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| Pricing | Free | Free |
| Capabilities | 15 decomposed | 10 decomposed |
| Times Matched | 0 | 0 |
Snyk Code performs deep static analysis of source code using the DeepCode AI Engine to identify security vulnerabilities, code quality issues, and anti-patterns without executing code. The engine analyzes Abstract Syntax Trees (AST) across 40+ programming languages, correlating patterns against a proprietary vulnerability database and machine learning models trained on historical vulnerability data. Real-time scanning integrates directly into IDEs, providing inline fix suggestions with contextual code examples during development.
Unique: Uses DeepCode AI Engine (proprietary machine learning models trained on historical vulnerability patterns) combined with AST-based structural analysis across 40+ languages, providing inline fix suggestions with code examples directly in the IDE rather than just flagging issues in a separate dashboard
vs alternatives: Faster developer feedback than traditional SAST tools (SonarQube, Checkmarx) because it integrates real-time scanning into the IDE with AI-generated fix examples, reducing context-switching and time-to-remediation
Snyk Open Source scans project manifests (package.json, requirements.txt, pom.xml, Gemfile, go.mod, etc.) to identify known vulnerabilities in direct and transitive open-source dependencies. The platform maintains a proprietary database of vulnerability intelligence aggregated from public CVE feeds, security advisories, and Snyk's own research. Scanning can be triggered on-demand, scheduled, or integrated into CI/CD pipelines; continuous monitoring watches for newly disclosed vulnerabilities in already-scanned projects and alerts developers to remediation paths (patches, upgrades, or workarounds).
Unique: Combines proprietary vulnerability intelligence database with continuous monitoring that automatically re-scans projects when new vulnerabilities are disclosed, providing proactive alerts rather than only scanning on-demand; includes transitive dependency analysis and remediation path recommendations (upgrade, patch, or workaround) with risk scoring
vs alternatives: More comprehensive than npm audit or pip check because it scans transitive dependencies, provides remediation recommendations with risk scoring, and continuously monitors for newly disclosed vulnerabilities rather than only scanning at build time
Snyk integrates with Jira (cloud and self-hosted) to automatically create and track vulnerability issues, enabling security findings to be managed within existing issue tracking workflows. The integration maps Snyk vulnerabilities to Jira issues with configurable fields (priority, assignee, labels, custom fields), enables developers to track remediation progress, and provides bidirectional sync to keep Snyk and Jira in sync. Integration is available in Team plan and above.
Unique: Provides bidirectional integration with Jira (cloud and self-hosted) to automatically create and track vulnerability issues with configurable field mapping, enabling security findings to be managed within existing issue tracking workflows rather than in a separate security dashboard
vs alternatives: More integrated than standalone security platforms because it brings vulnerability findings directly into Jira workflows; more flexible than native Jira security plugins because it supports multiple scanning types (code, dependencies, containers, IaC) in a unified platform
Snyk provides remediation recommendations for identified vulnerabilities, including upgrade paths for dependencies, base image recommendations for containers, and corrected IaC code examples. For open-source dependencies, Snyk can automatically apply patches via the snyk fix command or create pull requests with recommended upgrades. Recommendations are prioritized based on risk scores, and Snyk provides guidance on breaking changes and compatibility impacts to help developers make informed remediation decisions.
Unique: Provides prioritized remediation recommendations based on proprietary risk scoring, with automated patching via snyk fix command for open-source dependencies and pull request creation for dependency upgrades; includes compatibility and breaking change analysis to help developers make informed decisions
vs alternatives: More comprehensive than Dependabot or Renovate because it includes risk-based prioritization and compatibility analysis; more actionable than manual CVE research because it provides specific upgrade paths and breaking change guidance
Snyk generates compliance reports mapping vulnerability findings to regulatory frameworks (CIS benchmarks, PCI-DSS, HIPAA, SOC 2, GDPR, etc.) and provides audit trails documenting vulnerability discovery, assignment, remediation, and closure. Reports are available in multiple formats (PDF, JSON, CSV) and can be scheduled for automatic generation and delivery. Compliance reporting is available in Ignite and Enterprise plans and helps organizations demonstrate security posture to auditors and stakeholders.
Unique: Maps vulnerability findings to multiple regulatory frameworks (CIS, PCI-DSS, HIPAA, SOC 2, GDPR) and generates compliance reports with audit trails documenting discovery, assignment, and remediation; available in Ignite/Enterprise plans for organizations with strict compliance requirements
vs alternatives: More comprehensive than standalone compliance tools because it integrates vulnerability findings with compliance framework mappings; more developer-friendly than manual compliance documentation because it automates report generation and audit trail tracking
Snyk provides real-time and historical reporting capabilities designed for security engineers and GRC (Governance, Risk, Compliance) teams. Reports track vulnerability discovery trends, remediation progress, policy compliance, and security posture over time. Reporting is available in Ignite and Enterprise tiers and supports compliance documentation and executive visibility.
Unique: Provides real-time and historical reporting designed specifically for GRC teams, tracking vulnerability trends and remediation progress with compliance-focused metrics and audit trails
vs alternatives: More compliance-focused than basic vulnerability lists because it tracks trends, remediation progress, and policy compliance over time, supporting regulatory audits and executive reporting
Snyk API & Web (available as add-on) provides dynamic application security testing (DAST) capabilities for discovering and testing vulnerabilities in running APIs and web applications. The system performs active scanning of application endpoints to identify runtime vulnerabilities, injection flaws, authentication issues, and other OWASP Top 10 issues. DAST scanning complements static analysis by testing actual application behavior.
Unique: Provides dynamic application security testing (DAST) as add-on to complement static analysis, enabling runtime vulnerability discovery in APIs and web applications through active scanning
vs alternatives: Complements static analysis by testing actual application behavior at runtime, discovering vulnerabilities that static analysis cannot detect (e.g., authentication bypasses, business logic flaws)
Snyk Container scans Docker images and container registries (Docker Hub, Amazon ECR, Google Container Registry, Azure Container Registry, Artifactory, Quay, etc.) for vulnerabilities in base OS layers, application dependencies, and configuration issues. Scanning can be triggered on image push, scheduled periodically, or integrated into CI/CD pipelines. The platform analyzes image layers, identifies vulnerable packages, and provides remediation recommendations (base image upgrades, dependency patches). Integration with container registries enables continuous monitoring of deployed images for newly disclosed vulnerabilities.
Unique: Integrates with multiple container registries (Docker Hub, ECR, GCR, ACR, Artifactory, Quay) and provides continuous monitoring of deployed images for newly disclosed vulnerabilities, combined with base image recommendations and layer-by-layer vulnerability analysis rather than just flagging vulnerable packages
vs alternatives: More comprehensive than Trivy or Grype because it integrates with multiple registries, provides continuous monitoring of deployed images, and offers base image recommendations; more developer-friendly than Aqua or Twistlock because it integrates into Snyk's unified platform with consistent remediation workflows
+7 more capabilities
Aggregates 67 TB of source code from the Software Heritage archive, filtering for permissively licensed repositories (MIT, Apache 2.0, BSD, etc.) across 600+ programming languages. Uses automated license detection and validation to ensure legal compliance for model training. Implements a rigorous deduplication pipeline at file and repository levels to eliminate redundant training data and reduce dataset bloat.
Unique: Largest open-source code dataset at 67 TB with automated opt-out governance allowing repository owners to request removal, combined with rigorous deduplication and PII removal pipeline — no other public dataset offers this scale with legal compliance and community control mechanisms
vs alternatives: Larger and more legally compliant than GitHub's CodeSearchNet (14M files) or Google's BigQuery public datasets, with explicit opt-out governance vs. implicit inclusion, and covers 600+ languages vs. Codex training data's undisclosed language distribution
Implements a community-driven opt-out system where repository owners can request removal of their code from the dataset without legal takedown notices. Maintains a registry of excluded repositories and re-applies exclusions during dataset updates. Provides transparent governance documentation and a clear submission process for removal requests, balancing open access with creator rights.
Unique: First large-scale code dataset to implement opt-out governance at dataset level rather than relying solely on license compliance, with transparent registry and community submission process — shifts power from dataset creators to code contributors
vs alternatives: More respectful of creator autonomy than GitHub Copilot's training approach (no opt-out) or academic datasets (one-time snapshot), and more scalable than individual DMCA takedowns
The Stack v2 scores higher at 61/100 vs Snyk at 56/100.
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Automated pipeline that scans source code for personally identifiable information (email addresses, API keys, SSH keys, credit card patterns, phone numbers) and removes or redacts them before dataset release. Uses regex patterns, entropy-based detection for secrets, and heuristic rules to identify sensitive data. Operates at file level with configurable sensitivity thresholds to balance data utility against privacy risk.
Unique: Combines regex pattern matching, entropy-based secret detection, and heuristic rules in a unified pipeline with configurable sensitivity — more comprehensive than simple regex-only approaches, but trades off false positive rate against security coverage
vs alternatives: More thorough than GitHub's secret scanning (which only flags known patterns) because it includes entropy-based detection for unknown secret formats, but less accurate than specialized tools like TruffleHog due to language-agnostic approach
Indexes 67 TB of source code across 600+ programming languages with language-aware metadata (syntax, file extension, language family). Enables retrieval by language, license, repository, or code patterns. Uses Software Heritage's existing indexing infrastructure as foundation, augmented with language detection and classification. Supports both bulk download and filtered queries for specific language subsets.
Unique: Leverages Software Heritage's existing language detection and indexing infrastructure, then augments with BigCode-specific language classification and filtering — avoids reinventing language detection while providing dataset-specific query capabilities
vs alternatives: More comprehensive language coverage (600+ languages) than GitHub's Linguist (500+ languages) and more accessible than Software Heritage's raw API because it's pre-filtered for permissive licenses and deduplicated
Removes duplicate code files and repositories using content hashing (SHA-256 or similar) and fuzzy matching for near-duplicates. Operates in two stages: exact deduplication via hash matching, then fuzzy matching (e.g., Jaccard similarity or MinHash) to catch semantically identical code with minor formatting differences. Preserves one canonical copy of each unique code pattern while removing redundant training examples.
Unique: Two-stage deduplication combining exact hash matching with fuzzy similarity matching (likely MinHash or Jaccard) to catch both identical and near-identical code — more thorough than single-stage approaches but computationally expensive
vs alternatives: More aggressive deduplication than CodeSearchNet (which uses simple hash matching) because it catches near-duplicates, but less semantic than clone detection tools (which understand code structure) because it's content-based
Integrates with Software Heritage's comprehensive archive of 200+ million repositories and their full version control history. Extracts source code snapshots from Software Heritage's Git/Mercurial/SVN repositories, preserving repository metadata (commit history, author info, timestamps). Provides access to code at specific points in time, enabling historical analysis or training on code evolution patterns.
Unique: Leverages Software Heritage's universal code archive (200M+ repositories) as data source, providing access to code that would be impossible to collect via GitHub API alone — enables training on archived/deleted repositories and non-GitHub platforms (GitLab, Gitea, etc.)
vs alternatives: More comprehensive than GitHub-only datasets because it includes code from GitLab, Gitea, SourceForge, and other platforms archived by Software Heritage; more legally defensible than web scraping because it uses an established, community-maintained archive
Tracks and validates SPDX license identifiers for each repository, ensuring only permissively licensed code (MIT, Apache 2.0, BSD, etc.) is included. Maintains license metadata alongside code files, enabling downstream users to verify legal compliance. Implements license hierarchy and compatibility checking to handle dual-licensed or complex licensing scenarios.
Unique: Combines automated SPDX detection with manual review and maintains license metadata alongside code, enabling downstream users to verify compliance — more transparent than datasets that simply claim 'permissive licenses' without proof
vs alternatives: More legally rigorous than GitHub's CodeSearchNet (which doesn't validate licenses) and more transparent than Codex training data (which doesn't disclose license filtering at all)
Maintains versioned snapshots of the dataset (e.g., v2.0, v2.1) with documented changes between versions (new repositories added, deduplication improvements, PII removal updates). Provides checksums and manifests for reproducibility, enabling researchers to cite specific dataset versions and reproduce results. Tracks dataset lineage and transformation history.
Unique: Maintains semantic versioning and detailed changelogs for dataset releases, enabling researchers to cite specific versions and understand dataset evolution — more rigorous than one-off dataset releases without versioning
vs alternatives: More reproducible than academic datasets that are released once without versioning, and more transparent than commercial datasets (Codex) that don't disclose version history or changes
+2 more capabilities