GitLab Premium costs $29 per user per month when billed annually. This price creates a significant gap compared to the $4 per user per month starting rate for GitHub Team. The massive pricing difference between GitHub’s entry-level tier and GitLab’s Premium tier means that large enterprise organizations must evaluate if the integrated security features justify the higher costs when they scale to hundreds of developers. This discrepancy forces enterprise teams to weigh the cost of GitLab’s integrated DevSecOps tools against the lower price and broader ecosystem of GitHub. While GitLab includes security scanning, GitHub wins on developer experience and AI assistance. Migration between platforms involves rebuilding CI/CD pipelines and takes 2 to 6 months for enterprise teams. GitHub currently holds 100 million developers compared to the 5 million users on GitLab. Reuters reported that GitLab is exploring a sale after hearing from potential acquirers such as Datadog. Analysts suggest a sale to a firm like Datadog could benefit GitLab by combining DevSecOps with observability. Such a pairing allows development teams to release and monitor applications with a single platform. However, an acquisition might change the existing feature set and pricing structures. GitLab’s popularity rests on its ability to let enterprises optimize pre-production activities without building CI/CD and testing pipelines from scratch. For most teams, GitHub is the better default choice.
| Plan | Monthly Price per User | AI Add-on / Credit Note |
|---|---|---|
| GitHub Team | $4 | Copilot starts at $19 |
| GitLab Premium | $29 | Duo Enterprise add-on available |
| GitLab Ultimate | Custom | Includes advanced security/compliance |
Technical vulnerabilities
Security teams face immediate threats from recent vulnerabilities in GitLab’s AI workflows. A critical flaw, CVE-2026-1868, has a CVSS score of 9.9 and allows insecure template expansion through Duo Agent Platform Flow definitions. Another high-severity issue, CVE-2026-4868, carries a CVSS score of 8.2 and lets an authenticated user trigger Duo AI workflows under another user’s identity. These bugs affect self-managed installations that have not applied the latest patches. The identity resolution error in CVE-2026-4868 could expose private project data when lower-privileged users bypass access controls. A January session failure showed how Redis configuration errors block agentic chat for entire customer environments. Can GitLab stabilize these AI features before the next major vulnerability emerges? The platform faces authorization bugs across GraphQL, Duo Workflows, Operations, and authentication endpoints that require immediate patching. The remaining medium-severity issues involve unauthorized data exposure and permission bypass. These flaws affect versions 18.8, 18.11, or 19.0.
Agentic AI rollout
The GitLab Duo Agent Platform introduces specialized agents like the Planner Agent and the Security Analyst Agent. These tools use a credit-based system where Premium customers receive $12 and Ultimate customers receive $24 in monthly credits. This credit model attempts to resolve the AI paradox where increased code authoring speed creates new bottlenecks in code reviews and security triage. Since developers spend only about 20% of their time writing code, the improvements in total innovation velocity from AI remain incremental. Most users only consume a small part of the full GitLab capability set, which makes the all-in-one pricing model a hard sell for many. GitLab Duo Enterprise, which was previously only for Ultimate users, acts as a paid add-on for Premium subscribers to access automated remediation. This tool helps teams proactively detect and fix security vulnerabilities. The platform includes the Code Agentic Chat to generate code and configurations. Users use the CI/CD Agentic Chat to troubleshoot pipelines. The system provides the Developer flow to build structured merge requests for all users. The platform includes the MCP Client to connect to systems like Jira or Slack. Teams can build custom agents using the AI Catalog to replicate engineering standards. The Code Review flow analyzes changes to streamline workflows. This helps simplify workflows through AI-native analysis. The system provides the Fix CI/CD pipeline flow to analyze failures and identify causes.
