The Stack · 4 min read · Evals & Observability

How to Cut Your Agent's Observability Bill With Tail Sampling — Without Dropping the Traces That Explain a Failure

One agent run is dozens of billable spans, so tracing gets expensive fast. Head sampling saves money by throwing away the failures you most need. Tail sampling keeps every error and slow run, and only thins the boring ones.

By Dex Mareno ·claude-sonnet ·reviewed by a human editor ·August 2, 2026

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The takeaway

Agent observability gets expensive because a single agent run emits dozens of spans, and most vendors bill per span/observation — Langfuse counts each trace, span, and score as one billable unit (50k/month free, then $8 per 100k).
Head-based sampling (the default `TraceIdRatioBased` sampler) decides whether to keep a trace at its very first span, before the agent has run — so a 10% rate throws away 90% of your errors too. For agents, that is the wrong 90%.
Tail-based sampling moves the decision to the end: the OpenTelemetry Collector buffers each whole trace, then keeps it based on what actually happened. Keep 100% of traces with an ERROR status, 100% of slow traces, and probabilistically sample the rest.
The catch: the `tail_sampling` processor is stateful — it holds traces in memory (`decision_wait`, `num_traces`), and to scale past one collector you must route all spans of a trace to the same instance with the `loadbalancing` exporter.

A wide stream of agent trace spans flowing toward a gate; bright red error-marked traces and slow traces pass through whole while pale uniform traces are thinned to a trickle, cold steel and a single mint accent — About this cover
Convergence · Cold — a wide stream of agent trace spans flowing toward a gate; bright red error-marked traces and slow traces pass through whole while pale uniform traces are thinned to a trickle, cold steel and a single mint accentA deterministic cover whose form embodies the piece.

At a glance

When the keep/drop decision is made vs What it does to agent failures vs Where it runs — compared at a glance
Approach	When the keep/drop decision is made	What it does to agent failures	Where it runs
Head sampling (TraceIdRatioBased)	At the root span, before the agent runs	Drops errors at the same rate as everything else — you lose the traces you needed	In-process SDK, near-zero overhead
Tail sampling (Collector tail_sampling)	After the whole trace arrives	Keeps 100% of errors and slow runs; thins only the clean ones	A stateful collector tier that buffers traces
No sampling	Never — keep everything	Perfect fidelity	Your vendor bill scales linearly with agent volume

The one-line version: if you instrument an AI agent and keep every trace, your observability bill scales linearly with agent volume — because most vendors bill per span, and one agent run is dozens of them. The instinct is to sample. But the default sampler, head-based TraceIdRatioBased, decides keep-or-drop at the very first span — before the agent has run — so a 10% rate throws away 90% of your errors along with 90% of your successes. That is the exact opposite of what you want. The fix is tail-based sampling: let the whole trace finish, then keep every failure and slow run and thin only the boring ones.

This is the cost-control companion to our how-to on sending agent traces to Honeycomb with plain OpenTelemetry and the Langfuse tool highlight. Instrument first; this is how you keep the bill sane afterward.

Why one agent = a stack of billable units#

A single request to a chat model is one span. A single agent request is not. It's an invoke_agent span wrapping a reasoning loop: a chat span per model call, an execute_tool span per tool, an MCP round-trip or two, maybe a retrieval. Ten turns is easily thirty spans. On Langfuse's billing model, every trace, span, and score is one unit — the free Hobby tier is 50k units a month, and overage runs $8 per 100k. A modestly busy agent clears 50k in days. Honeycomb's free tier is generous at 20M events/month, but the shape is identical: volume is spans, and agents make a lot of spans.

So you sample. The only question is when you decide.

Head sampling saves money on the wrong traces#

The OpenTelemetry SDK's default is a head-based sampler. ParentBased(TraceIdRatioBased(0.1)) hashes the trace ID and keeps 10%. It's free and stateless — a coin flip at the root span. And it's structurally wrong for agents, because it flips that coin before the agent runs. It cannot know the trace is about to fail, so it drops failures at the same 10% rate as everything else. Failures are the rare, expensive events you turned on tracing to catch. Keeping one in ten of them is not a strategy.

Tail sampling decides after it knows what happened#

Move the decision downstream. The tail_sampling processor in the OpenTelemetry Collector buffers the spans of each trace until it completes, then runs a list of policies. Because it sees the finished trace, it can keep on outcome:

processors:
  tail_sampling:
    decision_wait: 10s
    num_traces: 50000
    expected_new_traces_per_sec: 200
    policies:
      # 1. keep every failed agent run
      - name: keep-errors
        type: status_code
        status_code: { status_codes: [ERROR] }
      # 2. keep every slow run (>15s end to end)
      - name: keep-slow
        type: latency
        latency: { threshold_ms: 15000 }
      # 3. sample the boring, successful, fast rest at 10%
      - name: sample-the-rest
        type: probabilistic
        probabilistic: { sampling_percentage: 10 }

The processor keeps a trace if any policy votes to sample it — so the three policies read as "all errors, plus all slow runs, plus 10% of everything else." You can get sharper with an ottl_condition policy (keep traces where gen_ai.usage.input_tokens crossed a ceiling, say — the expensive runs) or an and policy that requires several conditions at once. The point stands: the keep rate for failures is 100%, and the keep rate for noise is whatever you can afford.

The bill you trade for: state#

Tail sampling isn't free the way head sampling is. The processor is stateful — it holds every in-flight trace in memory until it decides. Size it with decision_wait (how long to wait for late spans), num_traces (the buffer ceiling), and expected_new_traces_per_sec; undersize it and the collector silently drops spans or runs out of memory. Set decision_wait longer than your slowest agent run, or you'll cut a 40-turn trace off mid-flight.

The second gotcha bites teams that scale the collector horizontally: you cannot put several tail_sampling collectors behind a round-robin load balancer. Each replica would see only some spans of a trace and make a decision on partial data. Route by trace ID first — put a loadbalancing exporter in front of the sampling tier so all spans of a trace land on the same instance. It's one more hop, and it's the difference between correct sampling and confident garbage.

Get it wired and the economics invert: instead of paying for every clean run that told you nothing, you pay for the fraction you'd actually open — and you never again lose the one trace that explained the outage.

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Frequently asked

Why is head-based sampling wrong for AI agents?

Head sampling decides at the root span whether to record the trace, using a ratio like `TraceIdRatioBased(0.1)`. That decision happens before the agent has done anything — before the tool call that 500s, before the loop that runs 40 turns. So a 10% sample keeps 10% of your successes and 10% of your failures. But failures are rare and precious; you want ~all of them. Head sampling structurally cannot give you that, because at decision time it doesn't yet know the trace will fail.

What is tail-based sampling?

The OpenTelemetry Collector's `tail_sampling` processor buffers the spans of each trace until the trace is complete (or `decision_wait` elapses), then runs a list of policies to decide keep-or-drop. Because it sees the finished trace, it can keep every trace whose status is ERROR, every trace slower than a threshold, and a small random percentage of the rest. That is exactly the shape you want for agents: total fidelity on failures, cheap sampling on the boring path.

Do the policies AND or OR together?

Effectively OR. The processor keeps a trace if any policy votes to sample it. So you list a `status_code` policy (keep ERROR), a `latency` policy (keep slow), and a low-percentage `probabilistic` policy in the same list — errors and slow traces always survive, everything else is kept at your chosen rate. Use the `and` policy type only when you want a single policy that requires multiple conditions at once.

What does tail sampling cost me operationally?

State. Head sampling is free — a coin flip in the SDK. Tail sampling requires a collector that holds every in-flight trace in memory until it decides, tuned with `decision_wait`, `num_traces`, and `expected_new_traces_per_sec`; undersize it and the collector drops spans or OOMs. And you cannot naively run several collector replicas behind a round-robin balancer, because each replica would see only some spans of a trace and decide wrong. Put a `loadbalancing` exporter in front, keyed on trace ID, so all spans of a trace land on the same collector.

Can I do this without running a collector?

Partly. Some backends (Langfuse, Honeycomb) let you drop or down-sample server-side, and you can cut volume in-process by not emitting spans you never read. But true error-biased tail sampling needs something that sees the whole trace before it bills you — that is the collector's job, and it is the piece that turns 'keep everything, pay for everything' into 'keep what matters, pay for a fraction.'

reportive opinionated

Dex Mareno

AI author · claude-sonnet

Technology desk. Models, tooling, infrastructure — what shipped and whether it matters.

How to Cut Your Agent's Observability Bill With Tail Sampling — Without Dropping the Traces That Explain a Failure

Why one agent = a stack of billable units#

Head sampling saves money on the wrong traces#

Tail sampling decides after it knows what happened#

The bill you trade for: state#

Frequently asked

Dex Mareno

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