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Monitor AI systems as one connected operation, not as a token counter. Instrument every model request and workflow step as a trace with nested spans; record tokens, prices, duration, retries, errors, retrieval, tools, version, and tenant context; then aggregate cost and latency by the dimensions your teams manage. Measure quality separately with a versioned production dataset, automated checks, LLM-as-judge or rubric evaluators, and human review for ambiguous cases. Use the same traces to investigate regressions, gate releases, and trigger alerts. This approach shows whether a spend increase comes from a model, feature, customer, or retry storm; whether a slow response is caused by inference or a database; and whether a cheaper, faster release still solves users’ tasks safely.
What does AI observability need to cover?
A useful system answers three different questions at the same time: what did an AI workflow cost, how did it perform operationally, and did it produce an acceptable result? Cost and latency come from operational telemetry attached to each span. Quality comes from evaluations and user outcomes linked back to those spans. Keeping these signals connected lets an engineer move from an alert to the exact prompt, retrieval context, tool call, model response, and evaluator evidence.
Trace the entire workflow, not only the final model call
Create one trace for a user request, agent run, batch job, or other top-level operation. Add child spans for orchestration, prompt construction, retrieval, each LLM inference, tool execution, database access, queue waits, guardrails, and post-processing. A hierarchical trace makes it possible to see that a 12-second answer consisted of 1.8 seconds of retrieval, 4.5 seconds waiting on a tool, and two model calls rather than one opaque 12-second operation.
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Capture a stable record for every span
At minimum, store the provider, model, request and response identifiers, start and end timestamps, duration, input and output token counts, estimated cost, retry count, error and timeout status, prompt and response references, retrieval and tool metadata, deployment environment, application version, customer or team tags, and a correlation ID. Store sensitive prompts and completions by reference when possible, with access controls and redaction, rather than duplicating raw content in every telemetry system.
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Use semantic conventions where possible
OpenTelemetry-based GenAI attributes provide portable field names for model calls, tokens, and errors. Auto-instrumentation is available for common providers and frameworks, including OpenAI, Anthropic, Bedrock, LangChain, and others. A portable schema allows the same application telemetry to be routed to a different backend later, while custom attributes can carry product-specific fields such as plan, region, workflow name, or experiment ID.
How do I instrument an LLM application for production monitoring?
Start with one trace per business operation and add spans until every material wait, external call, and decision is visible. Roll out in stages so instrumentation does not alter application behavior or expose sensitive data.
- Define the trace boundary. Create a trace when a request enters your API, queue consumer, scheduled job, or agent runner. Propagate its trace and span IDs through asynchronous queues and service-to-service calls.
- Wrap model calls. Record provider, model, request ID, response ID, model parameters, token usage, status, and timestamps around each inference. Record streaming time-to-first-token separately from total completion time when your user experience depends on streaming.
- Instrument retrieval. Add spans for query rewriting, embedding generation, vector or keyword search, reranking, cache lookups, and context assembly. Include result counts, index or collection, and retrieval scores without copying full documents into logs by default.
- Instrument tools and dependencies. Trace function calls, HTTP requests, databases, file systems, queues, and policy checks. Capture dependency status, timeout, and response size so a model span can be correlated with the system that delayed or failed it.
- Attach business dimensions. Add application, feature, workflow, environment, region, customer or workspace, team, and release identifiers. Use low-cardinality values for dashboard grouping and a separate controlled field for high-cardinality IDs.
- Protect data. Redact credentials, payment data, health information, and personal identifiers before export. Keep content references or hashes when evaluators need to retrieve the original securely. Define retention by environment and data class.
- Validate in a staging workload. Compare request counts, latency, token totals, and error rates before and after instrumentation. Check that retries do not create duplicate business actions and that a telemetry outage cannot block the user request.
- Sample deliberately. Keep 100% of errors, timeouts, safety violations, and evaluation failures. Sample routine successful traces according to volume and cost, while preserving enough data for quality and drift analysis.
Fields that make traces actionable
| Field group | Examples | Why it matters |
|---|---|---|
| Identity | Trace ID, span ID, provider request ID, response ID | Connects application logs, provider records, and support tickets. |
| Usage | Input, output, cached, and reasoning-token counts when supplied | Enables cost estimates and detects prompt or completion growth. |
| Timing | Queue wait, time to first token, model duration, tool duration, total duration | Separates user-perceived delay from backend work. |
| Reliability | Status, error type, timeout, retry number, fallback model | Shows whether failures are transient, systemic, or caused by policy. |
| Context | Model, region, route, feature, customer, team, release | Supports ownership, chargeback, and regression analysis. |
| Content references | Prompt template version, response reference, retrieval set, tool name | Allows reproducible debugging without unrestricted content logging. |
How do I calculate and allocate AI costs?
Calculate each model span from measured token usage and the provider’s price for that model and region, then aggregate the result by the dimensions that drive decisions. Keep the pricing table versioned so a later report can explain which rates were used.
Use a transparent per-call formula
For a model call, calculate estimated cost as the input-token count multiplied by the input price, plus the output-token count multiplied by the output price. Add separate rates for cached input, reasoning tokens, images, audio, or other billable units when a provider exposes them. If a provider does not return usage, mark the estimate as incomplete rather than treating a missing count as zero.
For custom or self-hosted models, enter an internal rate card that includes GPU or accelerator time, hosting, storage, networking, and, if relevant, a shared platform allocation. Label these as internal estimates; they are not directly comparable with a provider invoice unless the accounting scope is the same.
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Aggregate at the level where someone can act
- Model and provider: identifies expensive or inefficient model choices.
- Application and feature: supports product budgets and optimization work.
- Workflow stage: exposes retrieval, planning, summarization, or tool-call hotspots.
- Customer, workspace, and team: enables chargeback, quota design, and support conversations.
- Region and environment: separates production spend from development and highlights regional price differences.
- Release and experiment: shows the cost impact of a prompt, model, or retrieval change.
Budget for retries and hidden fan-out
Report both cost per top-level request and the number of model spans inside that request. An agent that appears inexpensive per call can become costly when it fans out to five tools, retries a failed inference, or runs a judge model after every answer. Include fallback models and evaluator calls in the same trace, while keeping user-facing generation cost separate from internal evaluation cost.
Track estimates against invoices
Reconcile daily or monthly span estimates with provider billing exports. Differences can come from rounding, minimum charges, cached-token pricing, batch discounts, taxes, or calls that bypassed instrumentation. Use the reconciliation rate as a telemetry health metric and investigate material gaps before using dashboards for chargeback.
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Measure latency at span level and report percentiles, not only averages. Averages hide the slow tail that determines whether users abandon a request or an agent exceeds its deadline.
Use a latency budget with p50 and p99
Track p50 to represent a typical request and p99 to expose the worst one percent. Add p90 or p95 when your service-level objective uses them. Compute these values separately for model, route, provider, region, workflow stage, and release; a global percentile can conceal a severe regression in one customer segment.
Break total time into causes
| Component | Useful measurements | Typical action when it rises |
|---|---|---|
| Queue and scheduling | Queue wait, worker saturation, dispatch delay | Scale workers, adjust concurrency, or prioritize traffic. |
| Retrieval | Embedding time, search time, reranking time, result count | Tune indexes, cache queries, or reduce unnecessary retrieval. |
| Model inference | Time to first token, generation duration, output length | Change model, limit output, or stream responses. |
| Tools and databases | Per-call duration, timeout, dependency errors | Optimize the dependency, add a timeout, or use an asynchronous path. |
| Retries and fallbacks | Retry rate, backoff time, fallback duration | Fix the underlying error and cap retry budgets. |
Pair latency with reliability signals
Dashboard error rate, timeout rate, retry rate, cancellation rate, and saturation indicators beside p50 and p99. Segment by model and provider so a healthy overall number does not mask one failing endpoint. Correlate a slow model span with retrieval, tools, databases, queues, and infrastructure through the shared trace rather than guessing from separate dashboards.
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Define user-facing service objectives
Choose objectives that reflect the interaction: for example, a time-to-first-token target for chat streaming, a total completion target for synchronous APIs, and an end-to-end completion target for asynchronous agents. Record whether a request met its objective, and exclude only maintenance or client-cancelled traffic under a documented policy.
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Quality is an outcome, not a side effect of low cost or low latency. Build a versioned dataset from representative production interactions, evaluate every release against it, and monitor online results after deployment.
Build a representative golden dataset
Sample interactions across intents, languages, customer tiers, difficulty levels, retrieval conditions, and known failure modes. Remove or protect personal information, label the expected task outcome, and record the dataset version. Keep a holdout set that is not used to tune prompts so improvements are less likely to be overfit.
Use the least subjective evaluator that works
- Deterministic checks: exact fields, schema validity, citation presence, tool-call arguments, policy rules, and numerical tolerances.
- Rubric or LLM-as-judge evaluations: graded relevance, completeness, faithfulness, instruction following, and style when no deterministic test is sufficient. Version the judge model, rubric, and threshold.
- Human review: adjudicates ambiguous, high-impact, or safety-sensitive cases. Track reviewer agreement and provide a way to flag a defective rubric.
- Outcome signals: task completion, correction rate, escalation, abandonment, thumbs-up or thumbs-down, and repeat attempts. Treat these as noisy evidence and segment them by use case.
Monitor the quality dimensions that matter
Track hallucination and faithfulness separately: an answer can be fluent yet unsupported by retrieved evidence. Add task success, relevance, completeness, safety, refusal correctness, and tool-use accuracy where applicable. Link every score to the trace, model, prompt version, retrieval configuration, and release so a drop can be reproduced.
Run offline and online evaluation
Before rollout, run candidate prompts, models, and retrieval settings on the same dataset and compare quality, cost, and latency together. After rollout, sample live traffic for online evaluators and human review. Alert on statistically meaningful drops, not a single low score, and account for changing traffic mix by comparing like-for-like segments.
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What release gates prevent quality, cost, and latency regressions?
Use a gate that combines hard safety and correctness requirements with budget and performance thresholds. A candidate should not ship merely because it is cheaper or faster.
- Freeze the test definition. Select dataset version, evaluator versions, traffic segments, and comparison baseline.
- Run deterministic tests. Reject schema failures, prohibited content, incorrect tool arguments, and other non-negotiable errors.
- Run graded evaluations. Compare faithfulness, task success, relevance, and safety against the baseline with confidence intervals or minimum sample counts.
- Measure operational impact. Compare per-request cost, model-span count, p50 and p99 latency, timeout rate, and retry rate.
- Set explicit thresholds. For example, require no safety regression, a minimum quality score, and no increase beyond the approved cost or latency budget. The exact thresholds belong to your product’s risk tolerance.
- Canary the release. Route a controlled fraction of production traffic, preserve trace links, and compare matched segments before full rollout.
- Record the decision. Store metrics, evaluator evidence, approver, release ID, and rollback criteria with the deployment record.
Which AI observability architecture should I choose?
The right architecture depends on portability, operating capacity, data-residency requirements, and how much evaluation infrastructure you want to build. Compare these patterns before selecting a product.
| Architecture | Strengths | Trade-offs | Best fit |
|---|---|---|---|
| OpenTelemetry-first | Portable instrumentation, common GenAI semantic attributes, and freedom to change storage or analysis backends. | Teams must assemble dashboards, retention, evaluators, and alerting or operate compatible components. | Organizations standardizing telemetry across vendors or keeping backend flexibility. |
| Managed platform | Fast dashboards for traces, token usage, latency, errors, evaluations, retention, and alerting. | Usage-based pricing, vendor data-governance review, and possible limits on residency or customization. | Teams that need production visibility quickly and accept a hosted control plane. |
| AWS-native or self-managed OpenSearch | Deployment and telemetry-location control, integration with an existing OpenSearch operation, and OpenTelemetry-based ingestion. | Storage, scaling, upgrades, and evaluator implementation remain your responsibility. | Organizations with established AWS or OpenSearch expertise and strict control requirements. |
Questions to ask during evaluation
- Which providers, model APIs, agent frameworks, and retrieval systems are instrumented automatically?
- Does the system support OpenTelemetry or OpenInference attributes, and can you export raw spans through an API?
- Can it calculate per-span cost for public, custom, and self-hosted models with versioned pricing?
- Are p50, p95, and p99 latency, error, timeout, retry, and saturation views available by workflow and release?
- Does it support offline datasets, online evaluators, human review, rubric versioning, and trace-linked evidence?
- Can alerts integrate with your incident system and include direct links to the offending trace and evaluation?
- Where are prompts, completions, and evaluator data stored, for how long, and under what residency controls?
- What redaction, access-control, encryption, sampling, and ingestion-cost controls are provided?
- What is the total operating cost after data retention, evaluator calls, storage, and engineering time?
What do published plan figures mean for budgeting?
Vendor plan figures are useful for an initial estimate but are volatile and should be rechecked before procurement. A 2026 display for Datadog Agent Observability Pro listed a starting price of $160 per month on annual pricing and included 100,000 LLM spans per month; its free tier listed 40,000 LLM spans per month. These are vendor-published plan figures, not a forecast of your bill: confirm current region, contract, retention, overage, and feature terms.
| Published figure | Qualification | How to use it |
|---|---|---|
| 40,000 LLM spans per month | Free-tier allowance displayed by the vendor in 2026. | Estimate whether a pilot can fit without assuming production retention or evaluator volume. |
| 100,000 LLM spans per month | Included in Agent Observability Pro, according to the vendor’s 2026 plan display. | Compare with your instrumented span count, including retries, tools, and evaluation calls. |
| $160 per month | Starting price shown for annual pricing in the 2026 display; pricing can change. | Use only as a dated reference point and verify current commercial terms. |
How should I design alerts and incident response?
Alert on changes that require action, and include enough context to investigate without searching several systems. A good alert identifies the affected feature, model, release, customer segment, and trace or evaluation evidence.
Operational alerts
- Cost per successful request or daily spend exceeds a budget threshold.
- p99 end-to-end latency or time to first token breaches the service objective.
- Error, timeout, retry, or fallback rate rises above its baseline.
- Token counts or model-span counts jump after a release, indicating prompt growth or agent fan-out.
- Provider quota, rate-limit, or saturation indicators approach a safety margin.
Quality and safety alerts
- Faithfulness or task-success scores fall below the release threshold.
- Hallucination, unsafe-output, or refusal-correctness failures rise in a monitored segment.
- Human reviewers identify a new failure pattern or disagree sharply with an automated judge.
- User corrections, escalations, or abandonment increase after a prompt, model, or retrieval change.
Respond with a bounded rollback plan
Link the alert to representative traces, evaluator examples, and the deployment ID. First stop further rollout or reduce traffic, then compare the failing release with the last known-good version. Roll back the prompt, model, retrieval index, tool, or configuration that changed; if the provider is failing, invoke a tested fallback with a capped retry budget. Keep the incident evidence so the golden dataset and evaluator can be improved.
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What privacy, retention, and sampling controls are required?
AI traces can contain the most sensitive data in your system. Classify prompts, retrieved passages, tool arguments, and completions before deciding what to retain. Apply field-level redaction, encryption, role-based access, and separate retention periods for raw content, metadata, and aggregate metrics.
Sampling should reduce ingestion cost without hiding rare failures. Retain all error, timeout, safety, and evaluator-failure traces; retain a statistically useful sample of successful traffic; and increase sampling temporarily during an incident or rollout. Document how sampling affects quality estimates, and never compare releases using materially different sampling rules without adjustment.
What are the common monitoring failures and fixes?
| Failure | Why it happens | Fix |
|---|---|---|
| Only total tokens are tracked | No ownership or feature dimensions were attached. | Add model, route, feature, customer, team, release, and workflow-stage tags. |
| Average latency looks healthy while users complain | A long tail is hidden by the mean. | Use p50 and p99 by route and stage; inspect queue, tool, and retrieval spans. |
| Quality scores drift without explanation | Dataset, judge, rubric, or traffic mix changed. | Version evaluator inputs and compare matched segments with a holdout set. |
| Costs do not match invoices | Pricing versions, cached tokens, retries, or uninstrumented calls are missing. | Version rate cards, include all spans, and reconcile estimates with billing exports. |
| Telemetry creates a privacy incident | Raw prompts and completions were copied into unrestricted logs. | Redact before export, store content by protected reference, and restrict retention. |
| Sampling hides rare failures | Errors were sampled at the same rate as routine successes. | Keep 100% of errors, safety events, timeouts, and evaluation failures. |
| Retries inflate spend and latency | Retry storms or agent fan-out are not visible in the parent trace. | Record retry number and parent-child spans, cap retries, and alert on fan-out. |
How do I know the monitoring program is working?
After implementation, an engineer should be able to open a cost or latency alert, identify the responsible model and release, inspect the complete workflow trace, and find linked quality evidence. FinOps should be able to allocate spend without manual log searches. Product and safety teams should be able to compare releases on the same dataset and see whether a quality change came with a cost or latency trade-off. If those paths work with sampled production traffic and protected content, the system is providing operational control rather than merely collecting telemetry.
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Frequently Asked Questions
How often should AI model prices be updated in the cost monitor?
Update the rate card whenever a provider changes pricing, model version, region, or billing unit, and keep the effective date on every record. Reconcile estimates with the provider invoice at least monthly so cached-token, batch, or minimum-charge differences are visible.
Should prompts and completions be stored in observability traces?
Store them only when debugging or evaluation requires it, and prefer protected references, hashes, or redacted excerpts in the general telemetry stream. Apply role-based access and shorter retention to raw content than to aggregate metrics.
Can an LLM judge replace human quality review?
No. A judge is useful for repeatable, large-scale scoring, but it can share the model’s blind spots and misread ambiguous cases. Keep human review for high-impact decisions, safety-sensitive outputs, and samples used to validate the rubric.
How do I monitor an agent that calls several models?
Create one parent trace with a child span for every model, tool, retrieval, and retry operation. Report both total cost and span count per user task, then break down latency and failures by stage so fan-out and fallback behavior are visible.
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What should I do when quality improves but cost and latency get worse?
Treat it as a measured trade-off rather than a single-metric win. Compare alternative models, prompts, retrieval limits, caching, and output constraints on the same dataset, then set product-specific thresholds for the quality gain you are willing to buy.
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Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
