Last reviewed . Product packaging and pricing can change; verify the linked vendor sources before buying.
Try Arize Phoenix alongside Telemetry
Choose one Arize Phoenix workflow and test both systems with the same fixed dataset. Check which capabilities you still need before changing production monitoring.
- 1
Inventory Arize Phoenix
Phoenix OpenTelemetry and OpenInference traces
- 2
Map one workflow
No direct equivalent because Telemetry has no OTLP endpoint; retain Phoenix or another trace backend and correlate selected outcomes.
- 3
Dual-run the fixture
Verify that the trace backend receives the full test trace while Telemetry receives only the approved terminal fields and trace ID.
- 4
Record the decision
Decide whether OpenInference traces, prompt inspection, datasets, evaluators, experiments, or a self-hosted AI workspace are requirements.
How Telemetry is different
- Phoenix provides an AI-focused trace and evaluation workspace; Telemetry provides a general structured-event and SQL workspace.
- Phoenix uses OpenTelemetry and OpenInference instrumentation for trace-oriented AI evidence; Telemetry does not ingest OTLP.
- Telemetry stores selected completion events with application, customer, release, and business fields.
When Telemetry is a good fit
- The selected problem is aggregate outcome monitoring and cross-product analysis rather than detailed LLM trace inspection.
- An OTLP trace backend already exists and only a safe correlation identifier should cross into the outcome table.
- The team wants to retain Phoenix for traces and evaluations while using Telemetry for SQL dashboards and alerts.
Where each product is strongest
Arize Phoenix
- Open-source tracing for LLM, agent, retrieval, and tool workflows using OpenTelemetry and OpenInference.
- Evaluation, prompt, dataset, and experiment workflows for inspecting and improving AI applications.
- A stronger fit when a team needs self-hosted trace inspection, detailed LLM records, or tools for AI experiments.
Telemetry
- Direct DataFusion SQL over named agent, tool, request, evaluation, and product-outcome tables.
- Compact events can avoid copying prompts, completions, retrieved documents, and unrestricted tool payloads.
- One event-analysis layer for AI behavior plus API, job, webhook, billing, reliability, and product signals.
Evaluation checklist
Test the decision with a real workflow
- 1Decide whether OpenInference traces, prompt inspection, datasets, evaluators, experiments, or a self-hosted AI workspace are requirements.
- 2Test one agent failure and one release comparison in both systems, including the handoff between aggregate outcome and detailed trace.
- 3Compare deployment operations, storage, retention, access controls, instrumentation ownership, and current hosted-service packaging.
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.
Arize Phoenix workflow
Phoenix OpenTelemetry and OpenInference traces
Telemetry mapping
No direct equivalent because Telemetry has no OTLP endpoint; retain Phoenix or another trace backend and correlate selected outcomes.
Dual-run validation
Verify that the trace backend receives the full test trace while Telemetry receives only the approved terminal fields and trace ID.
Arize Phoenix workflow
Phoenix prompts, datasets, evaluators, and experiments
Telemetry mapping
Versioned evaluation outcome events for aggregate SQL; keep Phoenix for content inspection, dataset management, evaluator execution, and experiments.
Dual-run validation
Compare one evaluation run end to end, including coverage, version identifiers, score thresholds, and failed-example inspection.
Arize Phoenix workflow
Phoenix aggregate AI observability analysis
Telemetry mapping
Named event tables, DataFusion SQL, dashboards, and alerts for agent reliability, cost, quality, handoffs, and downstream outcomes.
Dual-run validation
Compare model requests, run outcomes, evaluated pass rate, p95 duration, and cost per accepted operation over a fixed 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 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