Inngest vs Temporal

Pick two tools from the same category to compare side-by-side.

	Inngest Durable functions and event-driven queueing platform for agent backends, with the AgentKit framework for building AI agents.	Temporal Durable-execution runtime that persists workflow state and automatically retries failures, used to run long-running agent workflows reliably.
Category	deployment	deployment
Vendor type	hybrid	hybrid
Pricing	freemium	freemium
Strengths	+ Step-level retries and durable state without managing workers or queues + Flow control (throttling, per-tenant concurrency, rate limits) as code, useful for LLM rate limits + Runs on existing serverless or server infrastructure rather than dedicated workers + AgentKit framework targets pausable, resumable AI agent workflows	+ Workflow state persists across crashes and deploys, suiting long-running agent loops + Automatic retries and timeouts for failed activities such as LLM and tool calls + MIT-licensed open-source server with multiple language SDKs + Managed Temporal Cloud option avoids self-hosting the cluster
Tradeoffs	− Execution orchestration depends on the Inngest service or self-run server, adding a platform dependency − Less low-level control than operating your own queueing or workflow cluster	− Self-hosting the server cluster and its datastore is operationally heavy − Deterministic workflow constraints impose a real learning curve on developers − Heavier abstraction than simple job queues for short-lived tasks
Used in stacks	0	0