Reduce agent-trace costs by deciding which detail is worth retaining: keep enough traces to investigate failures and unusual latency, move routine aggregate questions to metrics, and avoid putting large prompts and responses into spans by default. Sampling can reduce exported data, but the right approach depends on traffic, failure patterns, operational capacity, and whether your requirements permit dropping telemetry.
Start by finding what is driving volume and cost
Before changing instrumentation or sampling, establish a baseline. Measure trace and span rates, exported bytes, payload sizes, retention, and backend charges. Break those figures down by service or workflow and, where your instrumentation supports it, by agent operation, model call, tool call, and retrieval path. Also record how often traces contain errors or slow operations; those are the cases a reduction policy should be designed to preserve.
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There is no universal savings figure for an agent workload. The result depends on the current topology, what spans contain, the sampling policy, and the backend’s pricing. A reduction in trace count may not yield the same reduction in billable cost if large attributes or other retained data dominate.
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Do not record full agent instructions, messages, inputs, or model outputs by default. They can make spans large and may contain sensitive content. The OpenTelemetry GenAI conventions discuss recording content on attributes, but the GenAI agent conventions page is marked Development; treat instrumentation and policies tied to these attributes as version-dependent and review them when conventions change.
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If full content is needed for controlled debugging, make capture an explicit opt-in with suitable access controls, or retain the content in an approved external store and put a reference on the span. Check attribute and backend envelope limits as well: messages may be large or include media. A reference can reduce telemetry payload size, but it does not remove the need to govern access to the underlying content.
Choose a sampling strategy for the workload
OpenTelemetry documentation calls sampling “one of the most effective ways to reduce the costs of observability without losing visibility.” The visibility tradeoff matters: sampling removes some trace detail, so a percentage is not a substitute for a policy about which traces are useful.
| Approach | What it can retain | Tradeoffs |
|---|---|---|
| Head sampling | A trace-level decision made early, commonly using the trace ID and a probability. | Simple and efficient, and a deterministic trace-level decision can keep a trace together. It cannot inspect the completed trace, so it cannot guarantee retention of a trace because of an error or latency that occurs later. |
| Tail sampling | Traces selected after most or all spans are available, using signals such as errors, total latency, attributes, or service-specific policies. | Supports richer selection, but requires stateful buffering, sufficient capacity, monitoring, and ongoing policy maintenance. Some options are vendor-specific. |
| Combined sampling | A modest early sample followed by richer tail-sampling rules for traces that pass the early gate. | Can limit pipeline load at very high volume, but traces discarded at the head stage never reach the tail sampler. It therefore cannot guarantee retention of every rare failure. |
| No sampling | All telemetry that instrumentation emits, subject to later storage or processing choices. | May be appropriate when traffic is low or requirements prohibit dropping telemetry. If the need is only for aggregates, pre-aggregation into metrics may be a better fit than retaining every detailed trace. |
Use head sampling when simplicity and efficiency matter
Head sampling makes an early decision, before later operations reveal whether the trace contains an error or an unusual delay. A deterministic decision based on the trace ID helps retain or drop a trace as a whole rather than keeping arbitrary individual spans. The tradeoff is that a trace selected only because of information learned later cannot be recovered by the head sampler.
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Use tail sampling when selection needs completed-trace context
Tail sampling can apply rules based on errors, overall latency, or attributes that are not available when a trace begins. That flexibility comes with state and compute requirements: the sampler needs to hold trace data while it decides, and operators need to monitor capacity and maintain the policy. OpenTelemetry’s sampling guidance warns that tail samplers require monitoring and ongoing policy maintenance.
Combine stages only when the early gate is acceptable
At very high volume, an early sample can limit how much data reaches a stateful tail sampler. Make the consequence explicit in the policy: a trace dropped by the first stage cannot be retained later, even if it would have matched a tail rule for an error or latency outlier.
Keep all traces when dropping data is not appropriate
Sampling is most useful when many requests are routine and the retained population remains representative. It is a poor fit where regulation or another requirement prohibits dropping telemetry, or where the volume is already low enough that sampling offers little benefit.
OpenTelemetry’s sampling documentation, last modified October 16, 2025, presents 1,000 or more traces per second as a point at which to consider sampling and says that 1% or lower can accurately represent the other 99% in high-volume systems. These are contextual cues from the documentation, not a recommended universal threshold or rate. Validate any chosen rate against your own traffic and the failures and slow requests you need to diagnose.
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Move aggregate questions to metrics
Use metrics for questions such as how many requests ran, how latency is distributed, and how token usage changes over time. Use selected traces to inspect an execution path, investigate a failure, or understand an unusual slow request. This division avoids relying on a detailed trace for every request when the question is about a population.
OpenTelemetry’s GenAI overview describes traces, metrics, and events as signals serving different levels of detail. When that overview was published in 2024, its event approach was described as in development and unstable; verify the current implementation status before making events a dependency. Token counts and other cost-relevant dimensions belong in metrics when your instrumentation can record them reliably and your aggregation needs are met.
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- Define what must remain diagnosable. Identify failure classes, latency outliers, and workflow paths that need trace-level detail. Confirm whether policy or regulatory requirements rule out dropping any telemetry.
- Match the sampler to available signals. Use early, trace-level decisions for straightforward probability-based reduction. Use tail rules when errors, completed latency, or later attributes must influence retention and the pipeline can support buffering.
- Give unusual traces higher priority where supported. Retain errors and unusually slow requests at higher rates than routine successful requests if your sampler and instrumentation expose the needed signals.
- Roll out against representative traffic. Compare sampled and unsampled aggregate behavior, including request volume, latency, and token usage. Check that the retained traces still let the team investigate the cases the policy is meant to preserve.
- Monitor the sampling pipeline. Watch for capacity pressure and fallback behavior, especially with tail sampling. Revise rules when agent workflows, instrumentation, or semantic-convention versions change.
Consider trace compression research separately from sampling
Sampling reduces how many traces are retained; compression research explores representing requests more compactly while retaining them. The 2025 Mint paper describes parsing traces into common patterns and variable parameters. In the paper’s experiments, Mint authors reported storage reduced to an average of 2.7% and network overhead reduced to an average of 4.2%. Those results belong to the evaluated approach and experiments; they are not an OpenTelemetry sampling benchmark or a guaranteed result for a production agent workload.
Decide with the whole cost and risk picture
Compare candidate policies on whether they retain rare errors and latency outliers, how much exported data and backend ingestion they reduce, the state and compute they require, and whether retained traces preserve coherent whole-trace context. Also account for prompt-content sensitivity, whether your workload permits dropping telemetry, and any reliance on vendor-specific features.
If operating stateful sampling infrastructure is burdensome, a managed observability backend may be worth evaluating, but confirm current capabilities and pricing for your needs. AWS documents OpenSearch Service AI observability with OpenTelemetry integration and hierarchical agent traces as one example to assess, not as a general recommendation.
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