Choosing an AI observability platform: traces, evaluation, and cost

An AI application can be healthy by ordinary service metrics and still give the wrong answer. It can also produce a good answer too slowly or at a cost the product cannot support. Choosing an observability platform means deciding how the team will investigate all three: runtime behavior, output quality, and usage.

A single user request may involve retrieval, several model calls, and external tools. Application performance monitoring (APM) can trace instrumented operations along that path, but it needs AI-specific context to explain model usage and evaluate output. A successful HTTP response does not establish that retrieved evidence was relevant or that the answer followed the task.

This guide compares four approaches: AI capabilities in Guance, AI-focused development platforms, extensions to an existing APM platform, and gateway or evaluation components. The comparison focuses on trace coverage, evaluation, cost attribution, and deployment constraints rather than assigning a universal winner.

Published by Guance. This comparison is based on product documentation; it does not report a shared performance benchmark.

Define what the team needs to observe

Start with three related workloads:

Monitor model calls. Measure latency, errors, model identity, and provider-reported token usage. Streaming applications may also need time to first token and completion time; those answer different questions.

Trace the application path. Connect the user request to retrieval, tool execution, model calls, and downstream services. Preserve parent-child relationships and correlate activity across requests when a session spans several turns.

Evaluate output quality. Apply deterministic checks, task-specific scoring, or model-based judges to assess whether the result met the requirement. Faithfulness to retrieved context, answer relevance, schema conformance, and task completion are distinct criteria. An evaluation score should identify its rubric, evaluator version, and input data.

Model-based judging can run on sampled production traffic or on a controlled dataset. It adds inference cost and latency to the evaluation process, and its scores need calibration against labeled examples. Neither a quality score nor an infrastructure dashboard substitutes for the other.

OpenTelemetry can reduce the effort of collecting and correlating these signals. Its GenAI conventions continue to evolve, however, and a backend's support for OTLP transport does not guarantee support for every AI attribute or workflow. Verify the exact instrumentation, convention version, and fields that reach the backend. OpenTelemetry GenAI conventions

Option 1: combine AI and operational context in Guance

Guance's Agent Monitoring connects Agent and LLM requests with application telemetry. The practical benefit is the ability to investigate an AI response together with the services that produced it: a retrieval delay, a failed tool call, or a database bottleneck may explain the symptom better than model latency alone.

The documented capabilities cover several parts of that investigation:

• Request and session inspection. Trace and session views show collected call relationships, durations, model activity, and tool activity. The integration must emit the identifiers and spans needed to join that context.
• Usage analysis. Input and output token consumption can be analyzed alongside model and application information. Check what the provider actually returns and how missing usage is represented.
• Quality evaluation. Prompt Eval and Schema Eval support different tests. Prompt Eval can evaluate an output against a task-specific objective; Schema Eval checks structural requirements. Manual annotation and datasets support review and batch evaluation.
• Risk context. Collected risk events can be investigated in session or trace context. The availability and meaning of those events depend on the integration and rules in use.
• Related operational data. Service traces, logs, and infrastructure data can provide the explanation for a slow or failed step when they have been collected and correlated correctly.

Use the Agent Monitoring guide to select a supported integration path. The application integration documentation distinguishes application connectors from framework integrations and explains input/output collection settings. For example, empty prompt content can be an intentional result of disabled content collection, rather than a failed trace export.

When reviewing historical requests, verify which fields are present rather than assuming every record contains a model version, temperature, prompt length, and output. Also test the export workflow you need for analysis or audit; available formats and payload fields should be part of acceptance testing.

Guance can fit a team whose AI workload is tightly coupled to existing services and whose operational team wants one place to investigate it. For a private deployment, document the boundary of the telemetry system and its external dependencies. Keeping telemetry storage private does not prevent an application or evaluator from sending content to an external inference API.

Option 2: optimize the AI development workflow

Langfuse, LangSmith, Braintrust, and Arize products focus heavily on AI traces, datasets, experiments, and evaluation. Their value is often clearest in the development loop: capture a failed run, turn it into a reproducible example, compare a change, and review the result.

For a team working extensively with LangChain or LangGraph, start by testing LangSmith's framework integration. For a team requiring self-hosting and flexible instrumentation, test Langfuse's supported deployment. Braintrust and Arize should be evaluated on the exact dataset, scoring, and review workflow the team needs. Phoenix is a self-hostable platform, not merely a scoring function library.

Avoid making the decision from an entry-level subscription price. Seats, retained traces, observations, payload volume, evaluation runs, and model inference can be separate billing dimensions. A unit named “trace” in one service is not necessarily comparable to an event or observation in another. Retention and enterprise controls can change the plan needed for the same workload. Use the current Langfuse, LangSmith, and Braintrust pricing definitions when constructing a comparison.

Self-hosting also has edition boundaries. Langfuse's core is available to self-host, while some add-on features require a license key. LangSmith self-hosting is an Enterprise add-on. Phoenix uses Elastic License 2.0, which restricts providing its substantial functionality as a hosted or managed service to third parties. These are different operating and licensing choices. Langfuse deployment, LangSmith self-hosting, and Phoenix license

If infrastructure monitoring remains in another platform, prove that engineers can move between the AI run and its service context. Shared trace identifiers and context propagation can support that link. Sending an alert through a webhook, by itself, does not create it.

This approach fits a team whose main bottleneck is debugging and improving AI behavior, particularly when experiment management and review are central to releases. Measure the integration work with the rest of the operational stack rather than assuming it is either automatic or impossible.

Option 3: extend the APM platform already in place

Datadog, New Relic, and Grafana offer AI-related capabilities within their broader observability products. For an existing customer, this route may reuse service ownership, collection, dashboards, access controls, and alert workflows.

Do not assume broad APM vendors lack session tracking or evaluation. Datadog documents session identifiers in its SDK and custom model-based evaluation workflows. The useful comparison is whether a product handles your actual dataset, annotation, experiment, CI, and production-evaluation requirements. Category labels such as “AI-native” and “APM extension” are too coarse to establish evaluation quality. Datadog instrumentation reference and custom evaluation workflow

The same-platform benefit also requires instrumentation. A tool call without propagated context cannot be joined reliably to a service trace merely because both land in the same vendor's backend.

Price the incremental AI workload against the actual contract. Check which seats, ingestion, retention, evaluation, and infrastructure features are required, rather than adding unrelated public starting prices. Verify the account's service region and payload controls as part of the same review.

This route is attractive when the organization already relies on the platform and its AI workflows meet the development team's requirements. A separate evaluation tool can still be appropriate if a specialized release process needs more than the operational platform provides.

Option 4: combine an AI gateway with evaluation tooling

Helicone, Portkey, and LiteLLM are examples of products used around the model-access layer. Depending on the product and configuration, a gateway can provide routing, fallback, caching, usage accounting, and request logging. DeepEval and RAGAS are examples of evaluation libraries; Phoenix and MLflow provide broader platform capabilities as well.

A compatible proxy endpoint can make initial collection straightforward, but changing a base URL is not universally sufficient. Validate authentication, supported provider APIs, streaming, retries, timeout behavior, and how the gateway forwards errors. Measure the gateway's latency on your deployment and request pattern rather than importing a generic overhead figure.

A proxy observes traffic that passes through it. It cannot infer an internal retrieval step, local tool execution, or task state that the application never reports. Some gateway products accept additional tracing instrumentation; evaluate that path separately instead of assuming a hard product-wide limit at the API boundary.

Evaluation libraries let a team define task-specific checks, but a production workflow still needs dataset versioning, scheduling, result storage, calibration, access control, and a way to connect a score to the run it evaluated. Running a scoring function once is different from operating that workflow continuously.

This combination fits a team that needs model routing or control and is prepared to assemble the surrounding observability and evaluation system. It can also complement an existing platform. A gateway is not limited to being a temporary migration step, and it is not automatically a replacement for application tracing.

Compare the work each option leaves with your team

These are procurement and proof-of-concept questions, not results from a common performance benchmark.

Use a small workload with known failures

Instrument a representative request that calls a model, retrieves context, and uses a downstream service. Then introduce known failures: a slow retrieval, a tool timeout, a malformed result, and a plausible but unsupported answer.

Check the trace first. Can an engineer distinguish time spent in the model from time spent in a tool or service? Does the trace retain retries and failed attempts? Are missing payloads explained by policy rather than mistaken for successful empty responses?

Next, evaluate known good and bad outputs. Compare the evaluator's decisions with human labels and inspect disagreements. Version the rubric and model used for judging. A judge that labels every response “good” is cheap to run but provides no useful release signal.

Finally, reconcile usage at the workload level. Track input, output, and any separately exposed cached or reasoning usage according to provider definitions. Model pricing varies, and reasoning usage may already be included in output billing. A single total-token number is inadequate for attribution, but summing overlapping categories is wrong too.

Record payload collection, redaction, retention, and access requirements before the test expands. For organizations operating across regions, follow the actual data flow—including model and evaluator calls—rather than treating the vendor's home country as a complete residency assessment.

Frequently asked questions

How does AI observability relate to APM?

APM helps explain the behavior of the application and its dependencies. AI observability adds model usage, task context, and output evaluation. An instrumented trace can connect those concerns, but it cannot prove an answer is correct without an appropriate evaluation.

How should we choose between Langfuse and LangSmith?

Test the development workflow first. LangChain or LangGraph integration may make LangSmith convenient; a supported self-hosted deployment may make Langfuse attractive. Then compare retention, access controls, deployment ownership, and equivalent usage. Neither framework choice nor a headline monthly price answers the whole question.

Can we trust LLM-as-a-judge scores?

Treat them as measurements with error, not ground truth. Calibrate against labeled examples, inspect disagreements, and combine them with deterministic checks. Whether a score belongs in a release gate depends on the task and validated failure behavior.

Can these platforms work with models developed in China?

A vendor-neutral telemetry format does not guarantee automatic SDK coverage. Test the provider, framework, streaming format, and usage fields you deploy. Assess data location and external inference calls separately from instrumentation compatibility.

Can an AI gateway replace the observability platform?

It may satisfy a narrow requirement for request accounting and model routing. A workload that needs internal task reconstruction, downstream troubleshooting, and repeatable quality evaluation needs those capabilities somewhere as well. Determine whether the gateway supplies them, integrates with them, or leaves them for your team to build.

Choose the approach that covers your failure modes with an acceptable ongoing workload. For some teams that means a shared platform such as Guance; for others it means a specialized evaluation system or a deliberately assembled stack.