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Observability: OpenTelemetry, SLOs and how to cut your monitoring bill

Measure what matters with OpenTelemetry and SLOs that don't page anyone for nothing

Data reviewed on Oct 29, 2026 · LLM Stats indexes and official prices

The data, today

The context

Observability costs you twice: money and attention. A monitoring bill that grows faster than traffic usually comes from unfiltered logs, metrics with too many distinct labels and traces that are all stored. And poorly defined alerts cost attention: if everything rings, nothing gets answered.

OpenTelemetry separates instrumentation from the vendor: you instrument once and decide where the data goes, which makes it possible to switch tools or trim volume along the way. Start with a few SLOs tied to what the user sees and let them decide which alerts are worth having.

What to decide

  1. 1

    What do I measure first?

    Availability and latency of the routes your customer uses, measured from the user's point of view. Define a clear SLI, a realistic target and alert when the error budget is being consumed, not on every CPU spike.

  2. 2

    How do I cut the bill without going blind?

    Filter and sample in the collector before sending: drop debug logs in production, limit high-cardinality labels on metrics and sample traces, keeping the slow or failed ones. Measure what each source costs before deciding what to trim.

  3. 3

    Why are my alerts false?

    Almost always poorly defined SLOs: arbitrary thresholds, windows that are too short or metrics that don't reflect the user. The SLI and SLO guide explains how to define them so an alert means a concrete action.

Common mistakes

  • Sending every log to the paid tool without filtering or sampling.
  • Alerting on every infrastructure metric instead of on user experience.
  • Labeling metrics with unique identifiers such as user or request IDs.

Tools and comparators

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Guides to go deeper

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No provider pays for its position. Indexes come from LLM Stats; prices from each provider's standard API. How we measure

Reduce AI costsMeasure tokens per request and pay for big models only when neededAI customer supportWork out cost per conversation without dropping below your quality barDocument search and RAGFind the right passage before asking the model for an answerCoding with AIMeasure cost per coding task and review what the agent writesCut cloud costsFind the hidden spend in egress, operations and idle resourcesHandle traffic spikesAbsorb traffic bursts without dropping requests or the databaseShip to productionDeploy from Git with separate environments and clean configurationChoose a databaseCompare Supabase, Neon, Turso, PlanetScale and D1 by how they billLaunch an MVPDeploy and store data at minimum cost and without surprisesEvaluate new AI providersCompare Kimi, GLM, Qwen and Muse Spark with the leadersCI/CD from GitHub to productionShip every commit with tests and no long-lived keys in GitHubSecrets and environmentsSeparate dev, staging and production and stop leaking environment variablesQueues and background jobsProcess heavy tasks without duplicates when something retriesSecure file uploadsReceive files without routing them through your server or blowing up storage costsInfrastructure as codeDecide between Terraform and OpenTofu and keep your infrastructure in GitFast PostgreSQLFind the slow query with EXPLAIN and fix it with the right indexKubernetes or PaaS?Choose where to run your app based on your team, not on fashion
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