Want to keep track of the largest startup funding deals in 2025 with our curated list of $100 million-plus venture deals to U.S.-based companies? Check out The Crunchbase Megadeals Board.

This is a weekly feature that runs down the week’s top 10 announced funding rounds in the U.S. Check out last week’s biggest funding rounds here.

This was a big week for big checks, both confirmed and reported. Among confirmed rounds, the largest financing went to fintech provider Ramp, which landed $500 million at a $22.5 billion valuation to scale its visions around agentic AI. The next-largest financings went to MapLight Therapeutics, a developer of medicines for brain disorders, and Ambience Healthcare, a healthcare AI startup.

As for giant deals that were reported but not officially closed, Anthropric was said to be close to finalizing a round of up to $5 billion led by Iconiq Capital that would push its valuation all the way to $170 billion.

1. Ramp, $500M, fintech: New York-based Ramp, a provider of financial products and tools for businesses to automate finance tasks, raised $500 million at a $22.5 billion valuation. Iconiq Capital led the Series E financing, which brings total equity funding to date to $1.9 billion.

2. MapLight Therapeutics, $372.5M, biopharma and neuroscience: MapLight Therapeutics, a biopharma startup developing medicines for brain disorders, announced that it raised $372.5 million in Series D funding. Forbion and Goldman Sachs Alternatives co-led the financing for the 7-year-old, Redwood City, California-based company.

3. Ambience Healthcare, $243M, healthcare AI: Ambience Healthcare, an AI platform for healthcare systems to use in documentation, coding and clinical documentation, raised $243 million in a Series C round. Oak HC/FT and Andreessen Horowitz led the financing for the San Francisco-based company.

4. Quince, $200M, fashion: Quince, an affordable luxury brand online retailer, raised $200 million at a valuation of more than $4.5 billion, according to a report from Bloomberg. Iconiq Capital reportedly led the San Francisco-based company’s latest financing.

5. Observe, $156M, AI enterprise software: San Mateo, California-based Observe, a provider of AI-enabled observability tools for businesses, raised $156 million in a Series C funding round led by Sutter Hill Ventures. The financing brings funding to date for the 8-year-old company to more than $460 million, per Crunchbase data.

6. (tied) Motive, $150M, fleet management: San Francisco-based Motive, a provider of fleet tracking and driver safety software, raised $150 million in a new funding round led by Kleiner Perkins. The 12-year-old company is also reportedly taking steps toward an IPO.

6. (tied) Anaconda, $150M, AI software: Anaconda, a provider of AI tools for businesses using Python and open source applications, announced it raised over $150 million in a Series C funding round led by Insight Partners. The Austin, Texas-based company said it currently operates profitably with over $150 million in annual recurring revenue as of July.

8. Artbio, $132M, radiopharmaceuticals: Cambridge, Massachusetts-based Artbio, a clinical-stage radiopharmaceutical startup developing therapies (ARTs) to treat a range of cancers, raised $132 million in a Series B round that included Sofinnova Investments and B Capital as lead investors.

9. Fal, $125M, generative media: San Francisco-based Fal, a startup offering a generative image, video and audio platform for developers, raised $125 million in a Series C led by Meritech Capital Partners. The 4-year-old company said it has seen revenue increase 60x in the past 12 months.

10. Oxide Computer Co., $100M, cloud infrastructure: Oxide Computer Co., a developer of cloud infrastructure for on-premises computing, raised $100 million in a Series B round led by US Innovativr Technology. Founded in 2019, the Emeryville, California-based company has raised over $260 million to date, per Crunchbase data.

Methodology

We tracked the largest announced rounds in the Crunchbase database that were raised by U.S.-based companies for the seven-day period of July 26-Aug. 1. Although most announced rounds are represented in the database, there could be a small time lag as some rounds are reported late in the week.

Illustration: Dom Guzman

Despite a pickup in IPOs, startup exits and funding are still harder to come by than in years past. Add to that an increasingly competitive landscape for AI startups, and it’s no surprise that we’ve seen an upturn in startups buying other startups this year.

The reasons for the rise in startups buying their brethren are varied. In many cases, consolidation is driven by market forces, including a more challenging fundraising environment and more affordable valuations for buyers. For other startups, it’s simply faster to buy another company than try to build out certain technologies themselves.

By the numbers

In the first half of 2025, there were 427 reported M&A deals globally, according to Crunchbase data. That compares to 362 in the same period last year, representing an 18% increase.

For comparison’s sake, in the full years 2021 and 2022, there were more than 1,000 deals in which startups bought other startups, per Crunchbase data.

Buyer’s market

Michael Mufson, managing partner of investment banking firm Mufson Howe Hunter, believes that we’re seeing more early-stage startups combining forces because the fundraising environment “has become so challenging.”

“Venture capital is still tight, and without enough liquidity events to cycle capital back to LPs, VCs are being far more selective,” he told Crunchbase News. “For founders, it’s survival of the fittest — and that means getting creative to build a very tight investment thesis.”

In many cases, a merger between two early-stage companies can create a stronger, more compelling narrative for investors, in Mufson’s view.

“It may broaden the customer base, consolidate IP, or, increasingly, bring in critical capabilities like AI,” he added. “For startups lacking in AI expertise, acquiring or merging with a team that has that technical depth can help accelerate product development and improve funding prospects in a highly competitive market.”

Startup adviser Itay Sagie, owner of Israel-based Sagie Capital Advisors, agrees that the most significant driver of the startup-to-startup M&A uptick is the tightening of venture funding — despite a modest bump in venture funding globally in Q2.

“Small scale, startups which are far from being profitable have a hard time raising capital as VCs become more conservative, so they see M&A as the most logical option,” he told Crunchbase News in an email interview.

Another driver, Sagie believes, is that valuations appear to be “stabilizing at reasonable ARR multiplier ranges.”

This allows for larger startups that raised large rounds in 2021 at 40x-70x ARR valuations to use cash reserves to acquire smaller startups at reasonable valuations.

“So rather than facing a down round, they’re deploying that capital toward acquiring startups, especially ones that offer one of the three “Ts: complementary tech, traction, or talent,” Sagie added.

On the other side of the spectrum, the larger startups who are more financially sustainable with impressive unit economics and growth KPIs are even more attractive as startup buyers, in Sagie’s view, “as their equity is a more valid asset versus an overpriced, cash burning unicorn.”

Purchases include larger deals

Some of the deals this year have also been high-dollar transactions. And unsurprisingly, some of the larger deals involved AI companies.

• Specifically in the AI arena, one of the buzziest M&A transactions was OpenAI’s May purchase of Io, the device startup co-founded by famed Apple product designer Jony Ive, for a reported $6.5 billion.
• OpenAI also tried to purchase artificial intelligence-assisted coding tool Windsurf for $3 billion but that deal fell through. Instead, Cognition swept in to scoop up what was left of Windsurf after Google announced in mid-July that it was paying $2.4 billion to license Windsurf’s technology and for compensation.

Not all large M&A deals involved AI companies. Other notable startup purchases of peers this year include:

• Prime brokerage Hidden Road was acquired by crypto payments company Ripple in a $1.25 billion transaction announced in early April;
• In mid-May, Databricks announced its plans to acquire database management platform Neon in a deal reportedly valued at around $1 billion;
• More recently, cybersecurity unicorn Axonious announced it was purchasing Cynerio, a medical device security startup in a deal valued at just over $100 million; and
• On July 1, Canada-based legal software company Clio said it plans to acquire Spain’s vLex from Oakley Capital for $1 billion.

Also, many of the major acquirers raised large rounds before making their buys — with OpenAI in early April announcing a staggering $40 billion investment led by SoftBank. That deal marked the biggest venture investment ever. Last December, Databricks raised $10 billion at a $62 billion valuation, marking one of the largest venture capital raises of 2024 and one of the largest on record.

Asset purchases and acquihires

Lindsey S. Mignano, co-founder of SSM Law, notes an interesting trend she’s seeing: more asset acquisitions plus acquihires. This is where larger technology companies are buying the assets (think IP portfolio) of an early-stage company and taking one to three people from the founding team to integrate the technology and transition customer relationships.

SSM Law generally represents tech startups being acquired by larger, more established startups who have received more venture capital financing. An example of this would be an AI company that received a seed or Series A funding being acquired by a Series C or D company.

“The seller’s motivation to sell is often economic or market related — i.e., this company could not raise another round of venture capital or simply saw the writing on the wall that a big tech company was going to surpass them on ‘go-to-market’ due to strategic acquisitions or consolidations,” she told Crunchbase News in an interview.

The buyer’s motivation to buy is likewise economic, Mignano notes. It’s typically cheaper to buy the technology than build it, and cheaper “to buy the team with a six-month golden handcuff earnout than recruiting.”

But it’s also based on a rush to market, most particularly in the extremely competitive AI field.

“For AI companies, the buyer has immediate access to proprietary model architectures, inference platforms or edge-device integrations without the spend associated with AI training,” she said. “The buyer gets to immediately procure specialized datasets or fine-tuned models from a seller that effectively block out the competition in verticals with especially clunky sales cycles such as law, government or hospitals.”

In general, Mignano believes that “buyers are in a really good place right now.”

Related Crunchbase query:

Related Reading:

Illustration: Dom Guzman

By Medha Agarwal and Noah Lin

AI is redefining software’s role, enabling products that perform end-to-end work. This shift brings new pricing models, value metrics and GTM tactics.

At Defy, we’ve observed these four playbook stages that leading AI-first companies are using to scale.

Pricing and ROI: Selling value in a usage-based world

New pricing logic: AI products often use usage-based or hybrid pricing. That’s powerful, but less familiar. To succeed, teams must align pricing with outcomes and clearly articulate ROI.

Budget alignment: Unlike SaaS licenses or headcount, usage models need justification. For instance, Synthpop 1 charges per healthcare task automated — directly mapping cost to labor savings. This model resonates in labor-constrained industries.

Hybrid models for predictability: Blending tiered plans with usage minimums gives customers cost control while scaling affordably. For example: 10,000 credits at $500/month vs. 50,000 for $1,500. Lower unit costs reward growth.

Selling urgency: When pain isn’t acute, sellers should frame the cost of inaction. Ask:

• What’s the cost of staying manual?
• What happens if volume spikes?
• Can hiring solve this sustainably?

These questions qualify fit while creating urgency.

Discovery and qualification: Finding the right buyer

AI products require upfront investment, so qualifying buyers early is crucial.

Learn before pitching: Use discovery calls to understand how buyers currently tackle the problem. You can ask:

• What’s the current workflow?
• Who’s involved?
• Have you tried outsourcing or automation?

Position as labor alternative: Frame your product as a cost-effective way to avoid hiring. Ask:

• Is headcount or tooling the main constraint?
• Could this offset planned hiring?

Uncover real fit: Ask about competitors and hesitations around variable pricing. AI-first tools require real commitment — poor fit means wasted proof of concept and long sales cycles. Prioritize pain, urgency and organizational alignment.

Consultative selling: Guiding buyers through change

Once qualified, move from pitching to partnering.

Coach, don’t sell: Buyers often know the problem but lack a vision for solving it. Help them reimagine workflows and quantify the upside (speed, quality, reduced risk). Explain how your AI improves decisions — not just efficiency.

Build trust, not hype: Position your team as expert advisers. Highlight how competitors are adopting AI and frame your product as essential — not experimental. Focus on real problems, not futuristic features.

Co-create value: Buyers don’t want complexity. Understand their pain, then tailor a solution around it. When buyers feel heard and guided, they’re more willing to rethink their approach.

Proof through POCs: demonstrating real impact

A proof of concept isn’t just a technical validation — it’s also the key to proving value and earning trust.

Modern POCs = measurable outcomes: AI products tackle complex, variable tasks. POCs should reflect that — demonstrate consistent results across real scenarios, not just toy demos.

Structure for success: Successful teams scope tightly, set metrics early, and stay hands-on. Example:

• Origami Agents compares POC costs to hiring SDRs.
• Another AI platform focuses more on user enthusiasm and internal adoption than strict ROI, embedding in the workflow early.

Plan for conversion: Don’t wait until the POC ends to talk about the next steps. Begin commercial conversations midway, adjust pricing if needed, and ensure all stakeholders are aligned for expansion.

Final Word: Set the stage for long-term growth

AI adoption still feels experimental to many buyers. That’s why what happens after the sale matters just as much. Effective onboarding, early wins and long-term support are the foundation for retention and growth.

In our next piece, we’ll explore how AI-first companies succeed post-sale: from implementation playbooks to navigating internal resistance.

Medha Agarwal is general partner at Defy, where she partners with founders from the earliest stages. She previously spent seven years as a partner at Redpoint Ventures, backing early-stage companies including Whatnot, Tend, Proper Finance, LiveKit and Anvyl. Agarwal started her career at Bain & Co., founded two startups (Skedge.me and Roomidex), and invested at Bessemer Venture Partners. She studied social studies at Harvard, where she rowed varsity crew, and earned her MBA from Harvard Business School.

Noah Lin is an analyst at Defy, where he supports the full investment process, with a focus on sourcing and connecting with exceptional early-stage founders. He studied computer science and economics at Duke University. Before Defy, he was the first go-to-market hire at a high-growth Y Combinator startup, where he helped scale from early traction to repeatable enterprise sales.

Illustration: Dom Guzman