Compare AI observability approaches for traces, prompts, evaluations, model cost, tool reliability, SQL analysis, and product outcomes.
Reviewed by the Telemetry product team on . We checked the comparison sources, test criteria, workload assumptions, and which tools to keep when migrating. Who reviews this page
Test the tools on work you actually do
Before testing tools, decide what data to collect and exclude, who will query it, and how they will respond to problems. Specify retention and expected volume.
Evaluation criteria
Does the team investigate spans, prompt traces, evaluation datasets, typed product outcomes, or several of these?
Check what each record contains. That determines which questions you can answer without exporting data to another tool.
Where are reviewed labels, test datasets, experiments, and production acceptance outcomes stored?
Offline evaluation and production product analytics have different owners, privacy boundaries, and release workflows.
Can model usage be connected to feature, account, tool path, retry behavior, and an accepted outcome?
Token totals alone do not show whether an AI workflow creates product value or margin.
Does incident response require full traces, infrastructure context, aggregate SQL, or a handoff between them?
Use traces to investigate execution steps. Use completion events to count results such as paid orders or resolved tasks.
Are prompts and completions retained, redacted, sampled, or excluded before collection?
The most convenient debugging payload can also become the highest-risk stored data.
Who owns hosting, upgrades, retention, access, sampling, and the cost model?
Test the workload you expect to run, including its operating cost.
Start from the job
Goal
Prompt traces and AI-specific evaluation workflows
Evaluate first
Langfuse, LangSmith, or Arize Phoenix
Why
Start with the specialist whose trace and evaluation model matches the framework, dataset, and review workflow, then test the exact production path.
Goal
Python application telemetry beside AI behavior
Evaluate first
Pydantic Logfire and the existing application stack
Why
Test whether one Python-oriented observability workflow provides the application and AI context the team needs.
Goal
SQL for product and agent results
Evaluate first
Telemetry
Why
Use typed events to join agent reliability and cost with accounts, feature usage, billing, and retention.
Goal
Detailed traces and product results
Evaluate first
A specialist trace system together with Telemetry
Why
Correlate the systems with an approved run or trace identifier instead of forcing one event model to replace the other.
Market map · reviewed 2026-07-30
Categories overlap, and tools change. Check the vendor documentation linked below. Test hosted and self-managed versions with the same data, after reviewing it for sensitive fields.
Primary focus
Trace model and tool calls, inspect individual runs, and evaluate outputs in the same platform.
Boundary to test
Test how approved trace identifiers connect to product acceptance, account, billing, and retention outcomes.
Primary focus
Manage datasets, scorers, experiments, regression checks, and human or model-assisted review.
Boundary to test
Verify how an offline score, evaluated coverage, and experiment version connect to the released product outcome.
Primary focus
Inspect model requests, provider latency, usage, caching, retries, and gateway-level cost controls.
Boundary to test
Check whether request analytics can record the agent or customer result that becomes known after the model call.
Primary focus
Use OpenTelemetry conventions and collectors for model, agent, application, and infrastructure signals.
Boundary to test
Decide which details stay in traces and which final results you need to record as events for SQL analysis.
Primary focus
Query agent outcomes alongside feature usage, account activity, reliability, billing, and retention.
Boundary to test
Keep a specialist trace or evaluation platform when responders need step-level execution, datasets, judges, or prompt debugging.
Telemetry analyzes structured events. Keep your existing tools where you still need their logs, traces, metrics, evaluations, error diagnostics, or infrastructure monitoring.
Reproducible test
Sourced deep dives
Langfuse provides LLM traces, prompt management, evaluations, datasets, and experiments. Telemetry stores events for agent runs and product activity so you can query their cost, reliability, and results with SQL.
Read comparisonLangSmith provides tracing, evaluations, datasets, experiments, and deployment options for LLM applications. Telemetry stores the final results of AI and application tasks in event tables you can query with SQL.
Read comparisonArize Phoenix is an open-source AI observability and evaluation platform built around traces, prompts, datasets, and experiments. Telemetry focuses on compact structured outcomes and SQL.
Read comparisonPydantic Logfire combines OpenTelemetry-based application monitoring with AI tracing and conversation views. Telemetry stores selected application outcomes in tables you can query with SQL.
Read comparisonBraintrust is an AI evaluation and observability platform built around experiments, datasets, scorers, prompts, and production traces. Telemetry focuses on SQL over selected AI and product outcomes.
Read comparisonHelicone combines an AI gateway with LLM request observability, sessions, cost analytics, caching, and alerts. Telemetry is a provider-neutral SQL layer for selected AI and application outcomes.
Read comparisonOpik is an open-source LLM evaluation and observability platform with traces, datasets, metrics, experiments, and test suites. Telemetry uses SQL to analyze selected AI and application results.
Read comparisonW&B Weave is an AI observability and evaluation platform with traces, datasets, scorers, versioning, feedback, and production monitoring. Telemetry focuses on SQL over selected AI and product outcomes.
Read comparisonMLflow provides OpenTelemetry-compatible GenAI tracing, evaluations, prompt versioning, experiments, and production monitoring. Telemetry stores selected task results in event tables that you can join with other application data using SQL.
Read comparisonOpenLIT is an open-source, OpenTelemetry-native AI engineering platform with auto-instrumentation, traces, evaluations, prompts, experiments, dashboards, and collectors. Telemetry focuses on SQL outcome events.
Read comparisonUse the free plan and synthetic fixtures to compare ingestion, SQL, dashboards, exports, and alerts before moving production data.