Last reviewed . Product packaging and pricing can change; verify the linked vendor sources before buying.
Try Braintrust alongside Telemetry
Choose one Braintrust workflow and test both systems with the same fixed dataset. Check which capabilities you still need before changing production monitoring.
- 1
Inventory Braintrust
Braintrust traces and production logs
- 2
Map one workflow
Compact run, model, tool, evaluation, and product-outcome events with a safe correlation identifier; no detailed trace viewer.
- 3
Dual-run the fixture
Exercise one successful run, tool retry, terminal failure, and human review while confirming sensitive trace content is excluded.
- 4
Record the decision
Decide whether datasets, experiments, scorers, prompt management, trace inspection, and human review queues are required.
How Telemetry is different
- Braintrust has tools for evaluations, experiments, datasets, prompts, and traces. Telemetry queries structured events with SQL.
- Braintrust is designed for inspecting and scoring detailed AI executions; Telemetry can avoid storing prompts and completions by keeping only approved outcomes.
- Telemetry uses the same event model for AI cost and quality plus product, billing, API, job, webhook, and reliability signals.
When Telemetry is a good fit
- You need to measure cost, reliability, adoption, or accepted results across runs.
- Keep evaluations in Braintrust and query product and business events in Telemetry.
- Your data policy allows identifiers and scores but excludes prompts and completions.
Where each product is strongest
Braintrust
- Purpose-built datasets, experiments, scorers, prompt management, and production trace inspection.
- A direct workflow from observed AI failures to evaluated changes and regression testing.
- A stronger fit when your team needs to develop AI features by inspecting traces and running evaluations.
Telemetry
- SQL joins between model usage, reviewed outcomes, accounts, releases, and non-AI application workflows.
- Send selected event fields without copying prompts, outputs, retrieved content, or every span.
- Query AI activity and other product events in shared dashboards and alerts.
Evaluation checklist
Test the decision with a real workflow
- 1Decide whether datasets, experiments, scorers, prompt management, trace inspection, and human review queues are required.
- 2Run one production failure through trace investigation and one release through aggregate outcome comparison.
- 3Verify data capture, redaction, access, retention, export, deployment, and current pricing for the expected trace 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.
Braintrust workflow
Braintrust traces and production logs
Telemetry mapping
Compact run, model, tool, evaluation, and product-outcome events with a safe correlation identifier; no detailed trace viewer.
Dual-run validation
Exercise one successful run, tool retry, terminal failure, and human review while confirming sensitive trace content is excluded.
Braintrust workflow
Braintrust datasets, experiments, scorers, and prompts
Telemetry mapping
Versioned evaluation outcome events for aggregate SQL; retain Braintrust for dataset curation, scorer execution, and experiment review.
Dual-run validation
Run a frozen candidate and baseline with identical examples, scorers, thresholds, prompt versions, and coverage rules.
Braintrust workflow
Braintrust aggregate monitoring and evaluation analysis
Telemetry mapping
SQL dashboards joining AI reliability, cost, reviewed quality, release, account, and downstream product outcomes.
Dual-run validation
Reconcile run count, evaluated coverage, pass rate, cost, latency, and accepted outcomes over one closed 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 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 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