Whether your infrastructure is self-hosted or cloud-first, engineering teams are running into the same bottleneck: you can't review code fast enough to keep up with the volume of code you're now writing with coding agents.
For self-hosted teams, that bottleneck comes with extra constraints. You can't just plug in a cloud-based AI code review tool and call it a day. Self-hosted code lives behind a firewall for a reason, and sending it to a third-party cloud for review defeats the purpose of self-hosting.
Large, highly regulated teams that self-host also have other things to factor in:
- Strict regulatory and compliance frameworks. If your code review tool can't help enforce them, doing it by hand across large, complex repos is a nightmare.
- Traceability. Reviews need to connect back to the tickets in Jira or Linear that justified the change, or you'll be reconstructing that trail later.
- Legacy code and third-party dependencies. Outdated components and misused third-party APIs are especially risky for regulated teams, and standard review processes often miss them.
- Complex, messy codebases. Large monorepos and code that's been around for a decade or more make review harder. You're not just reviewing new code. You also have to think about how it interacts with years of code that came before it.
In this guide, we'll break down how self-hosted teams can bring AI code review into a self-hosted GitLab or GitHub Enterprise environment, and how to scale review speed without giving up control.
What is self-hosted AI code review?
Self-hosted AI code review is AI-powered code analysis that runs entirely within your private, self-managed infrastructure. Organizations self-host their developer tools for a variety of reasons: data privacy, regulatory compliance, cost, native platform integration, and control over which models touch their code.
Teams can choose which tools to self-host and which to run in the cloud. But self-hosting your source code repository (with GitHub Enterprise Server, GitLab Self-Managed, or self-hosted Bitbucket, for example) usually sets a strict security baseline. If adopting a third-party cloud AI tool would break that baseline, you need a self-hosted AI code review tool instead.
Self-hosted vs. SaaS AI code review
Most AI code review tools are cloud-based SaaS products. For enterprise teams bound by strict data privacy or compliance requirements, that's often a non-starter.
Self-hosted options bring AI code review to you. Instead of sending your code to a vendor's cloud, you run the review service inside your own network and choose where the model runs.
Here are the core differences:
| Self-hosted AI code review | SaaS AI code review | |
|---|---|---|
| Where code lives | Stays in your environment: on-prem, private cloud, or VPC | Sent to and stored in the vendor's cloud |
| Setup | Deployed in your infrastructure, connected to your code host by webhook | App install or OAuth with repository permissions |
| Compliance and IP security | You control the data path, which makes air-gapped and data residency requirements easier to meet | Relies on the vendor's security posture and data agreements |
| Maintenance and control | Owned by your DevOps or platform engineering team | Managed and updated by the vendor |
| Models | You choose the model provider, or host the model yourself | The vendor picks the models |
Hosting your code and hosting your review tool are separate choices, though. Some teams on GitHub Enterprise Server, GitLab Self-Managed, or self-hosted Bitbucket use a cloud review tool that can reach their instance through an IP allowlist or reverse proxy. That works if your policies allow code to leave your network. If they don't, you need the review tool inside your perimeter too.
The problem with SaaS AI code review tools
For teams with strict data requirements, cloud-hosted AI code review tools raise a few operational risks:
- Source code exposure. Sending proprietary code to a third-party cloud widens your attack surface. If that vendor has a breach, your code is part of it.
- Data retention and training. What happens to your pull request data after review depends on the vendor's retention and training policies. You have to read them closely and trust that they're followed.
- Compliance overhead. Moving sensitive data across third-party boundaries adds work for SOC 2, HIPAA, GDPR, and similar frameworks: more vendors to assess, more data flows to document.
- Model dependency. A SaaS tool chooses its models and rate limits for you. Model changes, outages, and pricing changes all happen on the vendor's schedule.
The benefits of self-hosted AI code review
If SaaS code review tools aren't a fit for your team, self-hosting might be. Here's why teams choose it:
- Source code privacy. Your code stays in your private cloud or on-prem infrastructure. Indexes, embeddings, and logs live in databases you provision and control.
- Bring your own model. Use your own API keys with the provider you already have an agreement with, or point the tool at a model endpoint you host. You aren't tied to one vendor's model choices.
- Simpler compliance. When code stays inside your network, it's easier to satisfy data governance requirements, and easier to prove it.
- Your identity provider. Sign-in can run through your existing SAML identity provider instead of a separate set of vendor accounts.
- Control over upgrades. You schedule new versions within the vendor's support window, so upgrades fit your change management process.
How AI code review works for self-hosted GitLab and GitHub Enterprise
All self-hosted tools share the same basic principle of local deployment, but the details depend on the tool and your stack.
Here's how Greptile users run AI code review on self-hosted GitLab and GitHub Enterprise:
- Greptile deploys onto your own compute with Docker Compose or Kubernetes. It stores its codebase index in PostgreSQL with pgvector (bundled, or your own managed database), alongside a Redis cache.
- When someone opens a merge request or pull request, GitLab, GitHub Enterprise, or Bitbucket sends a webhook to your internal Greptile instance.
- Greptile reviews the diff against its index of your full codebase. Your code stays on your servers. Review traffic goes to your code host and to the model endpoint you configure, so if both are inside your network, your code never leaves it. (Greptile also pulls container images from a registry. See network paths for the full list.)
- Greptile reasons over dependencies and context using the models you choose: Anthropic, OpenAI, Azure OpenAI, AWS Bedrock, or any OpenAI-compatible endpoint.
- Greptile posts its review as comments directly in the merge request or pull request, where your team already works.
Self-hosted Greptile runs on AWS, GCP, Azure, on-prem hardware, and air-gapped environments. Read more about Greptile's deployment options.
The same setup works for self-hosted Bitbucket and Gitea through Greptile Enterprise, and on-premises deployments also support Perforce through P4 Code Review. If you're on Bitbucket Cloud, Greptile connects on every plan. See the full list of supported code providers.
What self-hosted teams need to look for in AI code review tools
Running inside a private network raises the bar for security, performance, and control. Here's what to look for as you evaluate self-hosted AI code review tools (and our general framework for evaluating code review tools covers the rest):
- Fully private deployment. The tool should run inside your infrastructure with a containerized setup like Docker Compose or Kubernetes. A vendor-hosted "private" cloud isn't the same thing. Greptile can run entirely self-hosted, including in an air-gapped environment, using Docker Compose or Kubernetes.
- Model access you control. Your code goes wherever the model runs. Look for a tool that lets you use a provider inside your own cloud account (like AWS Bedrock or Azure OpenAI) or a model you host yourself, so prompts and responses stay within boundaries you've approved.
- Air-gapped support. If your environment has no internet access, the tool has to run reviews without any external calls, with images, models, and updates all delivered inside your network.
- Enterprise-grade security. Look for a vendor with SOC 2 Type II, plus HIPAA and GDPR compliance if those apply to you. Greptile is SOC 2 Type II compliant, and also HIPAA and GDPR compliant.
- Full codebase context. AI needs to see the whole repository to understand how a change affects code around it. Greptile builds a graph of your codebase and a knowledge base of how each part works and connects, so it can check cross-file dependencies before commenting.
- Low noise. Nothing kills AI review adoption faster than noisy comments. Greptile uses parallel review agents, each with a narrow focus, to hunt for real bugs and keep comments short and specific.
- Learning over time. A review tool should get better as your team uses it. Greptile learns from your team's feedback to stop repeating nits your developers ignore, so you don't have to keep re-teaching your conventions.
- Ongoing support. Self-hosted deployments are complex. You need help from the vendor's engineers and a predictable upgrade path. Greptile's self-hosted releases are never more than 30 days behind the cloud release, and our engineering team works with you on deployment.
Planning a self-hosted rollout
If you already self-host GitHub Enterprise Server, GitLab Self-Managed, or Bitbucket, most of the work in adopting AI code review isn't picking a tool. It's fitting the tool into an environment with strict network, change management, and security rules. Here's what to plan for, using Greptile's requirements as a reference point:
- Deployment method and sizing. Docker Compose runs everything on a single Linux VM and fits teams up to about 100 developers (roughly 8 cores and 32GB of RAM for 50 developers). Kubernetes with Helm is the better fit above 100 developers, or when you need high availability and horizontal scaling.
- Network paths. Your code host has to be able to reach Greptile's webhook endpoint, and Greptile has to be able to reach your code host's API. If your model runs outside your network, Greptile also needs outbound HTTPS to that provider, and pulling images needs access to Greptile's container registry. In an air-gapped install, models and images are delivered inside your network instead.
- Models. A self-hosted install needs three kinds of model: a reasoning model for reviews, a fast model for summaries, and an embedding model for indexing. Decide early whether those come from a cloud provider under your own account or from endpoints you host.
- Cluster security policies. On Kubernetes, the worker that sandboxes reviews runs privileged, so clusters with restrictive Pod Security Standards need an exception. Raise this with your platform security team early.
- Images and upgrades. Greptile provides container registry credentials and new image versions on a regular cadence. If your environment can't pull from an external registry, plan how images get into it.
- Identity. SAML SSO is available as an optional service, so sign-in can run through your existing identity provider.
- Local reviews. Developers can run the Greptile CLI against your self-hosted instance with an API key, for GitHub and GitLab repositories that are already onboarded.
Self-hosting Greptile requires an Enterprise license. Talk to our team about sizing and setup for your environment.
How Greptile handles validation, security, and trust for self-hosted teams
SaaS AI code review tools ask regulated teams to make a trade: validate code quickly, but send your codebase to a third-party cloud. You shouldn't have to choose between catching critical bugs and protecting your IP.
Self-hosted AI code review removes that trade by bringing the review inside your network perimeter. You get automated pull request review and bug detection without giving up data governance.
Self-hosted Greptile is built for this. It runs with Docker Compose or Kubernetes in your VPC, data center, or air-gapped environment. And it covers some of the biggest problems self-hosted teams face:
- Custom rules help you enforce compliance requirements. For example, you can have Greptile check that all user inputs are validated, SQL queries use parameterized statements, admin functions include role-based access control checks, and access to personal data is logged for audit purposes.
- Jira and Linear integrations tie reviews to tickets. Greptile finds the ticket or issue linked to a pull request, reviews the code against its requirements, and links what it read in its comments.
- Partner context covers third-party APIs. Where your code touches a partner's API or SDK, Greptile reviews it against guidance from that partner: their docs, implementation rules, and common failure patterns.
Integrations that call external services depend on what your network allows, and self-hosted releases can trail cloud by up to 30 days. Confirm which features you need with our team during setup.
Greptile is also built for complex codebases, monorepo or not. It indexes your entire codebase locally to evaluate cross-file dependencies, so it can catch complex bugs without sending code off-site. And it's agent-agnostic: it works independently of whichever coding agent wrote the code, so the tool that wrote a change isn't the one grading it. On a self-hosted install, you also choose which provider's models it runs on.
Teams using Greptile merge PRs up to 4x faster while catching 3x more bugs.
NVIDIA runs Greptile as a self-hosted deployment across Perforce and GitLab. Across that deployment, Greptile has reviewed roughly 395,000 pull requests, and one team's average time to merge went from more than 24 hours to six.
“Code reviews happen very quickly because Greptile is the first code reviewer. We're spending a lot less time doing code reviews, and are much more productive in shipping code.
”
Brex, on GitHub Enterprise Server, catches ~8,200 issues per month with Greptile across a nearly decade-old monorepo and 400+ engineers. Greptile learned the monorepo's boundaries, call paths, contracts, and test topology, and uses PR diagrams to show exactly what a change touches and where risk clusters.
“We started out with a pilot. Developers here were skeptical at first, since we had tried a few other products with relatively little success, but were pleasantly surprised by how many issues Greptile was catching out of the box. Now, Greptile reviews 100% of the code we write across Brex.
”
Comparing tools for your code host? See our roundups of the best AI code review tools for GitLab and the best AI code review tools for GitHub.
For teams that need security, control, and compliance, Greptile makes AI code review work inside your environment. Talk to us about self-hosted Greptile, or try Greptile Cloud free for 14 days to see the reviews for yourself →