Respan makes it dead simple to build production-ready LLM applications. With 2 lines of code, developers get a complete DevOps platform that speeds up monitoring & evaluate AI apps.
This is the 4th launch from Respan. View more
Respan Gateway
Launching today
Respan AI Gateway connects your app to 1,000+ AI models through one endpoint.
But routing is the easy part. Respan keeps production AI reliable and under control with fallbacks, retries, caching, spend limits, alerts, and full traces for every call.
Gateway, observability, evals, prompt management, monitors, and cost controls all run on one platform, so you do not need to stitch together five tools to debug production.
Free Options
Launch Team
Respan
Hi Product Hunt,
We built Respan AI Gateway because routing to more models is only the first step.
Once your AI product is in production, the harder questions show up fast:
What happens when a provider fails?
Which customer is driving cost?
Which model version caused the latency spike?
Did the fallback work?
How do we trace, evaluate, and control everything without stitching together five tools?
Respan Gateway gives teams one OpenAI- and Anthropic-compatible endpoint for 1,000+ models, with fallbacks, retries, caching, spend limits, alerts, traces, evals, prompt management, and monitors on the same platform.
The goal is simple: make production AI easier to ship, debug, and control.
Would love your feedback, questions, and support today!
@fran3cc Congrats on the launch! For teams that already have a multi-provider setup, what’s the simplest, lowest-risk way to try Respan Gateway in production what safeguards do you provide to ensure no customer-facing downtime or cost surprises during the transition?
2 lines of code complete DevOps platform. always sounds a bit too good 😄
What breaks first when you try to use it on a real production agent with tool calls and long traces?
Respan
@workout097_collab Totally fair. The 2 lines are for getting traffic into the gateway and traces showing up, not pretending production agents are easy.
In real agents, the first things that break are rate limits, long tool-call chains, cost spikes, and not knowing which model/tool/prompt version caused the issue.
Congrats on the launch! Genuine question from someone running multi-provider LLM calls in production: when a provider degrades mid-request (slow but not erroring), does the gateway support latency-based failover, or only hard-error fallback? And can the cost observability enforce per-provider daily caps, or is it reporting-only? The eval layer baked into the gateway is the part I haven't seen elsewhere — curious how you keep eval prompts from polluting the usage metrics.
This is what many dev teams are missing. I’ve seen so many projects stall because they couldn’t effectively trace which model version caused a latency spike.
How does Respan handle 'evals' for non-deterministic outputs? Is it easy to set up automated regression tests for prompt changes?
Having caching and fallbacks baked into one endpoint is a massive win for customer-facing AI features like conversational marketing bots. How does the gateway handle latency during failovers? Is the switch seamless enough that the end-user won't notice a lag?
DIY UX Test
Putting evals at the gateway layer instead of bolting them on downstream is a smart place to catch regressions before they reach prod. Does Respan run evals against live traffic samples, or is it more of a pre-deploy gate?
Sounds useful. We have a travel AI, and we want to run tests comparing the quality of our model’s responses against other popular models. Do you have any built-in mechanisms for that?