Harness has announced a $240 million Series E financing round led by Goldman Sachs Alternatives. The funding round values the software delivery platform provider at $5.5 billion and positions it to expand its global footprint and strengthen its “AI for everything after code” strategy.
Harness plans to use the new capital to accelerate product innovation, expand its AI agent ecosystem, and invest in new regions across North America, EMEA, and APAC. The company says the funding will also support deeper integration between the AI platform layers and continued development of capabilities designed to meet compliance and regulatory demands in industries such as finance, healthcare, and public sector.
The new funding includes a $200 million primary investment and a planned $40 million tender offer with participation from IVP, Menlo Ventures, and Unusual Ventures.
The company’s pitch reflects a growing tension in enterprise software development: while AI-assisted coding tools have dramatically increased the speed at which developers write code, the remainder of the software lifecycle – the so-called outer loop – remains a bottleneck. Industry research and customer data suggest that developers spend only 30–40% of their time writing code; the remaining 60-70% is spent on testing, security controls, governance tasks, deployments, and performance optimization. These processes have traditionally been labor-intensive, difficult to automate, and highly interdependent, slowing down the end-to-end flow of software delivery.
Harness argues that the accelerating volume of code generated by AI is intensifying the challenge. More code means more testing, more verification, more security work, and more compliance oversight. According to the company, many organizations are seeing effective code volume grow by a factor of four, creating a widening gap between rapid development and reliable, production-grade deployment. Harness aims to close that gap with an AI-native delivery platform that automates and governs everything that happens after code is written – an approach it says aligns closely with the next stage of DevOps evolution, where automation and governance must keep pace with AI-driven development speed.
Jyoti Bansal, cofounder and CEO of Harness, said the industry is entering a new phase where AI’s impact must extend far beyond generating code. “The next frontier for AI in software engineering is applying intelligence to the delivery process – testing, verification, deployments, governance, and everything that happens after code is written,” said Jyoti Bansal. “Our customers are moving faster than ever with AI, but the delivery process is where complexity and risk pile up. Harness is leading the way in bringing clarity, automation, and control to this part of the lifecycle so teams can ship software quickly, safely, and reliably at scale.”
Platform Engineering and DevOps
The company’s platform, known as the Harness AI Software Delivery Platform, is built around a unified architecture that integrates specialized AI agents, a contextual knowledge graph, and an enterprise orchestration engine. The agents automate tasks such as testing, verification, governance enforcement, and security checks. The knowledge graph maps the relationships between code changes, services, deployments, test results, incidents, cost signals, and environments, allowing the system to interpret and act on events with deeper accuracy. The orchestration layer then executes the decisions generated by the AI system in a predictable and compliant manner.
By connecting these layers, Harness aims to create a delivery system that continuously learns from organizational behavior and architecture, improving automation quality over time. The company says this approach helps identify issues earlier, reduce human coordination overhead, and prevent failures from reaching production environments. Unlike general-purpose AI coding assistants, Harness positions its platform as purpose-built for enterprise software operations at scale.
The company’s traction across major enterprises provides early validation of its approach. United Airlines reported a 75% reduction in deployment times and migrated more than 80% of its workloads to the cloud with support from the platform. Morningstar consolidated 36,000 CI/CD pipelines into 50 templates, achieving faster builds and shifting from multi-week release cycles to daily deployments. Keller Williams increased deployment frequency sixfold and accelerated release cycles by three weeks. National Australia Bank cut build times by 67% and improved troubleshooting efficiency by 85%.
Investors say these performance gains are aligned with what they see happening across enterprise engineering organizations. “AI has shifted the bottleneck from writing code to delivering it, and Harness is solving that problem at enterprise scale,” said Beat Cabiallavetta, Partner at Goldman Sachs Alternatives. He noted that enterprises are redesigning development systems around AI and require platforms that combine automation, governance, and security into a unified workflow.
The funding round follows a year of significant operational growth for Harness. The company is on pace to surpass $250 million in annual recurring revenue in 2025, representing more than 50% year-over-year growth. Over the past 12 months, the Harness platform has facilitated 128 million deployments, powered 81 million builds, protected 1.2 trillion API calls, and supported nearly $2 billion in cloud spend optimization across its user base. Harness now employs more than 1,200 people across 14 offices globally and has been recognized by several industry lists including the Forbes Cloud 100 and Fast Company’s Best Workplaces for Innovators.
The raise comes at a moment when enterprises are rewriting internal engineering roadmaps to prepare for significantly higher software throughput driven by AI coding assistants. With code volume rising, the ability to test, secure, and deploy at the same pace is increasingly viewed as a determining factor in delivering reliable digital services. Harness is positioning itself at the center of this shift, betting that the convergence of AI and software delivery automation will define the next era of platform engineering and DevOps.
Executive Insights FAQ
Why is Harness focusing specifically on the ‘after-code’ phase of software development?
Because the majority of engineering time and operational risk occurs after code is written, and AI-generated code is increasing downstream workload dramatically.
How does Harness differentiate itself from AI coding assistants?
Harness focuses on testing, verification, deployments, security, and governance rather than code generation, positioning itself as an AI system for delivery rather than development.
What makes Harness AI suitable for enterprise-scale operations?
Its knowledge graph and orchestration layer use deep contextual awareness of environments, services, and policies to ensure automation can be executed safely.
How is customer demand shaping the company’s growth strategy?
Enterprises adopting AI coding tools are experiencing rapid increases in code volume, driving the need for automated systems that prevent deployment bottlenecks and production risk.
What will the Series E funding enable the company to do next?
Harness plans to accelerate innovation, expand internationally, deepen platform automation capabilities, and grow its AI agent ecosystem.
