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Telemetry
Inspectable dashboard gallery

SaaS dashboard examples that show their evidence

Choose the decision you need to make, then inspect the event grain, metrics, complete query, synthetic result, assumptions, and interpretation boundary. Each example has a dedicated canonical page you can review or share.

Reviewed by the Telemetry product team on . Dashboard grain, metric definitions, DataFusion syntax, synthetic results, freshness, and interpretation boundaries. Review standards and ownership

6

complete dashboard patterns

24

named metric definitions

100%

SQL visible before signup

Six decisions, six contracts

The previews below are synthetic and demonstrate structure, not Telemetry customer performance or a universal benchmark.

Founders, product leaders, and engineering leadersDaily, after the newest complete UTC day

SaaS health overview

Put adoption, reliability, and commercial context on one review surface without pretending they share the same grain.

Did account adoption, application reliability, or represented revenue change enough to require a deeper review?

  • Active accounts
  • Successful milestones
  • API error rate
  • Monthly revenue represented
Service owners and on-call engineersFive-minute buckets for operations; daily for reviews

API reliability and latency

Compare traffic, error rate, and tail latency by endpoint while retaining enough volume to judge whether a percentile is stable.

Which sufficiently busy endpoint has the broadest error or tail-latency regression?

  • Request volume
  • Error rate
  • p50 latency
  • p95 latency
Incident commanders and customer-facing teamsDuring an incident and once after resolution

Incident customer impact

Translate a declared service incident into affected accounts, failed operations, regions, and represented revenue.

Which accounts and operations were directly represented in a declared incident window?

  • Affected accounts
  • Failed operations
  • Affected regions
  • Represented monthly revenue
AI product, platform, and finance teamsDaily with a complete provider-cost window

LLM usage and unit economics

Connect model calls to product workflows and accounts so token volume, latency, failures, and cost can be reviewed together.

Which model-backed workflow consumes the most estimated cost per successful outcome?

  • Requests
  • Input and output tokens
  • Estimated cost
  • Cost per successful request
Async platform and application teamsHourly for operations; weekly for capacity planning

Background job reliability

Separate terminal job outcomes from attempts, then compare retries, queue wait, and duration by stable job name.

Which logical job types fail permanently, retry, wait, or run slowly?

  • Completed jobs
  • Terminal failure rate
  • Retry rate
  • p95 duration
Product managers and growth engineersDaily, segmented by signup cohort

Account activation funnel

Measure durable account milestones in order and make the denominator explicit at every funnel step.

Where do newly signed-up accounts stop reaching durable activation milestones?

  • Signed-up accounts
  • Sources connected
  • First queries run
  • Dashboards created

Run the same analysis on a connected sample database

The SQL Lab includes product, API, model, job, billing, release, and incident data with more than 10,000 deterministic rows. Download it or query it locally before mapping these dashboards to production fields.