Last reviewed . Product packaging and pricing can change; verify the linked vendor sources before buying.
Try Grafana Cloud Logs and Loki alongside Telemetry
Choose one Grafana Cloud Logs and Loki workflow and test both systems with the same fixed dataset. Check which capabilities you still need before changing production monitoring.
- 1
Inventory Grafana Cloud Logs and Loki
Loki streams, labels, and parsed fields
- 2
Map one workflow
Event tables with low-cardinality dimensions as columns and high-cardinality identifiers kept out of partition choices.
- 3
Dual-run the fixture
Compare label filters, parsed-field types, dropped lines, and out-of-order records.
- 4
Record the decision
Inventory existing metrics, traces, logs, and Grafana dashboards; an established Grafana stack can outweigh the benefit of a narrower tool.
How Telemetry is different
- Telemetry uses typed event tables and DataFusion SQL. Loki uses indexed labels, structured metadata, and LogQL for log queries.
- Telemetry stores events that describe application and business results.
- Grafana queries and charts data from many sources. Telemetry charts the event tables you send to it.
When Telemetry is a good fit
- You need structured application-event analytics more than a general-purpose log aggregation layer.
- SQL is the preferred interface for joins, cohorts, funnels, revenue movement, or other multi-step analysis.
- You want a compact managed product and do not need Grafana's broader data-source ecosystem.
Where each product is strongest
Grafana Cloud Logs and Loki
- A broad ecosystem for logs, metrics, traces, profiles, dashboards, alerting, and many data sources.
- Loki's label and structured-metadata model supports scalable log workflows without indexing every field.
- A stronger fit when Grafana is already the team's shared observability interface or cross-signal correlation is required.
Telemetry
- Automatic typed tables for purpose-built JSON events and familiar SQL analysis.
- A managed path that does not require designing label cardinality, a Loki deployment, or a multi-source Grafana stack.
- Detailed recipes for business and product questions that are outside a traditional infrastructure-log starting point.
Evaluation checklist
Test the decision with a real workflow
- 1Inventory existing metrics, traces, logs, and Grafana dashboards; an established Grafana stack can outweigh the benefit of a narrower tool.
- 2Send one event stream, choose safe labels or dimensions, and compare LogQL and SQL for the operational and business questions you actually ask.
- 3Price ingestion, retention, active-series or signal requirements, hosting, and operating ownership using current documentation.
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.
Grafana Cloud Logs and Loki workflow
Loki streams, labels, and parsed fields
Telemetry mapping
Event tables with low-cardinality dimensions as columns and high-cardinality identifiers kept out of partition choices.
Dual-run validation
Compare label filters, parsed-field types, dropped lines, and out-of-order records.
Grafana Cloud Logs and Loki workflow
LogQL filters, aggregations, and recording rules
Telemetry mapping
DataFusion SQL using WHERE, conditional aggregates, time buckets, and saved queries.
Dual-run validation
Use the migration guide and compare the exact rows and aggregates for a shared UTC fixture.
Grafana Cloud Logs and Loki workflow
Grafana dashboards and the broader metrics/traces stack
Telemetry mapping
Focused SQL dashboards for migrated event questions; no automatic replacement for Prometheus or Tempo.
Dual-run validation
Inventory every panel data source and alert dependency before changing the Grafana stack.
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 structured logging and event analytics tools
Compare log platforms, wide-event systems, error monitoring, data infrastructure, and SQL event analytics using one production workflow.
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.
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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 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.
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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.
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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.
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