I’m working with OpenAI’s audio services including Whisper and speech synthesis through LangSmith tracing. When I use regular chat models like GPT-4, I can easily see the token usage and associated costs in my LangSmith dashboard. But when I run audio processing tasks or text-to-speech operations, the monitoring only shows basic trace information without any usage statistics or billing details. Has anyone figured out how to get detailed metrics for these audio models? I need to track costs properly for my project budget.
OpenAI’s API responses include usage metadata for audio services, but LangSmith doesn’t show it properly yet.
Hit this exact problem last year - we were burning through our audio budget with zero visibility. Fixed it by building middleware that grabs the raw OpenAI response before LangSmith touches it.
Whisper responses include duration and processing details. Speech synthesis gives you character counts and audio length. All the pricing data’s right there.
I built a simple wrapper that logs everything to our metrics system. Takes maybe 30 minutes to set up and you get real-time cost tracking.
Just hook into the response pipeline before LangSmith strips the usage info. Once you’ve got that raw data, calculating costs is easy with OpenAI’s published rates.
Keeps everything in your current workflow without adding dependencies or changing how you call the APIs.
LangSmith’s internal handling of non-text responses is the culprit here. Audio services return metadata that doesn’t fit their token-based tracking, which I learned the hard way migrating from direct OpenAI calls. Here’s what worked: build a custom callback handler that grabs the original response data before LangSmith processes it. Their callback system captures the full API response before it gets standardized. Inherit from LangSmith’s base handler and override the response processing methods. Extract what you need - audio duration, character counts, model usage data - then push it to your own metrics store alongside their trace data. You keep LangSmith’s tracing benefits but get the detailed cost tracking you’re after. Best part? The callback runs automatically with each call, so no manual logging hassle.
Yeah, LangSmith’s audio blind spot is annoying. Middleware and proxy solutions work but you’re stuck maintaining extra infrastructure.
I fixed this with a pre-processing layer that handles everything before hitting OpenAI. It calculates expected costs from file sizes and parameters, then validates against actual usage.
The system automatically tracks audio duration, speech synthesis character counts, and model parameters. Everything feeds into unified reporting showing real costs across all OpenAI services.
Best part? It’s proactive. You know budget impact before processing starts, not after LangSmith strips the data.
I use Latenode since it handles request interception and cost calculations without building custom infrastructure. Takes about an hour to set up and gives complete visibility.
You keep your existing LangSmith workflow but get the detailed audio metrics you need for budget management.
Been dealing with this exact headache for months at work. Manual logging sucks and tools like Weights & Biases just add complexity you don’t need.
What fixed it for me was automated tracking that captures everything before it hits the API. Instead of waiting for LangSmith to parse audio costs after, I intercept requests and log all the details upfront.
My system automatically grabs file sizes, processing time, model types, and calculates costs using OpenAI’s pricing. Everything feeds into a dashboard so I can see real budget impact immediately.
This works for any API service, not just OpenAI audio. You get complete cost visibility without waiting for monitoring tools to catch up.
I built this with Latenode since it handles API interception and data processing seamlessly. Way cleaner than piecing together multiple tools.
We hit this constantly in production. LangSmith treats everything like text operations, so audio metrics just disappear. Here’s what worked for us: build a proxy layer between your app and OpenAI’s endpoints. It grabs the full response before LangSmith normalizes everything into their format. The proxy pulls out audio duration, TTS character counts, and usage data, then dumps it all into our own cost tracking database. Best part? Your LangSmith setup doesn’t change at all. You keep the trace visualization and debugging, but now you’ve got detailed cost data in your own system. We calculate spending against OpenAI’s audio pricing in real-time and get alerts when projects hit budget limits. Two days to build, but it killed all the manual cost tracking we were stuck doing.
i totally get your struggle! langsmith lacks that audio cost tracking for whisper. manually logging with custom tags seems like the best bet for now, or you could check out weights & biases. they’re more suited for audio models until langsmith updates their system.
langsmith wasn’t built for audio, huh? i just access openai’s usage endpoint directly to get monthly breakdowns by service type. not real-time, but it gives me decent cost visibility for budgeting without complicating things further.