A five-person team usually picks the infrastructure it never has to think about, and that is the right call. It stops being the right call on the day the first enterprise customer asks where the agent runs and who is allowed to authorize its tools.
At prototype stage, Inngest is easy to understand. You write functions in TypeScript, Python, or Go, split durable work into steps, and let the platform deal with state, retries, flow control, and observability. Its current durable-agent model uses step-level memoization, so completed LLM calls and tool invocations are not repeated during a replay. It can also wait for events and invoke sub-agents.
That suits a web team stretching background jobs into longer AI workflows. The agent stays next to the application code, and there is not much infrastructure to run.
When the decision changes
The architecture conversation usually shifts in stages.
First you ship the feature. A TypeScript workflow calls an LLM, enriches some data, and writes a result. Developer experience decides the winner.
Then you have to survive failure. Tool calls need to resume at the right step. Inngest and Catalyst both handle that, through different execution models.
Then a second and a third agent stack show up. A Python team picks LangGraph, a .NET team picks Microsoft Agent Framework, and another group uses Google ADK. Catalyst's bring-your-own-framework model starts to matter here, because the platform standard can sit below those framework choices instead of competing with them.
Then you go through enterprise review. Customers ask for workload identity, deny-by-default access, private deployment, MCP governance, and evidence of what each agent did. That is where Catalyst's wider governance model can justify running a larger platform.
What I would compare
Retries and dashboards are the least interesting part of this comparison. I would look at:
- which languages and agent frameworks are supported
- what happens to completed steps and side effects during recovery
- who operates the workers, the state, and the execution plane
- how agent-to-tool access is authenticated and authorized
- which self-hosted, private, or air-gapped topologies exist
- how several teams share one platform without sharing permissions
Inngest can stay the best choice for developer-first durable functions, background jobs, and AI workflows, as long as its language and deployment model fit the team. Catalyst fits better once the requirement has grown from "make this function durable" into "run and govern many kinds of agents across the enterprise."
Both mistakes are common, and they are the same mistake pointed in opposite directions: buying the stage-four platform while you are still at stage one, and refusing to reopen a stage-one decision after the organization around it has changed. Let the architecture follow the risk you are actually carrying.
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