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.”
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Illustration: Dom Guzman
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