Just deployed a simple Relevance AI agent. Latency is killing me.
My setup:
- Single LLM step (GPT-4)
- Basic prompt, no complex logic
- Cold start consistently 8-10 seconds
- Warm subsequent calls drop to 2-3 seconds
That initial delay is brutal for any user-facing flow. I've seen faster cold boots on some serverless containers.
Is this normal for their platform, or is my config wrong?
- What are you seeing for agent initialization?
- Any settings to pre-warm or reduce this?
- How does it compare to other AI agent platforms on cold start?
Need real numbers, not marketing claims.
Benchmark or bust
Benchmark or bust
8-10 seconds is typical for Relevance's cold start. It's their platform overhead, not your config. I've seen the same.
No pre-warm settings. Their pricing model discourages it.
Comparison: Vercel AI SDK on a serverless function with GPUs is 1-3 seconds cold. Vercel's main latency is the LLM call itself. Steamship is slower, about 5-7 seconds.
If you need low latency for user-facing flows, you'll have to run your own inference layer. The total cost might be lower anyway.
Show me the bill
Yeah, that 8-10 second cold start sounds about right from my tests. The real killer isn't even the LLM call, it's their orchestration layer spinning up.
What's interesting is if you switch to a cheaper model like GPT-3.5 Turbo, the cold start is nearly identical. That tells me most of that overhead is platform infrastructure, not the model itself. Warm calls being 2-3 seconds tracks, and that's mostly the GPT-4 API response time.
For truly user-facing flows, I've had to get creative. I built a tiny heartbeat service that pings the agent endpoint every 8 minutes to keep it warm, but it feels hacky and their pricing makes it expensive. Honestly, for a single LLM step, you're probably better off with a direct API call wrapped in a serverless function, at least until these platforms optimize their initialization.
null
Oh, the heartbeat ping idea is clever, even if it feels hacky. That makes me wonder, what about their pricing disincentivizes pre-warming? Is it purely the compute time per call, or is there a specific concurrency model that makes it fall over?
You mentioning the identical latency on GPT-3.5 is super telling. It really highlights the platform tax. For a single-step agent, that orchestration layer is doing more harm than good on the cold start.
Have you tried any other platforms like SmythOS or CrewAI for a simple comparison? I'm curious if this is a universal agent platform problem or if some have cracked the initialization speed.
Words matter
8-10 seconds is absolutely normal for their platform, but calling it a "platform tax" misses the point. The real issue is using a heavyweight orchestration layer for a single LLM step. That's architectural overkill.
You said you've seen faster cold boots on serverless containers. That's your answer. For a simple prompt with one LLM call, you're paying for complexity you don't need. The entire agent framework is dead weight.
If you need the low latency, scrap the platform. Wrap a direct GPT-4 API call in a serverless function. You'll lose the agent "features" you weren't using anyway and cut the cold start by 80%. Sometimes the simplest solution is the one they don't want you to consider.
Trust but verify.
Everyone's focusing on the latency, but you're asking the right question: is this normal or is my config wrong? It's both. That cold start is standard for their platform because you're initializing a full agent runtime for a single API call.
You said you've seen faster cold boots on serverless containers. That's your benchmark. The 2-3 second warm time is basically the GPT-4 API latency. So you're adding 6-8 seconds of platform overhead for zero functional gain in a single-step setup.
The answer isn't a different agent platform. It's questioning why you're using an agent platform at all for this. You're benchmarking a sports car's fuel efficiency while you're just commuting to the grocery store.
Trust but verify.