Self-hosted AI code review puts the review application in infrastructure your team controls. That can help with source-code privacy, network boundaries, data residency, and model choice.
It does not automatically keep code private. If the application sends prompts to an external model API, that provider still receives part of the repository data. The useful question is not only where the reviewer runs. It is where every piece of the review data goes.
What self-hosted AI code review means
When you self-host a reviewer, your team runs the application in its own cloud account, VPC, cluster, or data center. Your systems handle deployment, upgrades, logs, access control, and capacity.
When a pull request arrives, the reviewer usually:
- receives a webhook from GitHub
- fetches the diff and selected repository files
- builds a prompt or review request
- sends that request to a model
- stores findings or posts them back to GitHub
Self-hosting gives you control over the first, second, and fifth steps. It gives you control over model inference only when the model also runs inside an environment you control.
What self-hosting can protect
A defined data path
You can inspect which services receive repository data, where those services run, how long they keep it, and which logs contain prompts or findings. That makes a security review concrete.
Network and access boundaries
The application can run in a private network and use credentials that your team manages. You can limit outbound traffic, restrict repository access, and connect the reviewer to internal systems without exposing those systems to a hosted vendor.
Model and provider choice
You can use a commercial provider, an internal gateway, or a local model. You can route different repositories to different providers when their data or quality requirements differ.
Long-term control
You can read the source, pin a version, patch behavior, and keep operating the system if a hosted product changes its pricing or removes a feature.
What self-hosting does not solve by itself
Running the application in your VPC does not make an external model API part of your network. The provider may still receive repository content, prompts, metadata, and generated findings.
Self-hosting also does not remove operational work. Someone must handle:
- upgrades and security fixes
- GitHub App credentials and webhook delivery
- queues, retries, and duplicate reviews
- model keys, budgets, and provider outages
- logs, retention, and access reviews
- capacity when several large pull requests arrive together
If your team does not want to own those tasks, a hosted service may be the better choice. The right answer can differ by repository. A public library and a regulated production service do not need the same deployment model.
Who should consider it
Self-hosting is worth a serious look when:
- source code cannot go to an unapproved service
- a customer or regulator requires a specific region or network boundary
- the team needs a local model or an approved model gateway
- the organization wants to audit or modify the review pipeline
- review volume makes model and infrastructure costs worth operating directly
It is usually a poor trade when the team has no owner for deployment, monitoring, upgrades, or incident response.
What to check before choosing a tool
Ask the vendor or inspect the source for clear answers to these questions:
- Which GitHub repositories can the reviewer read?
- Does it fetch only the diff, or can it inspect relevant surrounding code?
- Which service receives the prompt and model response?
- Are prompts, files, findings, and logs stored? For how long?
- Can the model provider be changed or hosted locally?
- How are webhook retries and duplicate pull-request events handled?
- Can the reviewer apply repository-specific rules?
- How do you upgrade it without losing configuration or history?
The last two questions are easy to skip. They are also where a self-hosted deployment becomes useful or turns into another system your team has to work around.
Where Scopy AI fits
Scopy AI is an open-source AI code reviewer. You can run it in the cloud or self-host it using the project documentation. The self-hosted deployment can connect to a compatible model provider, so the application and the model path can be considered separately.
If you need code and inference to remain inside your environment, the model must run there too. If an external model provider is acceptable, review its retention, training, region, and access terms as part of the same data-flow review.
For the cloud-versus-self-hosted decision, read self-hosted vs. cloud AI code review. For a provider-account comparison, see what BYOK protects and what it does not.
Frequently asked questions
Does self-hosted AI code review mean no code leaves the network?
No. That depends on the model path. A self-hosted application can still send prompts to an external model API.
Is self-hosting more secure than a hosted service?
It gives your team more control, not an automatic security result. You take responsibility for updates, credentials, access, logs, and the model provider.
Can a self-hosted reviewer use a commercial model?
Yes. The application can run in your environment while sending requests to a commercial provider. Decide whether that data path fits your requirements.
Can I start in the cloud and self-host later?
With a tool that supports both deployments, yes. Check whether the two modes use the same review rules, repository context, model settings, and findings workflow before relying on that migration path.