AI-powered code assistants for faster secure development
GitLab AI is an integrated set of AI-assisted developer tools inside GitLab that accelerate code authoring, review, and security scanning for engineering teams. It best serves DevOps and software engineering teams who want AI tied directly to CI/CD, repository context, and audit logs. Pricing includes a free tier with limited features plus paid Premium/Ultimate add-ons; larger enterprise features require paid plans.
GitLab AI brings generative code assistance and AI-enhanced DevOps features directly into the GitLab platform. It provides code completion, MR summaries, automatic test generation, and security scanning that use repository context to recommend changes. The key differentiator is native integration with GitLab CI/CD, issue tracking, and the project audit trail so outputs are traceable and access-controlled. GitLab AI is aimed at software engineers, SREs, and security teams who need assistive coding and automated reviews within their existing GitLab workflows. A free tier exists with basic AI features; advanced capabilities require paid tiers or GitLab Ultimate.
GitLab AI is GitLab’s built-in artificial intelligence capabilities layered across its single application for the DevOps lifecycle. Launched as a branded set of AI features after GitLab began integrating models and automation, GitLab AI is positioned to reduce friction between code authoring, CI/CD pipelines, and security/compliance checks by surfacing AI outputs inside merge requests, issues, and pipelines. The core value proposition is contextual, repository-aware assistance: models act on the same repo data, CI variables, and MR history you already store in GitLab, preserving auditability and role-based access controls that teams rely on.
Feature-wise, GitLab AI currently includes code completion and generation embedded in the Web IDE and merge request experience, merge request summary generation and suggested changelists that accelerate reviews, and automated test generation (unit test suggestions) that are derived from repository code. It also includes automated security and license scanning augmented by model-driven prioritization — for example, AI can highlight high-risk vulnerabilities in dependency reports and suggest remediation snippets. Additionally, GitLab AI ties these outputs into pipeline jobs and the audit log so suggested changes, approvals, and model-triggered pipeline runs are recorded and can be routed into existing CI/CD workflows.
On pricing, GitLab provides a baseline of features in its Free plan for public projects and limited private projects; GitLab’s AI-branded features expand with paid tiers. As of 2026, code assistance basics and MR summaries are available in GitLab’s paid tiers (Premium/Ultimate) with per-user licensing on top of existing plan pricing — enterprises typically enable advanced AI features under GitLab Ultimate or via add-on arrangements. GitLab offers self-managed and SaaS options; feature availability and limits (such as tokens, model access, or CI pipeline quotas) vary by plan and on-premise configuration. For accurate per-seat costs, GitLab’s published prices for Premium/Ultimate and Enterprise support should be consulted, since AI add-on terms are often part of commercial negotiations.
Typical users include engineering teams that want AI integrated into their existing DevOps toolchain. For example, a Senior Software Engineer uses MR summaries to cut review time by surfacing key changes; a Security Engineer leverages AI-augmented vulnerability triage to reduce false positives in dependency scans. Product teams also use GitLab AI to auto-generate test scaffolding and improve onboarding for junior engineers. Compared to a stand-alone code assistant, GitLab AI’s advantage is the native CI/CD and audit integration; teams that need deep editor-agnostic, multi-IDE support may still choose specialized assistants like GitHub Copilot or JetBrains Fleet integrations instead.
Three capabilities that set GitLab AI apart from its nearest competitors.
Current tiers and what you get at each price point. Verified against the vendor's pricing page.
| Plan | Price | What you get | Best for |
|---|---|---|---|
| Free | Free | Basic repo and CI minutes limits; limited AI features for public projects | Individual developers exploring GitLab workflow |
| Premium | $19 per user/month | Enhanced CI/CD, SSO, basic AI MR summaries and suggestions | Growing teams needing reliability and basic AI assists |
| Ultimate | $99 per user/month | Full security, compliance, advanced AI code and scanning features | Enterprises requiring security, compliance, and AI features |
| Self-managed Enterprise (Custom) | Custom | On-prem AI model hosting and extended CI pipelines by contract | Large orgs needing on-premise control and custom SLAs |
Copy these into GitLab AI as-is. Each targets a different high-value workflow.
You are GitLab AI assisting a code reviewer. Given a merge request diff or description pasted after this prompt, produce a concise, actionable review-ready MR summary. Constraints: (1) produce a 3-sentence plain-language summary that states intent and impact; (2) list up to 12 changed files with file types; (3) highlight up to 5 high-risk items (security, performance, API, schema) with one-line rationale each; (4) suggest 2–4 appropriate reviewers by role. Output format: JSON with keys: summary, changed_files (array), risks (array of {file,issue}), suggested_reviewers (array). Example input placeholder: <PASTE MR DIFF OR DESCRIPTION HERE>.
You are GitLab AI generating a unit test scaffold. Input: paste a single function or small class implementation after this prompt. Constraints: (1) produce a pytest file named test_<module>.py containing imports, three clear test cases (happy path, edge case, error case) with descriptive names; (2) use fixtures or simple mocks if external calls exist and add TODOs where behavior is undefined; (3) include a one-line command to run the tests. Output format: provide the full file content as a single code string and the run command. Example input placeholder: <PASTE FUNCTION OR CLASS CODE HERE>.
You are GitLab AI writing an optimized .gitlab-ci.yml snippet to parallelize and cache test runs. Inputs: specify project language (Python or Node) and provide TEST_MATRIX variable like [unit,integration,smoke]. Constraints: (1) include a parallel matrix job that splits tests into logical groups using GitLab parallel matrix; (2) include a caching strategy and artifacts retention of 1 day; (3) keep snippet under ~60 lines and note trade-offs. Output format: two labeled sections: YAML_SNIPPET (ready to paste) and SUMMARY (2–3 lines estimating runtime improvement and trade-offs). Example variable placeholder: TEST_MATRIX=[unit,integration,smoke].
You are GitLab AI performing security triage for a reported vulnerability from SAST/SCA. Input: paste the scanner output or CVE reference after this prompt. Constraints: (1) produce a prioritized remediation plan with three severity buckets (urgent, high, low) and target SLAs for each; (2) calculate an exploitability score using CVSS factors and state confidence level; (3) include one GitLab CI rule snippet that fails pipelines when severity >= high. Output format: Markdown with sections titled Summary, CVSS_Estimate, Remediation_Plan (prioritized list with SLAs), and CI_Rule (YAML snippet). Example input placeholder: <PASTE SCANNER OUTPUT OR CVE HERE>.
You are GitLab AI acting as a security engineer and maintainer. Given a small repository context and a vulnerability finding (paste the relevant file code and scanner finding after this prompt), perform three steps: (A) produce a minimal unified diff that fixes the vulnerability (include file paths); (B) produce updated or new unit tests that validate the fix; (C) produce a merge request draft description that includes risk assessment, test plan, rollback steps, and references to related issue and pipeline IDs. Constraints: keep the patch minimal, include commands to run tests locally, and ensure diffs are in unified diff format. Output format: three labeled sections: DIFF, TESTS, MR_DRAFT.
You are GitLab AI supporting an SRE investigating a performance regression detected in CI benchmarks. Input: paste baseline and current metrics (CSV or summary) after this prompt. Tasks: (1) produce a prioritized investigation plan with hypotheses to test; (2) provide exact commands to reproduce benchmarks locally and commands for profiling (perf, flamegraph, or language-specific profilers); (3) include a GitLab CI job snippet that reproduces the slowdown and captures profiling artifacts; (4) provide metric thresholds for alerting and a 6-step rollback/mitigation checklist. Output format: numbered plan, command blocks, and one YAML CI job snippet. Example input placeholder: <PASTE BENCHMARK CSV OR SUMMARY HERE>.
Choose GitLab AI over GitHub Copilot if you prioritize CI/CD and audit-integrated AI tied directly to your repositories and compliance needs.
Head-to-head comparisons between GitLab AI and top alternatives: