Last reviewed . Product packaging and pricing can change; verify the linked vendor sources before buying.
Try OpenLIT alongside Telemetry
Choose one OpenLIT workflow and test both systems with the same fixed dataset. Check which capabilities you still need before changing production monitoring.
- 1
Inventory OpenLIT
OpenLIT OpenTelemetry traces, metrics, logs, and automatic instrumentation
- 2
Map one workflow
Application-owned JSON outcome events; retain OpenLIT or another OTLP backend for automatic collection and trace waterfalls.
- 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
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 to collect AI traces, metrics, and logs automatically or through manual instrumentation. Telemetry receives JSON events from your application.
- OpenLIT includes AI tracing, evaluation, prompt, experiment, dashboard, and collector workflows that Telemetry does not replace.
- Telemetry stores completed task results in named tables so you can query them alongside other product events.
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
- An HTTP API and SDKs for sending application outcomes with defined fields and a clear meaning for each event.
- DataFusion SQL joins between AI behavior, product usage, billing, APIs, jobs, databases, and customer context.
- Send the events you need without adopting OTLP or instrumenting every AI component.
Evaluation checklist
Test the decision with a real workflow
- 1Decide whether auto-instrumentation, OTLP export, collectors, prompts, evaluations, experiments, GPU signals, and self-hosting are requirements.
- 2Test one agent flow with content capture disabled or reviewed, then compare trace evidence, aggregate SQL, correlation, and deletion behavior.
- 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
List the queries, alerts, exports, and retention you use today. Define the event fields they need and translate one query. Run both systems with the same test data. Check null handling, timestamps, and aggregates before moving more queries.
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 one workflow
Start with one backend workflow
Pick an API route, AI workflow, webhook, or job queue. Send structured events and query them before expanding coverage.
Category buying guide
Compare AI observability tools for your workflow
Compare AI observability approaches for traces, prompts, evaluations, model cost, tool reliability, SQL analysis, and product outcomes.
Read the comparison guideMore comparisons
PostHog for backend events
PostHog combines product analytics, funnels, retention, SQL, and a data warehouse. Telemetry focuses on backend event tables and SQL dashboards. A coding agent can add the instrumentation from your codebase.
Read comparisonDatadog alternative for startups
Datadog covers observability and security across your infrastructure. Telemetry hosts structured application events, SQL dashboards, and threshold alerts for teams that need to query their own workflows.
Read comparisonClickHouse logging API without running ClickHouse
ClickHouse is a columnar analytics database, and ClickStack adds observability tools. Telemetry hosts event ingestion, SQL queries, dashboards, and alerts so you do not have to assemble or operate that stack.
Read comparisonAxiom alternative for structured event analytics
Axiom offers event ingestion, search, APL queries, dashboards, and monitors. Telemetry uses typed application event tables and SQL. Its coding-agent prompts help you add instrumentation and build queries from your codebase.
Read comparisonBetter Stack Logs alternative for SQL event analytics
Better Stack combines logs, dashboards, alerting, incident management, and uptime monitoring. Telemetry focuses on application event tables and SQL queries, with prompts that help a coding agent add instrumentation to your codebase.
Read comparisonHoneycomb alternative for lightweight wide events
Honeycomb supports high-cardinality debugging with wide events and distributed traces. Telemetry stores application and business events in SQL tables, with dashboards and threshold alerts.
Read comparisonGrafana Loki alternative for structured log SQL
Grafana Cloud and Loki combine logs, metrics, traces, dashboards, and alerts. Telemetry hosts JSON event tables and SQL for teams that need to analyze application workflows.
Read comparisonSentry alternative for structured events and SQL
Sentry combines error monitoring, tracing, profiling, session replay, and logs around application health. Telemetry is the narrower alternative when a team's first requirement is custom structured workflow events and SQL analysis rather than exception-centric debugging.
Read comparisonSplunk alternative for structured events
Splunk offers search, security analytics, logs, infrastructure monitoring, APM, real-user monitoring, and OpenTelemetry collection. Telemetry focuses on application events that you choose to send and query with SQL.
Read comparisonElastic alternative for structured event SQL
Elastic Observability combines Elasticsearch, Kibana, logs, metrics, APM, profiling, and OpenTelemetry collection. Telemetry is the focused alternative when the main job is managed application-event ingestion and SQL analysis without operating or modeling a broader Elastic deployment.
Read comparisonNew Relic alternative for structured events
New Relic is a broad observability platform spanning APM, infrastructure, logs, browser, mobile, synthetics, errors, and NRQL. Telemetry is the narrower choice when a team wants custom structured outcomes, SQL, and a lightweight event-analysis workflow.
Read comparisonTelemetry vs Langfuse for AI observability
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 comparisonTelemetry vs LangSmith for AI observability
LangSmith 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 comparisonTelemetry vs Arize Phoenix
Arize 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 comparisonTelemetry vs Pydantic Logfire
Pydantic 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 comparisonTelemetry vs Mixpanel
Mixpanel is a product and digital analytics platform built around behavioral reports such as insights, funnels, flows, retention, and cohorts. Telemetry is the narrower choice for SQL over application-owned product and operational events.
Read comparisonTelemetry vs Amplitude
Amplitude is a digital analytics platform with product-analysis workflows for events, funnels, retention, journeys, cohorts, and experimentation. Telemetry focuses on compact structured events and explicit SQL.
Read comparisonTelemetry vs Braintrust
Braintrust 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 comparisonTelemetry vs Helicone
Helicone 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 comparisonTelemetry vs Opik
Opik 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 comparisonTelemetry vs W&B Weave
W&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 comparisonTelemetry vs MLflow for GenAI
MLflow 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 comparison