GitLab vs GitHub

Q: Can I keep GitHub repos but use GitLab CI/CD?

Yes. GitLab can mirror your GitHub repos and run CI/CD pipelines on them, so you can keep code in GitHub while standardizing on GitLab for delivery. Many teams start here before fully migrating.

Q: How does GitLab pricing compare to GitHub at scale?

GitHub starts with lower list prices, but Copilot, Advanced Security, additional Actions minutes, are each sold separately. GitLab includes CI/CD and advanced planning in every paid tier, with no orchestration fees for self‑hosted runners, and expands security from baseline scanning in Premium to full DevSecOps coverage in Ultimate, which can help keep total cost of ownership more predictable as you grow.

Q: Is GitLab good for regulated industries?

Yes. GitLab offers SaaS, Dedicated, and self-managed deployment options to address different regulatory contexts. Compliance frameworks, audit event streaming, and FIPS-compliant builds are available in Ultimate and Dedicated tiers. Many customers in financial services and the public sector choose these options for sovereignty and compliance needs.

Q: How long does migration take?

It depends on size and complexity. GitLab’s importer handles repos, issues, and PRs. Some teams migrate in days, while some run GitLab and GitHub in parallel for a while before completing the migration.

Q: Is GitLab good for open source projects?

Yes. GitLab’s free tier includes CI/CD, security scanning (basic SAST scanning, secret detection, and container scanning), and full DevOps tooling for public projects. The platform itself is open core with thousands of community contributors. Open source projects may be eligible to join the [GitLab for Open Source Program](/solutions/open-source/) which grants access to Ultimate features.

Q: How does project planning functionality compare between GitLab and GitHub?

Both platforms have boards, roadmaps, and issue hierarchy. GitHub Projects includes a native burnup chart. GitLab goes further with multi-level epics, Value Stream Analytics (tracking DORA metrics and cycle time across the full SDLC), native burndown charts, velocity tracking, and portfolio-level roadmap rollups with epic health status.

Q: Can I use GitLab and GitHub together?

Yes. You can mirror repositories between GitLab and GitHub.

Q: What do we need in place before migrating from GitHub to GitLab?

Start with an audit of your current repos, CI workflows, and integrations. GitLab’s importer brings over repos, issues, and PRs; mapping GitHub Actions workflows to GitLab CI and planning access controls in advance makes the switch smoother.

Q: How do GitLab and GitHub compare for DevOps‑heavy workflows?

GitLab was built as a complete DevSecOps platform — CI/CD, security scanning, compliance frameworks, and advanced planning are all native to a single application with a unified data model. GitHub provides native CI/CD through Actions and security scanning through paid add-ons (Code Security and Secret Protection), but organizations needing Premium and Ultimate capabilities such as DAST, compliance automation, Value Stream Analytics, or DORA metrics must add third-party tools to fill those gaps.

Q: How does GitLab CI/CD compare to GitHub Actions?

GitLab CI/CD gives developers advanced pipeline orchestration (parent‑child and multi‑project pipelines), full YAML reuse (`extends`, `!reference`, `merge keys`), rich pipeline graphs, and `allow_failure' controls that help author and maintain complex pipelines efficiently. GitLab Runner is open source, written in Go, runs on more platforms (including Docker, Kubernetes, Podman, Alpine, FreeBSD), and has no orchestration fees for self‑hosted runners, while GitHub Actions Runner is written in C# and tied to minutes‑based pricing.

What are the fundamental differences between GitLab and GitHub?

GitHub

GitLab

Platform consolidation

GitHub

Add-ons required, gaps remain

GitHub's security scanning, AI, and advanced CI/CD are native but sold as separate paid add-ons on top of the base platform price. Value stream analytics, DORA metrics, DAST, and compliance automation are not available from GitHub at any tier and require third-party tools to fill.

GitLab is a unified platform

GitLab puts planning, source code, CI/CD, security, and deployment into a single application with one permission model, one audit trail, and shared analytics. That means fewer tools to run, fewer integrations to maintain, and one place to apply policies and AI across the whole lifecycle.

AI & context

GitHub

Context with code- and repo-centric AI

GitHub Copilot draws context from GitHub data such as repositories, pull requests, issues, Projects, and security findings, and the GitHub MCP server exposes that platform data to agents. For broader SDLC automation across planning, CI/CD, and security, assembly of multiple tools and MCP servers is required, while GitLab focuses on SDLC-wide agents on a single platform.

Full-lifecycle, policy-aware AI context

GitLab Duo Agent Platform runs on a single DevSecOps platform, so agents see issues, epics, merge requests, pipelines, environments, and security findings in one data model. That unified SDLC and policy context lets GitLab Duo power agents for planning, CI/CD troubleshooting, and security remediation without stitching together separate products or backends.

GitHub

AI centered on code and repository tasks

GitHub Copilot and the GA Copilot coding agent automate code- and repo-scoped tasks like implementing features, fixing bugs, improving tests, and updating documentation via pull requests and GitHub Actions. For broader SDLC automation across cross-project planning, CI/CD orchestration, and security remediation, assembly of multiple tools and workflows is required, while GitLab focuses on SDLC-wide agents and flows on one platform.

GitLab’s AI works across your entire delivery chain

GitLab Duo Agent Platform provides agents and flows that automate planning, coding, CI/CD, security, documentation, onboarding, and modernization. These agents help break down epics into issues, convert and optimize pipelines, diagnose and fix failed builds, triage and remediate vulnerabilities, generate architecture docs and onboarding guides, and modernize codebases (for example, upgrading Java or refactoring legacy services) on a single platform.

GitHub

GitHub trains AI models by default for some tiers

As of April 24, 2026, GitHub uses Copilot interaction data — inputs, outputs, code snippets, and associated context — to train AI models by default for Free, Pro, and Pro+ users. Users must manually opt out. This data may also be shared with GitHub affiliates, including Microsoft for AI development purposes. Copilot Business and Copilot Enterprise customers are exempt from this model training under their data protection agreements.

GitLab doesn’t use customer code to train models. Period.

GitLab doesn’t train its models on any customer code at any tier, including free, Premium, and Ultimate. This means there’s no way any contractors, open source contributors, or developers on individual-tier accounts could have code entering training pipelines without company visibility or control.

Vendor independence

GitHub

Tied to Microsoft and Azure

GitHub is owned by Microsoft and all Copilot requests pass through a GitHub proxy hosted in Azure.

GitLab is cloud agnostic

GitLab is not commercially tied to any cloud provider.

GitHub

Broad cloud model catalog and BYOM

GitHub Copilot offers a wide range of cloud-hosted models (multiple GPT-5 families, Claude, Gemini, Grok/Qwen, and more) plus “bring your own API key” options for several providers. Admins can enable or disable specific models at the enterprise or organization level, and organizations within an enterprise can have different model availability — but there is no per-feature or per-team model routing. Within the approved model pool, individual users can also select a model for inline completions, though the available options are more limited than in GitLab Duo Chat and require org-level enablement on Business/Enterprise plans.

Flexible model choice on one platform

GitLab Duo lets you use GitLab-managed models or your own deployments (Anthropic, OpenAI, Mistral, Llama, etc.) through the AI Gateway, and choose models per feature or team from a single admin surface. This gives you model flexibility without fragmenting your DevSecOps platform.

GitHub

Cloud-only AI execution

GitHub Copilot always runs in provider clouds (Azure/OpenAI, Anthropic via Bedrock/GCP, Google Gemini, xAI), even when you supply your own API keys or use GitHub Enterprise Server. There is no way to run Copilot models or agents fully on-premises or air-gapped, which makes GitLab the stronger option for organizations that require self-hosted AI with strict data residency and isolation.

Self-hosted models and agents, including air-gapped

GitLab Duo Self-Hosted and GitLab Duo Agent Platform Self-Hosted let you run models and agents on your own infrastructure (on-prem or private cloud) via a self-hosted AI Gateway, including fully offline and air-gapped setups. You can also mix self-hosted and GitLab-managed models in a hybrid configuration when internet access is allowed.

Security & governance

Comparing GitHub Enterprise with Copilot Enterprise against GitLab Premium with Duo Agent Platform, unless otherwise stated. GitLab Duo Agent Platform subject to usage-based pricing.

One platform.

No toolchain tax.

Experience the difference

GitLab is the unified DevSecOps platform that replaces fragmented toolchains with one integrated system that’s built for AI-era velocity, enterprise security, advanced planning with Value Stream Analytics, and deployment anywhere.

Try for free

The problem was that a majority of a developer’s time had been spent on integrating and connecting one disparate tool to the other. And if there was a problem, finding out which tool was causing it. With a single platform, though, everything is already connected and integrated by design.

25%

of technology budget saved

40%

decrease in deployment time

Andre Nita

Chief Technology Officer, McKenzie Intelligence Services

View case study

25%

of technology budget saved

40%

decrease in deployment time

How do GitLab and GitHub's features compare?

GitHub

GitLab

CI/CD

Built-in CI/CD

GitHub

GitHub Actions provides native CI/CD and deployment workflows with reusable workflows and composite actions, but lacks multi-project/parent-child pipelines and merge keys. Pipeline visualization is limited to single workflows, lacking multi-workflow and repository pipeline views.

GitHub Actions Runner supports fewer environments (no native Alpine/FreeBSD/Podman or running the runner itself in Docker) and autoscaling requires Kubernetes or custom scale-set tooling.

GitLab provides native, fully integrated CI/CD with parent-child and multi-project pipelines, reusable YAML (extends, !reference, anchors), CI/CD templates and Catalog components, and pipeline graphs that show stages, needs-based dependencies, and downstream/child pipelines.

GitLab Runner is open source and runs on a broad set of platforms (Linux including Alpine, FreeBSD, Windows, macOS, Docker, Kubernetes, Podman) with built-in auto-scaling options, making it easier to standardize CI/CD across heterogeneous environments.

Built-in CI/CD

Merge trains

GitHub

Merge queue supports batching up to 100 PRs but does not run speculative parallel pipelines on cumulative merge results.

Native merge trains with up to 20 parallel pipelines, each validating the cumulative result of all queued MRs. Failed MRs are automatically removed and only affected pipelines rebuild.

Merge trains

Review apps

GitHub

GitHub can display deployment preview links on pull requests via its Environments feature, but does not automatically provision or tear down preview environments. Teams must build and maintain custom Actions workflows for the full review app experience.

Built-in dynamic preview link created automatically for each branch or merge request

Review apps

Advanced pipelines

GitHub

No native parent-child or multi-project pipeline orchestration with unified status tracking across repos

Native support for parent-child pipelines and multi-project pipelines

Advanced pipelines

Orchestration fees

GitHub

Orchestration fees postponed — for now. GitHub announced $0.002/min orchestration fees for self-hosted runners, then indefinitely postponed implementation after enterprise backlash.

GitLab charges zero orchestration fees for self-hosted CI/CD runners. Period.

Orchestration fees

AI

Enterprise-ready agentic AI

GitHub

Copilot Coding Agent, Agentic Code Review, and Copilot CLI are GA.

Agentic Workflows is in technical preview. Agent HQ is in public preview.

GitLab Duo Agent Platform, its pre-built agents (such as Planner, Data Analyst, and Security Analyst), and flows (such as Code Review and Fix Pipeline) are GA and production ready.

Enterprise-ready agentic AI

AI model hosting

GitHub

Limited to Microsoft’s AI and Azure cloud ecosystem. No support for self-hosted models in private infrastructure.

Supports multiple AI providers for inference, along with self-hosted models in private enterprise environments.

AI model hosting

AI training data protection

GitHub

Enables use of customer data for training by default for Copilot Free, Pro, and Pro+ users.

Contractually prevents AI providers from using customer data for AI training.

AI training data protection

Token cost management

GitHub

Budget alerts and usage visibility, but no true enforced usage caps for token cost management.

Enables usage caps for token cost management and visibility.

Token cost management

Security & compliance

Advanced planning

Value stream analytics

GitHub

No native tracking for cycle time, lead time, or DORA metrics.

Native VSA in GitLab Premium tracks cycle time and lead time across the full SDLC.

Value stream analytics

Portfolio roadmaps

GitHub

GitHub’s roadmap view is scoped to a single board in Projects. No cross-repo or cross-org rollup. No health status on parent items.

Cross-group epic roadmaps with rollup progress and health status.

Portfolio roadmaps

Native burndown and velocity tracking

GitHub

GitHub Projects includes a native burnup chart. It does not offer native burndown charts or velocity tracking.

Native burndown and burnup charts per milestone and iteration as well as velocity tracking.

Native burndown and velocity tracking

Issue hierarchy and cross-group visibility

GitHub

Sub-issues with nesting up to eight levels and progress indicators in Projects. No cross-group epic visibility, no health status on parent items beyond completion percentage, and no native connection to delivery metrics like DORA or cycle time.

Epics as primary work items with health status, start/due dates, nested child epics, and cross-group linked work item visibility.

Issue hierarchy and cross-group visibility

Comparing GitHub Enterprise with Copilot Enterprise against GitLab Premium with Duo Agent Platform, unless otherwise stated. GitLab Duo Agent Platform subject to usage-based pricing.

How do you migrate from GitHub to GitLab?

You don’t have to go all-in on day one. Keep your GitHub repos and run GitLab CI/CD on top, or migrate everything with our built-in importer. Your call.

Flexible migration paths

Keep GitHub repos, add GitLab CI/CD

Lowest lift — mirror repos and run pipelines in GitLab.

Full migration with GitHub importer

Bring your existing work into GitLab with a simple migration tool that handles repos, issues, and PRs, so teams can fully consolidate on one platform. Learn more

Phased rollout?

Pilot GitLab with a few teams, run both platforms in parallel for a limited time, then migrate remaining projects on a planned schedule.

Read the migration guide

Frequently asked questions

Can I keep GitHub repos but use GitLab CI/CD?

How does GitLab pricing compare to GitHub at scale?

Is GitLab good for regulated industries?

How long does migration take?

Is GitLab good for open source projects?

How does project planning functionality compare between GitLab and GitHub?

Can I use GitLab and GitHub together?

What do we need in place before migrating from GitHub to GitLab?

How do GitLab and GitHub compare for DevOps‑heavy workflows?

How does GitLab CI/CD compare to GitHub Actions?

Does GitLab work in air‑gapped or on‑premises environments?

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GitLab vs. GitHub

What are the fundamental differences between GitLab and GitHub?

Platform consolidation

Add-ons required, gaps remain

GitLab is a unified platform

AI & context

Context with code- and repo-centric AI

Full-lifecycle, policy-aware AI context

AI centered on code and repository tasks

GitLab’s AI works across your entire delivery chain

GitHub trains AI models by default for some tiers

GitLab doesn’t use customer code to train models. Period.

Vendor independence

Tied to Microsoft and Azure

GitLab is cloud agnostic

Broad cloud model catalog and BYOM

Flexible model choice on one platform

Cloud-only AI execution

Self-hosted models and agents, including air-gapped

Security & governance

Security requires paid add-ons

Built-in security

Proprietary platform and AI backend

Open core model

Experience the difference

How do GitLab and GitHub's features compare?

CI/CD

AI

Security & compliance

Advanced planning

How do you migrate from GitHub to GitLab?

Flexible migration paths

Keep GitHub repos, add GitLab CI/CD

Full migration with GitHub importer

Phased rollout?

Frequently asked questions

Start building faster today