At DigitalOcean, we have been vocal about our strategic shift: we are building the world’s premier Agentic Inference Cloud. Our mission is to provide the foundation where AI-native enterprises build and run production inference at scale. Today, I am thrilled to announce a significant step in that journey: we have acquired Katanemo Labs, Inc., a leader in agentic AI infrastructure.
By integrating Katanemo Labs, we are extending our platform further into the critical operational layer of agentic systems. Their technology pairs perfectly with DigitalOcean’s philosophy of operational simplicity, predictable economics, and scalable performance. We are also proud to welcome Salman Paracha, co-founder and CEO of Katanemo Labs, as our new Senior Vice President of AI. His team’s deep expertise in model research and open-source infrastructure development will be instrumental as we accelerate our mission to provide developers with an agentic inference cloud for running AI in production.
Bridging the Production Gap
In 2025, the center of gravity in AI shifted from experimentation to production. As the industry moves toward real-world deployment, the hardest problems have shifted from model access and accuracy to the challenges of running reliable, safe, and observable systems at scale.
- 61% of developers view closing the gap between prototype and production as their single greatest challenge, as found in our Currents research report.
- McKinsey research shows that fewer than 10% of AI use cases ever make it past the pilot stage.
- That same research identifies observability as a key capability for enabling large-scale, intelligent agent ecosystems to operate safely.**
By bringing Katanemo Labs into DigitalOcean, we are directly addressing this “production gap,” delivering core AI primitives—building blocks like orchestration, workflow execution, and safety protocols that make multi-agent systems reliable, observable, and fast to ship into reliable business assets.
Accelerating the Agentic Stack with Data Plane and Observability Innovation
At the heart of this acquisition is a proprietary, framework-agnostic data plane developed under the Plano open source project.
Plano acts as a “NoOps” layer for agents, abstracting away complexities so that engineering teams can focus on building core agent logic. Production-grade execution is handled automatically, providing predictability and performance developers need to scale with confidence.
We are also excited about Katanemo Labs’s proprietary research into agentic observability and its novel, signal-based innovation.
Their signals-based work provides early insights into agent behavior in production., By turning production traces into actionable behavioral insights, we are giving teams a way to efficiently identify informative interactions, diagnose failures, and continuously improve agent performance post-deployment.
A Unified Home for AI-Native Enterprises
Small Action Models from Katanemo Labs, like the Arch-Agent family, complement its data plane software with their flexibility and performance in real-world applications. By integrating these models, we ensure AI Teams don’t inherit the complexity or “margin stacking” of fragmented AI stacks. This acquisition gives our customers ready access to purpose-built primitives for autonomous agents, helping to accelerate time to production and reduce operational overhead.
What’s Next: Join Us at Deploy
We’re thrilled to have Katanemo Labs join DigitalOcean, reinforcing the flywheel we see every day: the world’s most ambitious AI builders are choosing DigitalOcean because we focus on the layers that matter most for real-world scale.
To see how this all comes together, I invite you to join Salman and me along with leaders from Arcee AI, Character.ai, Workato, and vLLM, at our upcoming conference.
Deploy: The Conference for the Inference Era
- When: April 28, 2026; 2:00pm - 8:00pm PT
- Where: Convene, in San Francisco & streamed globally
- The Focus: Real architectures. Real tradeoffs. Live systems running today.
We’ll be breaking down how to design inference systems that scale without breaking your performance—or your margin structure. Save your spot here ->