DeepSource vs detect-secrets

DeepSource is an AI code review platform for teams writing code with AI. It reviews pull requests through static analysis and an AI review agent, looking for bugs, anti-patterns, and security vulnerabilities. The review process uses more than 5,000 deterministic rules, while Autofix can provide verified, pre-generated patches for many detected issues. Other checks include secrets detection, open-source dependency vulnerability scanning, and infrastructure-as-code review for Terraform and CloudFormation. Teams can track code coverage and set thresholds for tested code. Supported version-control systems include GitHub, GitLab, Bitbucket, and Azure DevOps; listed integrations also include Jira, Okta, OneLogin, Slack, and Vanta. The service is available through web, API, Linux, macOS, Windows, and self-hosted deployment. Its GraphQL API has a documented limit of 5,000 requests per hour per user. The Open Source tier costs nothing and includes unlimited public repositories and members, plus monthly limits for reviews and formatting runs. Team costs $24 a year, billed yearly, with a stated rate of $24 per user per month. A 14-day trial is available. Enterprise uses custom pricing and offers cloud or self-hosted deployment, among other listed features.

detect-secrets is a free, open-source Python module for finding secrets in codebases and helping stop new secrets from entering Git changes. It runs on Linux, macOS, and Windows, with self-hosted use documented through pip or Homebrew. Heuristic regular expressions inspect changes for newly committed secrets, alongside entropy and keyword detection. The scan command creates or updates a baseline of potential findings; hook flags new secrets; and audit lets analysts review, label, report, or compare baseline entries. Users can configure it as a pre-commit hook with the pre-commit framework. Built-in plugins and filters can be adjusted, and custom detector plugins and filters can be developed. Exclusions, matching-line or file exclusions, secret-value exclusions, and inline allowlist comments help handle false positives. The package can also be imported into Python scripts to scan files and serialize findings. Word-list filtering and gibberish detection require optional packages, and gibberish detection is disabled by default. The project uses Apache-2.0 licensing.