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Useful LLM observability starts with traces that show the full workflow, usage figures that align with provider billing, and a deliberate policy for sensitive content. Record the model and workflow context needed to explain usage; choose sampling based on which failures you must retain; and leave prompts, responses, tool output, and retrieved content out of telemetry by default.
What an LLM trace should show
A model-call record alone can miss the reason a request was slow, expensive, or unsuccessful. Agent applications may include orchestration, multiple model calls, tool invocations, and retrieval. A hierarchical trace connects those steps so a team can inspect the workflow rather than treating each call as an isolated event.
OpenTelemetry’s Generative AI semantic conventions provide a shared vocabulary for describing GenAI activity across instrumentation and platforms. The conventions are maintained on a changing branch, so check their current status and exact field names when implementing or updating instrumentation. Amazon OpenSearch Service’s AI observability documentation is one example of hierarchical traces for agent workflows, model calls, tools, and retrieval; it also documents OpenTelemetry integration and querying with PPL. These are documented capabilities, not evidence of a comparative advantage over other platforms.
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- Workflow operation and the relevant parent-child relationships between orchestration, model, tool, and retrieval steps.
- Provider and the exact requested model name, when supplied by the provider.
- Provider-reported token usage and the span or workflow to which it belongs.
- Outcome and timing information needed to investigate errors and latency.
Keep span attributes focused on operational context. A trace should help answer what happened and where, without automatically becoming a copy of the conversation.
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How do I track LLM token usage and cost in traces?
Separate measured usage from calculated cost. Token counts reported by a provider are usage data; a platform’s dollar figure may be an estimate derived from a pricing table. To align usage telemetry with a customer bill, prefer provider-reported billed token counts when the provider exposes both billed and model-consumed counts.
Interpret token totals without double-counting
OpenTelemetry’s GenAI conventions say input usage should include all input token types, including cached tokens. Detailed token categories are breakdowns of a total, not additional usage to add on top of that total. If a provider reports categories such as cached, image, or reasoning tokens, document how they relate to the reported totals and whether the provider includes them in billed usage. Provider reporting differs, so do not assume every integration exposes the same categories or accounting basis.
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For a useful cost investigation, retain the provider, exact requested model name, operation, billed usage when available, and relevant workflow spans together. A derived cost should identify its pricing basis and be labeled as an estimate unless it is a provider-reported charge. Model, provider, date, and pricing basis all affect the result; there is no universal token price or meaningful universal cost figure.
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What the documented platform examples provide
MLflow documentation describes input, output, and total token counts for LLM calls, plus estimated USD cost that can be viewed at span and trace levels. The documented requirements are version-sensitive: token tracking is specified for MLflow 3.2.0 or later, while cost tracking is specified for 3.10.0 or later and requires the server’s [genai] extra. The same documentation says Databricks-managed MLflow cost computation requires LiteLLM or manually set cost attributes; it does not state that requirement for self-hosted MLflow. Check the current MLflow documentation for requirements applicable to your deployment before relying on these details.
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Amazon OpenSearch Service documents GenAI workflow traces and OpenTelemetry integration. The cited AI observability material establishes trace structure and querying capabilities, but it does not establish equivalent token-cost accounting details to those documented for MLflow. Choose a platform against your own requirements rather than inferring a vendor ranking from these examples.
Should I use head sampling or tail sampling for LLM traces?
Head and tail sampling make decisions at different points. Head sampling is simpler and cheaper to operate in a pipeline, but it must decide before the full trace is known. Tail sampling can retain traces based on later outcomes, but needs state and additional operational capacity.
| Strategy | When it decides | What it can select | Main trade-off |
|---|---|---|---|
| Head sampling | Early, often using trace ID and a configured probability | Traces based on information available at the decision point | Efficient and straightforward, but cannot guarantee retention of errors, slow traces, or attributes that appear later |
| Tail sampling | After all or most spans arrive | Whole traces selected using errors, latency, attributes, or service-specific rules | Richer decisions, but requires stateful components, monitoring, and potentially significant resources at high traffic |
| Combined sampling | An early decision followed by later tail decisions | Only traces that survive the early stage can be considered by the tail stage | Can protect a high-volume pipeline, but early drops cannot be recovered by tail rules |
Choose based on what you cannot afford to miss
- Use head sampling when traffic is high and repetitive, early reduction is important, and the policy can tolerate missing some later errors or slow traces.
- Use tail sampling when retaining particular failures, latency outliers, or attribute-defined cases matters enough to justify buffering and policy operations.
- Use a combined strategy only with an explicit understanding that tail sampling cannot select traces already discarded upstream.
OpenTelemetry’s sampling guidance identifies 1,000 or more traces per second as one criterion for considering sampling, not a threshold every application should meet. It also says a rate of 1% or lower may represent traffic in high-volume systems; that is guidance, not a universal target or an independent benchmark. Sampling may be unnecessary when volume is low, aggregate data can be pre-aggregated, or regulation prevents dropping data without an affordable retention path.
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Sampling can reduce observability costs while preserving a representative view, but the policy itself has costs: compute to sample, engineering effort to maintain rules, and the opportunity cost of missing useful information. OpenTelemetry’s documentation summarizes the potential as: “Sampling is one of the most effective ways to reduce the costs of observability without losing visibility.” Treat that as a rationale for thoughtful policy design, not a promise that any sample will preserve every important signal.
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If sampling decisions depend on operation, provider, requested model, server address, or server port, those values need to be available when the relevant spans are created. Teams may also use provider/model groups or outcome attributes when their instrumentation supplies them; that is an implementation choice, not a universal convention requirement. Validate policies against representative normal traffic and known failure scenarios, and monitor the sampler itself for dropped data and resource pressure.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do I keep prompts and responses private in observability traces?
Treat model instructions, user messages, and model outputs as sensitive by default. OpenTelemetry’s GenAI semantic conventions state: “OpenTelemetry instrumentations SHOULD NOT capture them by default, but SHOULD provide an option for users to opt in.” The same caution applies to tool outputs and retrieved context: they can contain confidential information even when the prompt itself does not.
Minimize content before it enters telemetry
- Start with content capture disabled. Emit operational metadata such as model, provider, usage, outcome, and timing without storing prompt or response bodies.
- Make any opt-in deliberate and narrow. Define which teams, workflows, environments, and data types may capture content, and what investigative purpose justifies it.
- Review every content source. Assess prompts, completions, tool inputs and results, and retrieved documents. Redaction of only the user message does not address sensitive data returned later in the workflow.
- Apply masking or redaction before export where feasible. MLflow publishes guidance for masking sensitive data from traces, but masking is one layer of a data-handling design; it does not prove that all sensitive data has been removed or that a deployment satisfies a legal requirement.
- Control access and retention. Restrict trace access to people with a need to investigate, set retention periods, and review who can retrieve content and how it is protected.
Use external content storage when content capture is justified
For production use cases where content is needed but telemetry volume or access needs must be controlled, OpenTelemetry describes storing content outside spans and recording references in the trace. This keeps the telemetry record smaller and allows separate access controls for the underlying content. A reference is not itself a privacy safeguard: protect the external store, restrict reference resolution, and apply an appropriate retention policy there as well.
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Large prompts and outputs may also exceed telemetry attribute or envelope limits. External storage avoids treating a trace backend as an unlimited content archive, while leaving the trace useful for linking operational events to separately governed content when access is authorized.
Quick Recap
A practical rollout sequence
- Instrument the workflow. Represent orchestration, model calls, tools, and retrieval as connected spans; adopt the current OpenTelemetry GenAI conventions and verify their field names and stability status.
- Establish usage semantics. Record provider-reported billed usage when available, document token categories and totals, and distinguish measured usage from estimated cost.
- Keep content off by default. Exclude prompts, completions, tool outputs, and retrieval payloads unless an approved use case requires them.
- Choose a sampling policy. Decide whether early efficiency or later error/latency selection matters more, then account for state, compute, maintenance, and the consequences of dropped traces.
- Test the whole path. Confirm that representative traces retain the context and usage needed for investigation, that content controls work across every span type, and that access and retention settings apply to both telemetry and any external content store.
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