Case Study: Otto scales AI workflows with multi-tenant concurrency using Inngest

Leveraging multi-tenant concurrency to scale AI workflows

Otto, an AI-powered platform for streamlining workflows, faced a significant challenge in scaling its AI agents. Their product required a sophisticated queuing system with multi-tenant concurrency to manage unpredictable, spiky workloads where a single user could trigger thousands of simultaneous AI tasks. Otto needed a seamless solution to integrate this functionality without the high operational overhead of building a custom system or using complex tools.

Inngest provided the solution with its developer-friendly platform for workflow orchestration. By leveraging Inngest's concurrency keys and virtual queues, Otto gained precise control over rate limiting and workload management. This implementation saved the company a month of engineering effort, allowing them to build and scale their AI workflows efficiently while ensuring performance and reliability for their users.

Otto

Sully Omar

Co-founder, CEO