Telemetry
Comparison

Telemetry vs OpenLIT

OpenLIT 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.

Reviewed by the Telemetry product team on . Product positioning, primary vendor sources, and evaluation guidance. Review standards and ownership

Last reviewed . Product packaging and pricing can change; verify the linked vendor sources before buying.

Evaluation evidence

OpenLIT to Telemetry: a reversible evaluation path

Map a bounded OpenLIT workflow, preserve the capabilities that remain necessary, and compare both systems over the same closed fixture before changing production coverage.

  1. 1

    Inventory OpenLIT

    OpenLIT OpenTelemetry traces, metrics, logs, and automatic instrumentation

  2. 2

    Map one workflow

    Application-owned JSON outcome events; retain OpenLIT or another OTLP backend for automatic collection and trace waterfalls.

  3. 3

    Dual-run the fixture

    Run one agent workflow with reviewed content-capture settings and compare spans, missing data, latency, cost, and terminal outcomes.

  4. 4

    Record the decision

    Decide whether auto-instrumentation, OTLP export, collectors, prompts, evaluations, experiments, GPU signals, and self-hosting are requirements.

How Telemetry is different

  • OpenLIT uses OpenTelemetry-native automatic or manual instrumentation for AI traces, metrics, and logs; Telemetry accepts application-owned JSON events.
  • OpenLIT includes AI tracing, evaluation, prompt, experiment, dashboard, and collector workflows that Telemetry does not replace.
  • Telemetry centers named terminal-outcome tables and cross-product SQL rather than automatic collection of a full AI execution path.

When Telemetry is a good fit

  • The selected workflow needs aggregate, auditable outcome SQL rather than automatic distributed tracing.
  • An existing OpenTelemetry stack retains detailed signals and only a safe correlation identifier crosses into outcome events.
  • OpenLIT can remain the trace and evaluation platform while Telemetry stores compact business and product outcomes.

Where each product is strongest

OpenLIT

  • OpenTelemetry-native automatic instrumentation across supported model providers, agent frameworks, vector databases, and application components.
  • Distributed trace inspection plus AI-specific token, latency, cost, evaluation, prompt, experiment, and dashboard workflows.
  • A stronger fit when self-hosting, OTLP routing, broad AI instrumentation, or an OpenTelemetry destination strategy is central.

Telemetry

  • A small HTTP and SDK surface for explicit application outcomes with reviewed fields and event grains.
  • DataFusion SQL joins between AI behavior, product usage, billing, APIs, jobs, databases, and customer context.
  • No requirement to adopt OTLP or automatically instrument every supported AI component for the selected outcome questions.

Evaluation checklist

Test the decision with a real workflow

  1. 1Decide whether auto-instrumentation, OTLP export, collectors, prompts, evaluations, experiments, GPU signals, and self-hosting are requirements.
  2. 2Test one agent flow with content capture disabled or reviewed, then compare trace evidence, aggregate SQL, correlation, and deletion behavior.
  3. 3Verify instrumentation defaults, deployment operations, retention, destinations, access, export, and current hosted packaging on the expected volume.

Migration path

Plan the query and event migration before changing tools

Inventory the queries, alerts, exports, and retention requirements the current workflow actually uses. Map those requirements to a typed event contract, translate a representative query, and dual-run the same fixture before expanding coverage. Similar operators do not guarantee equivalent null handling, time semantics, or aggregation results.

OpenLIT workflow

OpenLIT OpenTelemetry traces, metrics, logs, and automatic instrumentation

Telemetry mapping

Application-owned JSON outcome events; retain OpenLIT or another OTLP backend for automatic collection and trace waterfalls.

Dual-run validation

Run one agent workflow with reviewed content-capture settings and compare spans, missing data, latency, cost, and terminal outcomes.

OpenLIT workflow

OpenLIT evaluations, prompts, experiments, and AI dashboards

Telemetry mapping

Versioned evaluation outcomes and reviewed SQL; no built-in prompt, experiment, trace, or evaluator-execution equivalent.

Dual-run validation

Evaluate the same frozen examples and compare scorer versions, thresholds, coverage, failure review, and retained content.

OpenLIT workflow

OpenLIT collectors, destinations, and self-hosted platform

Telemetry mapping

A hosted ingestion and DataFusion SQL layer for selected event contracts, not a general OTLP collector or destination.

Dual-run validation

Inventory every exporter, collector, signal, dashboard, access rule, and retention dependency before changing the OpenTelemetry path.

Try the wedge

Start with one backend workflow

Pick an API route, AI workflow, webhook, or job queue. Send structured events and query them before expanding coverage.

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Category buying guide

AI Observability Tools: A Workflow-Based Comparison

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Review the full evaluation framework

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