Best AI Code Review Tools for Air-Gapped and Self-Hosted Environments (2026)

Air-gapped AI code review means the whole review, model calls included, runs with nothing crossing your network boundary. I design review pipelines for teams whose code can't leave the building, and I checked five tools against their own docs on October 27 and 28, 2026, ranking them on what a review sends over the wire.

Vendors use "self-hosted" and "air-gapped" as if they were one word, and procurement teams pay for the confusion later. The difference comes down to what crosses your network boundary during a review. Air-gapped code review means nothing does.

Coding agents moved inside the firewall in 2026 too, and IBM added on-premises, sovereign cloud and fully air-gapped deployment for its Bob coding agent (IBM, October 1, 2026). Reviewers now get held to the same bar.

Teams in regulated sectors often start from compliance rules, and the glossary entry on HIPAA and FedRAMP compliance covers why auditors ask about data paths first. The mode you need follows from the data path your auditors accept.

Most IT decision-makers would pick on-premises, private or hybrid infrastructure for AI on sensitive data, in a survey commissioned by storage vendor Cloudian (Cloudian, March 2026). Switching later is hard, and most executives say changing AI vendor or model would be difficult (IBM Institute for Business Value, June 2026).

Banks, defense contractors and hospital networks I've worked with all apply the same five checks. I use them as a gate, in order, because a tool that fails the first one never reaches the others.

Teams on GitLab Self-Managed usually feel the Git check first, because many reviewers document only cloud Git platforms. Teams on Bitbucket Data Center and Gerrit feel it even sooner, since fewer vendors list either one.

Our roundup of secure code review tools covers the wider category. The air-gapped list here is narrower, because most security reviewers still assume a cloud model.

You can run AI code review with self-hosted LLMs as long as the reviewer accepts an OpenAI-compatible endpoint and you can serve a model behind one. Most self-hosted reviewers in 2026 speak that API shape, so a model served through vLLM, Ollama or an internal gateway can sit behind the reviewer without code changes.

Local models are good enough to test. A Monash and Melbourne paper ran agentic secure code review on local open-weight models and matched an earlier agentic approach that used a hosted frontier model ( arXiv 2610.27383, October 2026). Every approach scored low in absolute terms, so treat the result as parity rather than a solved problem.

The authors frame the motivation in words any security lead will recognize, writing that "source code is confidential and cannot be sent to an externally hosted commercial model" (arXiv 2610.27383, October 2026). Air-gapped teams have said the same for years, and the paper gives them a tested way to act on it.

The model choice stays yours, and so does the evaluation. Before a pilot, I run the reviewer against a few dozen merged PRs with known bugs. I compare findings across two or three candidate models, using the steps in our AI code review POC.

Zero data retention is a promise from a vendor that your code won't be stored after processing. Your code still leaves the network to get processed, and you trust a contract for what happens next.

Zero egress means the code never leaves at all. Security teams in air-gapped sites treat retention promises as irrelevant, because the question they ask is about the wire, not the vendor's disk.

The practical setup for zero egress is an internal AI gateway that fronts your models, logs every call and enforces which models each service may use. The reviewer then points at the gateway and never learns a public hostname.

I prove zero egress before I trust it. Apply a deny-all egress policy to the reviewer's namespace, allow only the gateway, Git server and database, and run a week of real reviews. Any blocked connection is a finding for the vendor, and a clean week is evidence for the auditors.

Air-gapped systems age in place. Every component that a SaaS reviewer refreshes silently now needs an owner on your side.

Legal and security teams I work with want a written account of what leaves the environment before any pilot, and they ask early who carries upgrades. Put both answers in the pilot plan, with a named owner for models, rules, indexes and images.

I read each vendor's own deployment, networking and model-provider docs on October 27 and 28, 2026, and scored each tool against the five checks. "Not documented" means I couldn't find the answer in public docs, not that the feature is missing.

Qodo is a code review platform that runs as multi-tenant SaaS, single-tenant, on-premises or air-gapped. Qodo supports air-gapped installs on GitHub Enterprise, GitLab Self-Managed, Bitbucket Data Center and Gerrit, the four self-hosted platforms I see at regulated companies. Qodo is the reviewer I'd pick for teams on self-hosted Git that need review with zero egress.

The Helm values that matter for egress sit under global.llm. Leave openaiBaseUrl empty and the gateway defaults to api.openai.com, which needs internet access, so the first job is to fill it in.

global: llm: bundled: true # the in-cluster gateway stays on openaiBaseUrl: https://llm-gateway.internal.example/v1 # modelMap: replace the built-in model list with the models your endpoint serves; it must include a text-embedding-3 model for indexing and retrieval imageProxyPrefix: registry.internal.example/qodo/ Enter fullscreen mode Exit fullscreen mode Credentials for the endpoint belong in a Kubernetes Secret, never in extraHeaders. The model map also needs an embedding model, because indexing and retrieval depend on one.

Mirror every image listed for your chart version, keeping paths and tags identical, then run Qodo's render check, which greps the rendered chart for any image outside your registry. A partial mirror fails later with ImagePullBackOff, so the check matters.

Size node counts and storage with Qodo's team before you order hardware. Telemetry is off by default, which saves one conversation with your security team.

Qodo is best for platform and security teams that need one reviewer across several self-hosted Git platforms with nothing leaving the network. Pick Qodo when auditors need every model call to stay on your own endpoint.

Qodo passed all five checks. Its air-gapped install covers every self-hosted Git platform on my list, organization rules and findings apply across repositories, and the egress story rests on your own endpoint. Qodo is my first pick for regulated teams running GitHub Enterprise, GitLab Self-Managed, Bitbucket Data Center or Gerrit.

Greptile is an AI code reviewer that indexes your codebase and comments on PRs. The entire service can be self-hosted in an air-gapped environment, deployed with Docker Compose or Kubernetes and Helm (Greptile self-hosting docs).

Greptile is best for teams on GitHub Enterprise, GitLab or Perforce that want a self-hosted reviewer with codebase indexing.

Greptile passes the egress check on its own docs. Bitbucket Data Center and Gerrit don't appear in the self-hosted SCM table, and I found no documented governance across repos, so Greptile ranks second for organizations that run more than GitHub and GitLab.

Tabnine is a coding assistant with code review features. An Enterprise private installation runs in a VPC or on-premises and can be deployed in a completely air-gapped environment (Tabnine deployment docs). Tricentis acquired Tabnine on July 30, 2026.

Tabnine is best for air-gapped teams that want review and code completion from one private install.

Tabnine's air-gapped story is documented, but the review path runs through a CLI that is in maintenance mode, with only critical updates through December 31, 2026, as users move to the Tabnine plugin for OpenCode.

The reviewer also comes from the same vendor as the assistant writing the code. You shouldn't review code with the same tool that wrote it, because the reviewer shares the writer's blind spots.

SonarQube Server is self-managed static analysis that checks code against rules and quality gates. SonarQube Server 2026.5 integrates with GitHub, Bitbucket, GitLab and Azure DevOps.

SonarQube Server is best for teams that want deterministic rules and quality gates inside the network, with AI used for fixes.

SonarQube Server passes the egress check. Its review is rules-based static analysis, so it pairs with an LLM reviewer rather than replacing one.

The DIY route wires an open-source review CLI to a local model behind an OpenAI-compatible API, triggered by a webhook or a CI job on each PR. The tools in our earlier roundup of open-source code review tools all support that pattern.

A DIY reviewer is best for teams with a strong platform engineering group that already runs internal model serving.

A DIY build passes the egress check by construction. Self-hosted Git support, offline install, context, governance and independence all depend on how much you build and keep running, which is why the DIY option ranks last for most teams.

Air-gapped AI code review comes down to whether the review can run with nothing crossing the boundary and still understand the code.

Sort the deployment modes first, then hold every tool to zero egress against your own endpoint, and only then compare context and governance. Qodo cleared every check, with air-gapped installs on all four self-hosted Git platforms, as a reviewer separate from the agents writing the code. Start with Qodo when zero egress is a requirement.

AI code review can run with zero internet egress when the reviewer forwards model calls to an endpoint inside your network and installs from a mirrored bundle. Qodo documents that setup for air-gapped installs, with no outbound access to api.openai.com once your own endpoint is configured.

You supply the model endpoint, the Kubernetes cluster, a PostgreSQL database with pgvector, an image registry for the mirror and an identity provider. Sizing depends on your repositories, so confirm production capacity with the vendor. Qodo's air-gapped install uses exactly that list, plus a chart bundle you mirror.

In Qodo's deployment table, GitHub Enterprise, GitLab Self-Managed, Bitbucket Data Center and Gerrit support air-gapped installs. Cloud-hosted platforms such as GitHub Cloud and GitLab Cloud mark air-gapped as not applicable, and Bitbucket Cloud and Azure DevOps Cloud run as cloud deployments.

You don't need OpenAI or Anthropic access if you bring your own endpoint. Qodo's bundled gateway forwards to the OpenAI-compatible endpoint you configure, so fill in the endpoint before the first review, because an empty setting defaults to api.openai.com.

You mirror every image for your chart version into an internal registry and point the chart at it. Qodo ships each release as a chart bundle and image list, and its render check prints nothing when every image comes from your registry.

Your platform team handles upgrades, with new chart bundles and image mirrors for each release. Name an owner for models, rules, indexes and images before the pilot starts. Qodo ships each release as a chart bundle with a matching image list, so each upgrade follows the same mirror steps.