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
Launched this week
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.
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Launch Team
The underrated part here is having traces, evals, fallbacks, and cost controls in one place. Production AI gets messy fast, so fewer moving parts is a real win.
Respan
@farrukh_butt1 Really appreciate that!
The part I'd want to stress-test is how traces map back to customer and deployment context; that is usually where gateway-only setups stop being enough for debugging production incidents.
Respan
@jimmy_lee12 Absolutely thats a crucial point. Tracing back to the customer and deployment context is often where simple gateway setups fall short. For production incidents, having that full visibility really makes a difference otherwise its tough to pinpoint the root cause. Thats something we’re keeping in mind with Keywords AI, making sure traces carry enough context to be actionable.
@fran3cc Honestly, Al reliability is still a huge challenge. Glad to see tools tackling this problem.
Respan
@dipanshu_kushwaha5 Totally agree. AI reliability is still one of the hardest parts of putting these products into production.
Huge fan of the routing and spend-limiting features so far.
It really bridges the gap between a standard API router and a full-scale LLMops production platform.
Having traces baked in makes managing live traffic so much cleaner.
Respan
@kevin_huang_ynng_ Thanks so much! Glad to hear the routing and spend limiting features are hitting the mark. That gap between a simple API router and full scale LLMops is exactly what we were aiming to solve. Having traces baked in definitely helps keep live traffic manageable and its great to hear itss making a difference on your end.
The evals layer baked into the gateway is particularly interesting since most teams still just eyeball logs to check model performance.
I don't work in AI infra but even from the outside, the "something broke and you don't know why" problem makes total sense. having one place to see what's happening instead of piecing it together sounds like it saves a lot of pain. congrats on the launch.
Respan
@sidraarifali That’s exactly the pain we hear from teams. The first version usually works fine, but once real users, providers, prompts, and costs are involved, it gets messy fast.
Earth.fm
The "2 lines of code" promise immediately caught my attention. Anything that helps teams focus on shipping AI experiences instead of rebuilding infrastructure deserves a closer look. Well done!
Respan
@1mirul Thank you! That’s exactly the goal.
Teams should be spending their time building better AI experiences, not wiring together gateway logic, traces, evals, monitors, and cost controls from scratch.