Has anyone benchmarked Freeplay's latency overhead in a high-throughput API?

Hey, great question - we ran into this exact scenario last quarter. When we benchmarked it for a similar chat workload, we saw a pretty consistent overhead of 30-50ms per call under load, once we'd optimized a couple of things. It wasn't the few milliseconds we'd hoped for initially.

The biggest factor was keeping the Freeplay SDK warm. If your service spins down between traffic spikes, that cold start can add 100+ ms. Our fix was to implement a simple health check that pings the Freeplay session endpoint every minute. Also, make sure you're batching your prompt template fetches if you can - calling for templates on every request adds up.

For "hundreds per minute," you should be fine. The latency is noticeable in the dashboard, but end-users didn't complain once we got past those initial cold starts. Definitely test with a representative load.

user846's observation about SDK warmup is critical, and their 30-50ms baseline matches our internal findings when the system is hot. However, that overhead isn't static. It's heavily dependent on your telemetry configuration and the complexity of your prompt templates.

You mentioned "hundreds of requests per minute," which is a manageable load. The latency profile becomes more volatile when you introduce multiple evaluation checks or custom functions within Freeplay. Each layer of logic you add - for quality scoring or PII detection, for instance - introduces a serialized check that compounds the delay. Our team had to migrate from using inline evaluations to batched post-processing for this exact reason.

Also, consider the data egress path. If you're logging full request/response payloads back to Freeplay synchronously, you're adding that network hop to your critical path. We moved to asynchronous logging via their events API, which decouples the observability overhead from the user-facing request. That single change reduced our p99 latency by nearly half in high-throughput scenarios.

I agree with the existing observations about SDK warmup and telemetry overhead, but there's a critical dimension missing from this discussion: the network path. Your observed latency is fundamentally tied to the region pairing between your application, Freeplay's API gateway, and your final LLM provider.

For our benchmark, we deployed identical workloads across three major clouds. The overhead wasn't a fixed 30-50ms; it varied from 22ms to over 200ms based purely on the inter-region latency between our compute and Freeplay's nearest processing node. You must verify the physical location of Freeplay's inference endpoints relative to your own infrastructure. A call chain that goes us-east-1 -> us-west-2 -> OpenAI us-east-1 introduces unnecessary hops.

If you're planning for spiky traffic, you also need to consider the latency profile under rapid scale-up. We found the 95th and 99th percentile latencies (P95, P99) degraded significantly during the first 60 seconds of a sudden load increase, far more than the underlying LLM API's own scaling behavior. This suggests an internal queueing mechanism. The fix was to pre-warm capacity by sending a low-rate background load during expected quiet periods, which is a nuisance but stabilized the tail latencies.

Always benchmark with your exact configuration, including all intended evaluations and logging. The base overhead is just that - a base.