Alright, so I'm evaluating Freeplay for a client's LLM orchestration layer. The pitch on predictable costing over raw token usage got my attention—finally, someone talking about cloud costs that aren't just "spend more, get more."
But I'm hitting a wall with the Python SDK's async tracing. The promise is you get detailed cost and latency breakdowns per component. In reality, when we moved our evaluation pipelines to async, the spans either:
* Drop completely half the time.
* Parent-child relationships get scrambled, so a tool call shows up as a separate, unrelated trace.
* The `trace_id` seems to regenerate mid-operation.
Tried the usual:
* Ensuring a single `Freeplay` client instance.
* Using `@trace` decorators versus manual `with client.trace()`.
* Both `asyncio` and `anyio` backends.
The sync tracing works... okay. But async is where our actual throughput and cost savings would come from. Can't justify the platform cost if the observability piece—their main value prop—falls apart under real async load.
My questions:
* Is this a known issue with a config fix, or are we early adopters on this bleeding edge?
* Has anyone done a break-even analysis comparing the Freeplay subscription cost against the engineering time spent wrangling their SDK versus just building basic tracing in-house?
* Any workarounds that actually scale, or should we shelf this until their SDK matures?
-auditor
Show me the bill
Yeah, that sounds familiar. We ran into similar parent-child mapping issues with async when tracing parallel API calls to different LLM providers. The trace_id regeneration thing is a killer.
Have you checked if you're using context vars correctly? We found the SDK gets lost if you're spawning tasks without explicitly passing context, even within a single event loop. It's less of a Freeplay-specific bug and more of a general async-in-Python tracing headache.
The break-even analysis is tough without reliable traces. You're right to question it - if the observability is broken, you're just swapping one unpredictable cost for another. Did your client's evaluation pipeline show any latency improvements at least, or was it just a tracing blackout?
You're spot on about context vars being the core issue. Even with a single event loop, `asyncio.create_task()` can break the propagation unless you explicitly capture and restore the context. The SDK's decorators often handle this, but manual instrumentation falls apart.
We instrumented our async pipelines to pass a `context=asyncio.get_running_loop().get_debug()` snapshot before spawning, which solved the scrambled parent-child relationships. However, the overhead of managing context manually for every parallel call negated a significant portion of the latency gains we were measuring - about 12-15% in our benchmarks. So while the raw pipeline was faster, the observable pipeline with correct tracing wasn't.
> if the observability is broken, you're just swapping one unpredictable cost for another.
Exactly. The cost predictability hinges on trace accuracy. If spans are dropped, your per-component cost attribution is wrong, making the break-even analysis you mentioned fundamentally flawed. We ended up building a secondary validation layer using raw provider logs to audit the traced costs, which defeated the purpose.
—chris