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CI/CD with GitHub Actions: tests, deploys and credentials without permanent keys

Ship every commit with tests and no long-lived keys in GitHub

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

The data, today

The context

A good pipeline makes the scariest thing boring: shipping. Every push runs the tests, builds an artifact identified by the commit and deploys it the same way every time. When that breaks, the problem is rarely the tool; it is usually a difference between your machine and the build environment: a Node version, variables that only exist in your terminal, or dependencies you installed by hand.

The other delicate point is credentials. Storing a long-lived AWS key in GitHub secrets works, but if it leaks it stays open until someone rotates it. The alternative is OIDC: GitHub requests temporary credentials from AWS on every run, scoped to one repository and one branch. This page gathers Codifly's guides on that path and the catalog data to choose where to deploy.

What to decide

  1. 1

    What should the pipeline do on every push?

    Install from the lockfile, run types, lint and tests, and build an artifact tagged with the commit hash. If anything fails, nothing is deployed. What the pipeline doesn't test, nobody tests.

  2. 2

    How do I authenticate to AWS without storing keys?

    With OIDC: you create a GitHub identity provider in AWS and a role whose trust policy limits the repository and branch. The workflow assumes that role and receives credentials that expire in minutes. The OIDC guide walks through it step by step.

  3. 3

    Why does my build work locally and fail on deploy?

    Almost always an environment difference: an unpinned Node or package-manager version, an environment variable missing on the server, file-path casing between macOS and Linux, or dev dependencies the build needs. Reproduce the build in a clean container before blaming the provider.

Common mistakes

  • Storing long-lived AWS keys in repository secrets when OIDC is available.
  • Rebuilding the app for production instead of promoting the artifact you already tested in staging.
  • Leaving Node and package-manager versions unpinned between your machine and the pipeline.

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 leadersSecrets and environmentsSeparate dev, staging and production and stop leaking environment variablesObservability without surprise billsMeasure what matters with OpenTelemetry and SLOs that don't page anyone for nothingQueues 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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