DigitalOcean Powers Workato's Agentic Enterprise with Production-scale AI
Workato's AI Research Lab achieves 67% higher throughput, 77% faster time-to-first-token, and 67% lower inference costs on DigitalOcean's Agentic Inference Cloud, powered by NVIDIA
DigitalOcean (NYSE:DOCN), the Agentic Inference Cloud built for production AI, today announced that Workato's AI Research Lab is using its vertically integrated, inference-optimized platform, accelerated by NVIDIA Hopper GPUs, to advance the development of its next-generation enterprise AI agents while materially improving performance, cost efficiency, and deployment speed.
After moving its AI Labs workloads to DigitalOcean, Workato achieved immediate gains for frontier models, including Llama-3.3-70B:
- Inference cost: $0.77 power 1M tokens – 67% lower
- Throughput: 13,561 tokens per second per GPU – 67% higher
- Time-to-First-Token (TTFT): 1,455 ms at high load – 77% faster
- Time-to-Value: Reduced from weeks to days – 2X+ acceleration
With thousands of customers globally deploying over 1 trillion tasks since 2013, the Workato ONE platform enables customers to build, deploy, and govern AI agents at an enterprise scale. Built on a decade of integration expertise spanning 14,000+ applications, Workato's platform enables organizations to move from simple automation to agentic AI that can reason, act, and orchestrate work across the entire business.
To support that vision, Workato AI Research Lab required infrastructure capable of handling distributed training and sustained, reasoning-heavy inference under real production load. Not only was DigitalOcean able to provide high-performance NVIDIA Hopper GPUs faster than any other provider for the team to begin their work, but the Workato AI Research Lab team quickly discovered the significant performance boost and TCO improvement as a result of DigitalOcean's inference-optimized architecture and simplified experience.
"Before DigitalOcean, we didn't have a dedicated solution for in-house training and multi-node serving, and that was a major blocker for AI research," said Oscar Wu, AI Research Scientist at Workato. "DigitalOcean was the fastest provider to get us up and running, enabling us to advance our AI programs. The collaboration on performance optimization coupled with the support from the DigitalOcean team of solutions architects, accelerated our progress by roughly two to three times."
Working closely with Workato, DigitalOcean helped design and tune a distributed inference architecture on DigitalOcean Kubernetes (DOKS). As part of this collaboration, DigitalOcean configured NVIDIA Dynamo to intelligently coordinate workloads across interconnected GPU clusters. This ensured requests were routed to the most efficient compute resources in real time, reducing redundant processing, lowering costs, and improving responsiveness under heavy demand.
By optimizing orchestration around the model and intelligently routing requests across interconnected NVIDIA Hopper clusters, the platform eliminated redundant computation, a primary cost driver for long-context AI workloads. The result was sustained throughput, significantly faster time-to-first-token, and materially improved price-performance under high concurrency.
"As AI adoption accelerates, inference at scale is becoming the defining challenge for the industry," said Dave Salvator, Director of Accelerated Computing Solutions at NVIDIA. "The integration of the NVIDIA accelerated computing platform with DigitalOcean's inference-optimized platform unlocks the full potential of production-scale AI. The significant performance gains achieved by Workato highlight the impact of this collaboration."
For AI companies scaling production, inference economics directly impact margins, making cost efficiency and predictability critical. By moving to DigitalOcean's optimized environment, Workato reduced inference costs by 67% to $0.77 per million tokens while achieving a 33% hardware price-performance advantage.
Equally important, DigitalOcean's managed Kubernetes environment abstracted control-plane complexity and GPU scheduling, allowing Workato's lean AI Labs team to focus on research and product development rather than infrastructure management. This vertically integrated approach, spanning hardware, orchestration, and networking, is critical to reduce operational overhead while improving performance consistency and cost predictability.
"DigitalOcean lets us focus on research and advancing our models instead of managing infrastructure," said Kevin Huang, Infrastructure Engineer at Workato AI Labs. "We can provision GPUs quickly, deploy inference workloads in production, and iterate on real customer traffic without getting bogged down in platform complexity. The speed and performance has been critical to maintaining our momentum."
"Workato is pushing the frontier of agentic enterprise software," said Paddy Srinivasan, CEO of DigitalOcean. "As AI companies move from experimentation into production, the winners will be those who can iterate quickly on real customer workloads. We're proud to support Workato's momentum by providing an inference-optimized environment that lets their team focus on shipping — not managing infrastructure."
DigitalOcean is building the Agentic Inference Cloud for production AI, partnering with ambitious AI-native companies and enabling them to operate inference reliably at scale with predictable economics. Learn more about the DigitalOcean Agentic Inference Cloud.
About DigitalOcean
DigitalOcean is the Agentic Inference Cloud built for AI-native and Digital-native enterprises scaling production workloads. The platform combines production-ready GPU infrastructure with a full-stack cloud to deliver operational simplicity and predictable economics at scale. By integrating inference capabilities with core cloud services, DigitalOcean's Agentic Inference Cloud enables customers to expand as they grow — driving durable, compounding usage over time. More than 640,000 customers trust DigitalOcean to power their cloud and AI infrastructure. To learn more, visit www.digitalocean.com.
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