solid writeup. the token counting inconsistency across providers is the part that bites hardest in practice — we've been building agent pipelines where the same prompt goes through Claude and GPT-4 depending on the task, and without a standard like this you end up maintaining two separate cost tracking implementations that drift apart over ti
one thing i'd add: for agentic workloads the context window usage per turn is almost more important than total tokens, since you're re-sending the full conversation each loop. being able to trace that per-span with OTel attributes would make it way easier to catch context bloat before it eats your budget.****
Why not route through a model routing system so you don't have multiple billings through diff providers? Something like OpenRouter, or using Azure's AI (they also support Claude) foundry and plugging into their endpoint API system?
yeah routing through something like OpenRouter works for the billing consolidation part. the tricky bit with OTel tracing specifically is that when you add a routing layer, the span hierarchy gets messy — you end up with the router's spans mixed into your application traces. Azure AI Foundry handles this better since the Claude endpoint there still emits standard OTel spans. for the token counting inconsistency though, routing doesn't fully solve it since each provider still reports usage differently. you'd still want a normalization layer sitting between the OTel exporter and your observability backend.
So for my agentic system I'm running on a VPS with OpenClaw and plan to use model-router you're saying I should use something like this anyways? I'm more technical than the average user by far but I'm not a programmer myself
honestly if you're running on a VPS with OpenClaw and model-router, OTel tracing is probably overkill for your setup right now. it's more useful when you're running multiple models across different providers and need to debug which call is slow or failing. for a single-agent setup the logs from OpenClaw itself should give you enough visibility. where tracing starts paying off is when you add a second model or start chaining agents — then suddenly you need to trace a request across multiple hops and that's where OTel shines.
AI Infrastructure Engineer building production LLM pipelines and reliability systems for distributed platforms.
Recently focused on AI/LLM systems: evaluation frameworks, structured output validation
Great point about context bloat in agentic loops — this is the kind of thing that's invisible until your budget alert fires.
We track gen_ai.usage.input_tokens per span, so in a ReAct loop each chat child span shows how many tokens were re-sent. In Jaeger you can watch input_tokens grow with each iteration — that's your bloat signal. What we don't have yet is a context_window_utilization_ratio (input_tokens / model_max_context). Your comment just pushed this up our backlog (github.com/vola-trebla/toad-eye/is...)
On multi-provider cost drift — we normalize to a single gen_ai.toad_eye.cost USD attribute. Each provider's accumulateChunk() handles the differences (Anthropic splits across message_start/message_delta, OpenAI sends usage in the final chunk, Gemini overwrites per chunk). Different plumbing, same attribute out.
This topic probably deserves its own deep dive — too much nuance for a comment. Might be the next article.
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solid writeup. the token counting inconsistency across providers is the part that bites hardest in practice — we've been building agent pipelines where the same prompt goes through Claude and GPT-4 depending on the task, and without a standard like this you end up maintaining two separate cost tracking implementations that drift apart over ti
one thing i'd add: for agentic workloads the context window usage per turn is almost more important than total tokens, since you're re-sending the full conversation each loop. being able to trace that per-span with OTel attributes would make it way easier to catch context bloat before it eats your budget.****
Why not route through a model routing system so you don't have multiple billings through diff providers? Something like OpenRouter, or using Azure's AI (they also support Claude) foundry and plugging into their endpoint API system?
yeah routing through something like OpenRouter works for the billing consolidation part. the tricky bit with OTel tracing specifically is that when you add a routing layer, the span hierarchy gets messy — you end up with the router's spans mixed into your application traces. Azure AI Foundry handles this better since the Claude endpoint there still emits standard OTel spans. for the token counting inconsistency though, routing doesn't fully solve it since each provider still reports usage differently. you'd still want a normalization layer sitting between the OTel exporter and your observability backend.
So for my agentic system I'm running on a VPS with OpenClaw and plan to use model-router you're saying I should use something like this anyways? I'm more technical than the average user by far but I'm not a programmer myself
honestly if you're running on a VPS with OpenClaw and model-router, OTel tracing is probably overkill for your setup right now. it's more useful when you're running multiple models across different providers and need to debug which call is slow or failing. for a single-agent setup the logs from OpenClaw itself should give you enough visibility. where tracing starts paying off is when you add a second model or start chaining agents — then suddenly you need to trace a request across multiple hops and that's where OTel shines.
Great point about context bloat in agentic loops — this is the kind of thing that's invisible until your budget alert fires.
We track
gen_ai.usage.input_tokensper span, so in a ReAct loop eachchatchild span shows how many tokens were re-sent. In Jaeger you can watch input_tokens grow with each iteration — that's your bloat signal. What we don't have yet is acontext_window_utilization_ratio(input_tokens / model_max_context). Your comment just pushed this up our backlog (github.com/vola-trebla/toad-eye/is...)On multi-provider cost drift — we normalize to a single
gen_ai.toad_eye.costUSD attribute. Each provider'saccumulateChunk()handles the differences (Anthropic splits acrossmessage_start/message_delta, OpenAI sends usage in the final chunk, Gemini overwrites per chunk). Different plumbing, same attribute out.This topic probably deserves its own deep dive — too much nuance for a comment. Might be the next article.