Hello Devs 👋
AI code review sounds pretty straightforward until you work on a project where the source code cannot leave your company's network.
You might want an AI tool to review pull requests, catch bugs, check coding standards, and help developers fix issues before merging. But if you're working in a regulated industry, handling sensitive intellectual property, or building software inside a restricted network, connecting your repository to an external AI service might not be an option.
This creates a practical problem. You want the benefits of AI code review, but you also need control over where your source code goes, where the model runs, and which services can access your development environment.
I've found that the terminology can make this more confusing than it needs to be. Some tools offer self-hosted deployment, some let you use your own LLM, and some support a completely air-gapped environment. These are different capabilities, and choosing the wrong one can create problems later.
Let's look at what each option actually means and which tools are worth evaluating.
First, what is the difference between self-hosted and air-gapped?
Before comparing tools, I would separate three deployment models.
Self-hosted AI code review means the review service runs on infrastructure you control. That could be your own Kubernetes cluster, private cloud, or internal servers. However, the service might still connect to an external model provider, depending on how it is configured.
Self-hosted LLM support means you can run the model inside your own infrastructure and configure the review tool to use it. This gives you more control over model access and data movement, but you still need to check whether other components send data outside your network.
Air-gapped AI code review is the strictest option. The review engine, model, codebase index, Git integration, and other required services must work within an isolated environment without relying on an internet connection.
Here's a simple way to think about it:
| Deployment | Review service | AI model | Internet access |
|---|---|---|---|
| Hosted SaaS | Vendor infrastructure | Usually external or vendor-managed | Required for service access |
| Self-hosted | Your infrastructure | May be external or internal | Depends on configuration |
| Fully air-gapped | Your isolated infrastructure | Inside your network | Not required for normal operation |
The last row is what matters when your security team says that source code and review data must never leave the environment.
Running a review container on your own servers doesn't automatically make the complete workflow air-gapped.
What should you check before choosing a tool?
I wouldn't start by comparing how many issues each tool finds. For a restricted environment, deployment architecture comes first.
Start with your Git provider. Do you use GitHub Enterprise Server, GitLab Self-Managed, Bitbucket Data Center, Azure DevOps, or Gerrit? The tool needs to support your actual setup, not just its cloud-hosted equivalent.
Next, check the model configuration. Can you use an internally hosted model through a private endpoint, or does the review engine require a particular external provider? If you want to use a self-hosted model, confirm that the product supports it for your deployment edition.
Then look beyond the model itself. Code review tools may need to index repositories, generate embeddings, store review history, authenticate users, and retrieve context from related repositories. You need to know where those components run and whether any of them require outbound connections.
Finally, ask how the system gets installed and updated. An air-gapped environment may need offline installation packages, internally mirrored container images, and a process for importing updates after security review.
These details are not as exciting as an AI-generated review, but they determine whether the tool will actually work in your environment.
1. Qodo: Full on-premises and air-gapped AI code review
Qodo is the first option I would evaluate if the requirement is a complete AI code review platform inside a restricted environment, rather than just a locally running model that comments on a diff.
Qodo 3.0, brings full on-premises and air-gapped deployment to the platform. According to Qodo's announcement, the deployment includes the Context Engine and Agentic Toolbox, so codebase understanding and coding-agent workflows can run within the organization's environment alongside the review platform.
Qodo also supports self-hosted models through an internal model gateway, including supported models such as NVIDIA Nemotron. This matters because a private deployment and a private model are separate requirements. You need both configured correctly if your policy prohibits sending code or prompts to an external model provider.
Qodo documents integrations for self-hosted Git platforms including GitHub Enterprise, GitLab Self-Managed, Bitbucket Data Center, and Gerrit. The exact integration and installation requirements depend on the Git provider and deployment configuration, so I would confirm those details during evaluation.
What makes Qodo 3.0 interesting?
The deployment model is only one part of the story. Qodo 3.0 also focuses on connecting code quality, system context, and engineering governance across the development workflow.
Its Software Map shows relationships between repositories and services. That can help a reviewer understand the potential impact of a change beyond the files in the current pull request. This is particularly useful in larger systems where a shared library or API change can affect several downstream services.
Qodo 3.0 also introduces PR Triage, currently announced as a Research Preview. It groups related pull requests into work packages and provides information such as potential blast radius, review difficulty, and review order. For larger changes involving multiple repositories, that gives reviewers a broader view of the work instead of treating every pull request as an isolated task.
Why the Agentic Toolbox matters
This is the part I would pay particular attention to if your developers are already using Claude Code, Codex, Kiro, or other compatible coding agents.
Qodo's Agentic Toolbox brings codebase context, organizational rules, and independent review into the coding-agent workflow. An agent can retrieve relevant rules and investigate the codebase before making changes, then request a Qodo review of its local changes before opening a pull request.
For example, a developer could ask an agent to update a shared payment library. Before editing the code, the agent can investigate related repositories and historical decisions. After implementation, it can request an independent review and work through the findings.
The important part is that these capabilities are available as part of the platform's agentic workflow, rather than requiring the developer to wait until a pull request reaches a hosted review service.
For a company with strict network controls, the ability to keep the Context Engine and Agentic Toolbox inside the same deployment boundary is particularly relevant.
Qodo is the strongest starting point in this comparison for teams looking for a documented, full-platform air-gapped deployment with self-hosted model support and coding-agent integration. You should still validate the exact model, Git provider, and offline installation requirements against your security team's policies.
2. CodeRabbit: Self-hosted review with your own model configuration
CodeRabbit is another option worth evaluating for organizations that want to run the review service within their infrastructure.
Its documented self-hosted offering runs the review agent inside the customer's environment and supports enterprise Git platforms, including GitHub Enterprise Server, GitLab Self-Managed, Azure DevOps, and Bitbucket Data Center. Its deployment documentation describes using a customer-configured LLM provider.
There is an important qualification here. A self-hosted CodeRabbit review service does not automatically mean that the entire workflow is air-gapped. If the configured model runs outside your network, review prompts and source code used by the model may still cross the network boundary.
That makes CodeRabbit relevant for teams that need control over where the review service runs and want to configure their model provider, but you should verify whether its available deployment and model options meet your specific isolation requirements.
I would also confirm commercial eligibility and deployment prerequisites directly with CodeRabbit, because self-hosted availability is tied to its enterprise offering.
Consider CodeRabbit when you need a self-hosted review service and its supported model configuration matches your infrastructure and security requirements.
3. Greptile: Useful for review, but verify the deployment boundary
Greptile is known for AI code review and codebase-aware analysis. Its ability to understand a repository and review changes is relevant to teams looking for more context than a simple diff-based check.
However, when the requirement is specifically air-gapped deployment, the key question isn't only whether the reviewer understands your code. It's whether the complete review workflow can run without sending source code, prompts, repository context, or other required data to an external service.
I would not assume that a cloud-oriented code review product supports a fully isolated installation just because it offers strong codebase understanding or enterprise security controls.
If you're considering Greptile for a restricted environment, ask the vendor to confirm whether a fully on-premises or air-gapped deployment is available for your use case, whether self-hosted LLMs are supported, and which components require outbound network access.
Greptile is worth investigating for its review capabilities, but treat air-gapped support as an explicit requirement to verify rather than an assumed feature.
4. Build your own review workflow around a local LLM
There is another route: build a lightweight internal review service around a model hosted in your own network.
For example, your team could use a self-hosted model server, a script that extracts pull request diffs, and a review process that returns findings to developers. You could integrate it with an internal Git server or run it as part of your CI pipeline.
A simple architecture might look like this:
Internal Git server
|
v
Diff extraction and context
|
v
Internal review service
|
v
Self-hosted LLM
|
v
Review findings
|
v
Developer or CI pipeline
This can be a reasonable starting point for a proof of concept, especially if you already have infrastructure for running models internally.
The trade-off is that your team owns much more of the engineering work. You'll need to handle repository context, prompt construction, review history, access controls, model upgrades, failure handling, false positives, and integration with developer workflows.
You may also need to build the codebase index and cross-repository context layer yourself. A model that can read a diff is not necessarily able to reason reliably about dependencies that aren't included in its input.
For a small team experimenting with local models, that might be acceptable. For a large organization that needs consistent review and governance across many repositories, the maintenance cost can become significant.
Which option should you choose?
The right choice depends on how strict your environment is and how much infrastructure your team wants to own.
| Your requirement | What I would evaluate first |
|---|---|
| Full platform inside an isolated network | Qodo 3.0 |
| Air-gapped review with an internally hosted model | Qodo 3.0, validating model and installation requirements |
| Self-hosted review service with a configurable model provider | Qodo and CodeRabbit |
| A review workflow built around your own local model | An internal solution, with ongoing maintenance factored in |
| Strong code review but unclear isolation requirements | Confirm deployment and data-flow details before selecting any tool |
I would not choose based on a feature checklist alone. Two products may both say "self-hosted" while making very different assumptions about model access, indexing, authentication, telemetry, and updates.
Ask vendors to walk through the actual architecture and data flow for your environment. If possible, run a proof of concept with outbound access blocked and confirm that the review process still works as expected.
A practical checklist for your proof of concept
Before approving an AI code review tool for a restricted network, I would test the following:
- The Git provider is supported in its self-managed form.
- The review service runs inside the required network boundary.
- The selected LLM runs locally or through an approved internal model gateway.
- Repository indexing, embeddings, and codebase context remain inside the boundary.
- Authentication and authorization use approved internal systems.
- The tool can operate without unexpected outbound connections.
- Installation and updates can be handled through your approved process.
- Review findings are useful on real pull requests, not just synthetic examples.
- The tool can access the context needed for cross-repository changes.
- Security teams can inspect logs, permissions, data retention, and model configuration.
One useful test is to disconnect the environment from external networks after installation and try a normal review. If the tool stops working because a hidden dependency needs an external API, you've found an important issue before production rollout.
Final thoughts
Air-gapped AI code review is not simply about running an LLM on a local server. The review engine, repository context, indexing, Git integration, and supporting services all need to fit within your security boundary.
That is why Qodo 3.0 is particularly relevant to this discussion. Its announced support for full on-premises and air-gapped deployment includes the Context Engine and Agentic Toolbox, while self-hosted model support lets organizations run reviews through an internal model gateway. Its Software Map and PR Triage features also address the broader challenge of understanding how changes affect a connected system.
The Agentic Toolbox adds another useful dimension: developers can bring organizational rules and independent code review into the coding-agent workflow, rather than relying only on a review that happens after a pull request is opened.
CodeRabbit offers a self-hosted review option worth investigating, while Greptile should be evaluated against the specific deployment requirements rather than assumed to be air-gap compatible. Building your own local review workflow is possible too, but it means owning the context, integration, and maintenance work.
For me, the most important question to ask any vendor is simple:
Can the complete review workflow run inside our security boundary, using a model we control, without depending on an external service?
Get that answer first. Then compare review quality, developer experience, and the features that will make the tool useful to your team.
👨💻 TL;DR
- Qodo: The first option I would evaluate for a full air-gapped platform, self-hosted models, codebase context, and agentic review workflows.
- CodeRabbit: Offers self-hosted review, but confirm the model configuration and whether it meets your air-gap requirements.
- Greptile: Review capabilities may be relevant, but verify availability of a fully isolated deployment and self-hosted LLM support directly.
- Build your own: Gives you control over the stack, but your team owns integration, context retrieval, maintenance, and reliability.
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