Harness raises $240M Series E at $5.5B valuation to govern AI-written code
What's the deal? Harness, an AI-native software delivery platform founded in 2017, has raised $240 million in a Series E round led by Goldman Sachs AlternativesDealroom has a profile for this one. Try Dealroom →, lifting its valuation to $5.5 billion. The round splits into $200 million in primary capital and a planned $40 million tender offer that lets existing investors cash out some holdings.
What's the endgame? Harness targets what it calls the "outer loop" of software engineering — everything after a developer writes code, including testing, security scanning, governance, compliance checks, and cost optimization. The platform uses specialized AI agents, a proprietary Software Delivery Knowledge Graph, and enterprise orchestration tools to automate that pipeline.
The numbers: Harness is projected to pass $250 million in annual recurring revenue in 2025, more than 50% year-over-year growth. It reported 128 million deployments in the past 12 months and $1.9 billion in customer cloud spend optimized. The $5.5 billion valuation puts it at roughly 22 times projected 2025 ARR.
Who's backing it? Enterprise customers include United Airlines, MorningstarDealroom has a profile for this one. Try Dealroom →, Keller WilliamsDealroom has a profile for this one. Try Dealroom →, and National Australia BankDealroom has a profile for this one. Try Dealroom →. Previous backers include Menlo Ventures and IVP. Harness has also grown through acquisitions of security platform Traceable and Qwiet AI, adding security and AI-assisted code analysis layers.
Why now? AI code-generation tools have accelerated how fast teams produce code, creating more vulnerabilities, deployment complexity, and governance surface area for regulated industries like finance and aviation. Harness is positioning itself as the infrastructure layer that makes AI-accelerated development safe enough for enterprises that cannot afford outages or compliance failures.
The signal: Goldman's bet reflects sustained investor appetite for AI-native enterprise tools — and a growing view that the next infrastructure opportunity lies not in writing code faster, but in controlling what all that machine-generated code does once it ships.
Read more: cryptobriefing.com
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