Last reviewed . Product packaging and pricing can change; verify the linked vendor sources before buying.
Try Langfuse alongside Telemetry
Choose one Langfuse workflow and test both systems with the same fixed dataset. Check which capabilities you still need before changing production monitoring.
- 1
Inventory Langfuse
Langfuse traces, generations, spans, and observations
- 2
Map one workflow
No direct equivalent; keep Langfuse for hierarchical LLM traces and emit only selected run, request, tool, or outcome events to Telemetry.
- 3
Dual-run the fixture
Correlate one synthetic run by an approved run or trace ID and confirm responders can still reach the detailed evidence.
- 4
Record the decision
Decide whether responders need stored prompt and completion content, hierarchical trace inspection, prompt management, datasets, or experiment execution.
How Telemetry is different
- Langfuse records model and tool steps in LLM traces. Telemetry stores events your application sends when an operation finishes.
- Langfuse includes prompt, dataset, experiment, and evaluation workflows that Telemetry does not provide.
- Use Telemetry to record final results with selected fields, then join them with cost, release, account, and reliability data using SQL.
When Telemetry is a good fit
- The primary requirement is SQL analysis of final agent outcomes, tool reliability, cost, handoffs, releases, and customer impact.
- Detailed LLM traces already live elsewhere or are not required for the selected workflow.
- You want to keep your trace or evaluation tool and query selected outcomes alongside other product events.
Where each product is strongest
Langfuse
- LLM traces that preserve the execution hierarchy across model, tool, retriever, and agent observations.
- Prompt management, datasets, experiments, scores, human annotation, and model-based evaluation in one LLM engineering workflow.
- A stronger fit when teams need to inspect prompts and outputs, replay detailed runs, or operate a dedicated evaluation lifecycle.
Telemetry
- Named structured-event tables and DataFusion SQL for aggregate agent, product, operational, and business questions.
- Query categories, versions, costs, and results without sending prompts or completions to Telemetry.
- The same event model, dashboards, and alerts can cover AI workflows, APIs, jobs, webhooks, billing, and activation.
Evaluation checklist
Test the decision with a real workflow
- 1Decide whether responders need stored prompt and completion content, hierarchical trace inspection, prompt management, datasets, or experiment execution.
- 2Instrument the same agent run in both products and compare trace detail, privacy boundaries, evaluation workflow, aggregate query clarity, and operational ownership.
- 3Model current cloud or self-hosting requirements, ingestion, retention, seats, evaluation volume, and the cost of operating each retained system.
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.
Langfuse workflow
Langfuse traces, generations, spans, and observations
Telemetry mapping
No direct equivalent; keep Langfuse for hierarchical LLM traces and emit only selected run, request, tool, or outcome events to Telemetry.
Dual-run validation
Correlate one synthetic run by an approved run or trace ID and confirm responders can still reach the detailed evidence.
Langfuse workflow
Langfuse prompts, datasets, experiments, and evaluation scores
Telemetry mapping
Versioned prompt, dataset, evaluator, and score fields on compact evaluation events; no built-in prompt, dataset, scorer, or experiment workflow.
Dual-run validation
Run a frozen evaluation before and after the change and compare score grain, coverage, thresholds, and failed-example review.
Langfuse workflow
Langfuse metrics, dashboards, and aggregate cost analysis
Telemetry mapping
DataFusion SQL, dashboards, and threshold alerts over application-owned model, agent, evaluation, and product-outcome tables.
Dual-run validation
Dual-run request count, token use, estimated cost, run success, and evaluated acceptance over the same closed UTC interval.
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 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 comparisonTelemetry 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.
Read comparison