Sacred

At a glance

• Editor score
  6.3 / 10
• Pricing
  Open source
• Best for
  Self-hosted researchers needing reproducible configs
• Paid from
  Free
• Run comparison
  No
• Facts checked
  21 Sep 2026

• Where it wins

  • Automatic capture of source, dependencies, Git, and host information
  • Tracks metrics, resources, files, and model-output artifacts
  • Supports MongoDB, SQL, S3, Google Cloud Storage, and local storage

• Where it doesn't

  • Self-hosted deployment requires managing the operating environment
  • No verified run-comparison feature
  • Primarily suited to Python-based experiment workflows

Our verdict on Sacred

Sacred is an open-source Python tool for configuring, organizing, logging, and reproducing computational and machine-learning experiments. It is aimed at researchers and developers who need controlled experiment execution, automatic metadata capture, and reproducible configurations rather than a hosted experiment-management service. Configuration scopes, automatic injection, command-line execution, parameter overrides, and run variants support repeatable workflows. Its API SDKs also provide a way to integrate experiment handling into Python-based projects.

Reproducibility is Sacred’s clearest strength. It can capture experiment configurations, parameters, source files, dependencies, Git information, host information, metrics, resources, and output artifacts. Automatic random seeding supports repeatable execution, while observers store run metadata and results. Numerical metrics and time-series logging cover core experiment records, and artifact tracking extends that record to files and model outputs. This makes Sacred a focused fit for teams that value traceability around how a run was configured and what it produced.

Storage flexibility is another important consideration. Observers can use MongoDB, file storage, TinyDB, SQL databases, Amazon S3, or Google Cloud Storage, allowing the deployment to match an existing research environment. The trade-off is that Sacred is self-hosted and open-source rather than a managed SaaS product, so it suits teams prepared to operate their own setup. It also does not provide verified run comparison, making it less suitable for buyers who want built-in visual comparison across experiments. Pick Sacred for reproducible Python workflows and configurable storage; choose another tool if hosted management or run comparison is central.

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Sacred pricing

Sacred fact sheet

Sacred integrations

Sacred lists 5 integrations on its own site.

• MongoDB
• TinyDB
• SQL databases
• Amazon S3
• Google Cloud Storage

Alternatives to Sacred

• Weights & BiasesA broad ML platform that extends experiment tracking into production workflows.8.9
• ClearMLA broad MLOps platform with experiment tracking, data versioning, orchestration, and serving.8.8
• CometA polished experiment tracking platform with strong comparison, lineage, and registry tools.8.7

See all Sacred alternatives →

Featured on iTechGuides

Sacred is listed in our ML Experiment Tracking Software directory. Add the badge to your site — it links back to this page.

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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

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Last updated 7 Oct 2026 · How we research and update