Self-hosting AI code review is a model-placement decision, not a tool decision

When a team desires a self-hosted AI code reviewer, the true question is where the model operates, not which repository to clone. The tool itself is merely the simple part. The crucial decision-making lies in model placement, which determines the extent of the benefits gained. The current PR-Agent repository on GitHub serves as a clear example of the distinction.

Owned by the PR-Agent organization, donated by Qodo, the README explicitly states that this open-source project is distinct from the Qodo commercial offering. It mentions compatibility with GitHub, GitLab, Bitbucket, Azure DevOps, and Gitea, and deployment options as a GitHub Action, Docker, or a CLI. On the model side, it can route any model reachable through LiteLLM, encompassing local runners like Ollama as well as hosted services from OpenAI, Anthropic, Google, DeepSeek, and Mistral endpoints (PR-Agent repository, checked 2026-09-14).

Thus, the honest framing presents three options, which are fundamentally different from one another. They involve either a hosted model combined with a self-hosted app, or a local model paired with a local app. The reviewer code runs on your infrastructure, while the LLM calls are made over an API. In this scenario, you retain control over the workflow, prompt configuration, and Git platform integration.

Alternatively, you could opt for a local model running on your own hardware through Ollama or a LiteLLM-compatible local endpoint. In this case, the code remains on the machine, representing an air-gapped setup where the term 'private' holds genuine significance. The trade-off is evident: you now manage both the hardware and the model, and the quality of the review is contingent on the local model you choose to run.

In contrast, having the vendor run both the application and the model constitutes a different approach entirely – it is not self-hosted in any sense. This scenario, often encountered with free cloud tiers and most commercial review tools, does not align with the goal of keeping code off third-party infrastructure. A common mistake in the search for the best self-hosted tool is treating these three operations as a single product category.

They represent distinct operations with different failure modes. A team aiming for their next Git push to run a reviewer on internal infrastructure, while being content with using a hosted LLM, would gain nothing by changing their model choice; they merely need to adjust their tooling layer. Conversely, a team seeking to maintain data boundaries must also determine which locally runnable model provides trustworthy reviews.

The tool itself cannot rectify the model deficiencies. Therefore, the primary place to begin the search is the tool's own repository and documentation, as the model-placement question is answered there in source, not in generic listicles.