is a very important element.”
A costly business?
TechCrunch asked Brave about its own decision to build a search index. It told us that prior to switching to its own tech it “always risked Microsoft imposing restrictions on us or simply cutting us off” — so the move was intended to free the business from a risky dependency.
“According to our quality assessment team, which does blinded assessments for quality of results, we are on par with Google and better than Bing in the countries we measure (those in which Brave Search is the default for Brave browser users),” the company also told us, adding that Brave Search is “the fastest growing search engine since Bing” with over 1 billion queries per month.
Discussing the costs of developing the index, Brave described the process as “long and very expensive” — pointing back to its 2021 acquisition of the open source Tailcat search engine, a technology whose development it said dates back to 2014.
“There is a reason why there are only three fully-fledged independent search indexes in the West,” Brave added.
The company licenses its search index via the Brave Search API. The API is being used by “many leading companies in the AI space,” per Brave, which added that it’s quickly becoming a “significant” source of revenue.
TechCrunch also asked search engineer Peter Popov about the costs involved in building a search index. Popov spent 15 years at Russian search giant Yandex, working on core search and ranking, and is now VP of ads at VK.
“Very roughly, a search index, which includes hardware and the cost of writing a search, does not cost much more than $10 million,” Popov told us, couching such an outlay as “not a very large investment.” He suggested that advances in AI have made it easier to produce quality search results without needing vast amounts of users feeding in data,”by using modern LLM models that contain knowledge of search semantics out of the box.”
At the same time, he warned there is growing challenge over where search bots can — or can’t — freely crawl. This is a problem as a search index needs wide access to information sources in order to usefully serve users’ queries.
“Proprietary platforms are often quite unfriendly to attempts to collect information,” Popov told TechCrunch.
“Creating a search index for the entire Internet is not such a difficult task from a technical point of view. The volume of useful information on the internet grows more slowly than computing power. By the way, one of the problems for scaling AI is precisely the relatively small volume of such information.”
“The useful-for-information-search internet is not that big,” he continued. “There is no internet of sites to search right now. And wait, of these [mainstream web] platforms, only Wikipedia is open to search. So that leaves Wikipedia search.
“After Wikipedia there are not many useful sites like arxiv.org or large online libraries. Information of this kind can be used in two ways — either by providing data during network training, or by feeding the neural network with search results during inference, in which case the search is one of the components of LLM working under the hood.”
In other words, in order for a search index to be useful, it also needs to be able to freely crawl the internet. But with Big Tech more jealously guarding info inside its own platforms these days, as giants compete to monetize user data afresh for training LLMs, this is also complicating the business of trying to get out from under their shadow by indexing the internet for search… From a rock to a hard place, then.
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.