Yes, you can self-host an AI code review tool. But running the review application on your own infrastructure does not decide where the model runs or where your code goes. Those are separate choices. Most of the real trade-offs come from the second one: whether the model that reads your diffs is a server you operate on your network, or an external endpoint that receives the review context.
Two placements that teams bundle together
A code review deployment has two placements, and each can be set independently:
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- Application placement is where the review service runs: a server or cluster you control, or a vendor-hosted service. It determines who operates the service that receives pull request events, assembles review context and posts comments.
- Model placement is where inference happens: a model server on your network (local or on-prem), or an external endpoint the tool calls.
Proval documents configurable local and external compatible endpoints, and Mira lists multiple provider and endpoint choices. Because both products expose endpoint configuration rather than one fixed path, that setting is the thing to check before assuming where your code is processed.
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- Self-hosted application with a local model: the most contained arrangement, and the heaviest to run.
- Self-hosted application with an external endpoint: you control the service, but model requests leave your network.
- Vendor-hosted application with an external model: the least control over the data path, and the least operating work.
- Vendor-hosted application calling a model you run: this requires the vendor’s service to reach an endpoint you expose. Confirm that connection and its network exposure before assuming it works.
What “self-hosted” does and does not guarantee
Hosting the application does not keep data on your network. The model request carries the diff and any repository context the tool attaches, and other systems may also receive or store pieces of the codebase. Check each of these:
- The model request: diff hunks, surrounding file context, and any repository context the tool adds.
- Embeddings or indexes built from the codebase, and where they are stored.
- Application logs and telemetry, including whether prompts or model responses are written to them.
- Webhooks and any relay or queue between your code host and the review service.
- Backups and snapshots of the review service’s data volumes.
- Third-party services the application calls for authentication, notifications or analytics.
Proval’s FAQ states the placement choice directly: “Use a local model if you need to keep everything on your network.” Read that as the vendor’s description of how its endpoint configuration works. It is not a guarantee about every other service in a deployment, and it says nothing about how a different review product handles the same data.
The two model placements side by side
| Decision axis | Local or on-prem model | Configured external endpoint |
|---|---|---|
| Data path and control | Inference stays on infrastructure your team operates. Logs, telemetry, embeddings, webhooks, backups and connected services still need checking. | Review context is sent to the configured endpoint. Provider retention, training use, data residency and contract terms need review. No universal provider policy is established here. |
| Model choice | Limited by what your runtime supports, which models you can host, your hardware, and your own evaluation. | May offer a broader model catalog, depending on the review tool and endpoint. Verify current support for the model you need. |
| Review quality | Must be measured on your repositories and review tasks. Being local does not establish quality. | Must be measured the same way. Being hosted does not establish quality. |
| Latency and capacity | Depends on hardware, model size, context length, concurrency and serving configuration. No universal threshold applies. | Depends on the provider, network path, model, service limits, context length and workload. No cross-provider figure is available. |
| Cost structure | Hardware or reserved GPU capacity, power, utilization, runtime and model maintenance, and engineering time. No reliable break-even figure exists. | Model charges that scale with request volume, plus any hosting or gateway fees. Published per-review estimates are product-specific. |
| Operations and security | Your team restricts access, protects credentials, monitors resource use and handles updates. | You assess provider access controls, retention terms and service dependencies. |
Quality has to be measured on your pull requests
Whether a model runs locally or behind an API says nothing about how many real problems it catches or how much noise it produces. The published quality figures in this area are vendor-reported. Mira’s project page reports its own offline benchmark of 50 pull requests, with Claude Sonnet 4.6 as the judge. Mira reports an F1 score of 44, precision of 43% and recall of 46%, with a median review time of about 77 seconds per pull request. The page also compares selected competitor tools, which score differently and take longer. Those numbers are not reproduced here.
Read the figures with their method attached. Under that judge, a little under half of the findings Mira raised were counted as correct, and it caught about 46% of the issues in its reference set. The dataset is bounded, the judge is a model rather than a human reviewer, and the vendor ran the benchmark. None of that shows how a model will perform on your codebase. The available evidence also does not isolate model placement as a variable, so it gives no reliable basis for ranking a local model against a hosted one on review quality.
Run your own evaluation before choosing
- Select already-reviewed pull requests from the repositories the tool will cover. Include a mix of bug fixes, refactors, dependency changes and changes a human reviewer flagged. A few dozen is a practical starting size for a first pass.
- Build a reference set from the findings reviewers acted on, and note issues that were later found in QA or production as misses.
- Freeze the review context: the same diff, prompt configuration and repository context for every candidate.
- Run each candidate configuration, for example a local model at two sizes and one external endpoint, and log every comment it posts.
- Score each comment as actionable, noise or incorrect, count misses against the reference set, and time each review from start to finish.
- Compare cost per review using the same volume assumptions for every option.
This is a method to adopt, not a result this article reports. Use the scores to set a quality bar your team can defend, then compare only the candidates that clear it.
Cost: separate model charges from the cost of running the model
The published prices cited here come from GitHub, for its Copilot code review feature. GitHub’s documentation says Copilot code review uses AI credits for model interaction and GitHub Actions minutes for agentic context gathering and tool use. Its estimate is $0.05–$1 in AI credits for a typical Lite review and $0.25–$5 for Balanced effort, excluding Actions minutes. GitHub says the estimates may change. These figures describe that product’s pricing model. They are not a general estimate for self-hosted review and should not be used as one.
Self-hosting changes the shape of the bill rather than removing it. Build the comparison from both sides:
- Local or on-prem: hardware or reserved cloud GPU capacity, power and cooling where applicable, idle time, runtime and model upgrades, monitoring, and the engineering time needed to keep it running.
- External endpoint: model charges per request or token, multiplied by expected review volume, plus any hosting or gateway fees you add.
Compare both at your expected monthly review count. No reliable break-even figure exists across hardware, models and workloads, so the crossover depends on your volume and on how much operations time you already carry. A practical test: divide the fixed monthly cost of the local stack by your monthly review count, and compare the result with the external per-review charge, but only for candidates that meet the same quality bar.
Latency and capacity depend on your setup
No universal latency target holds across hardware, model sizes and workloads. Mira’s median of about 77 seconds per pull request is its own measurement on its benchmark setup, not a target to adopt. Measure what developers experience instead: the time from a pull request opening or receiving a push to the review comment appearing.
For a local runtime, the main variables are hardware, model size, context length and concurrency. Ollama’s documentation lists supported GPU hardware, but it does not name a minimum GPU for code review work, so size hardware from your own model and context needs. Then load-test at the number of simultaneous pull requests you expect, including bursts, such as a merge window when many branches update at once.
For an external endpoint, latency includes the network path, provider queueing and any service limits. You can see those effects only by testing from your own network.
Running a local endpoint safely
A local model server is production infrastructure. vLLM’s documentation covers authentication scope and risks including resource exhaustion and access to the cache directory. Use those as the starting list for your threat model:
- Keep the endpoint on a private network segment that only the review service can reach. Do not publish it to the internet.
- Enable authentication and confirm exactly what it covers. Check whether every route, including management or metadata endpoints, falls under the same control.
- Cap concurrency, request size and output length so a burst of large diffs cannot starve the GPU or exhaust memory.
- Restrict read and write access to the model cache and any directory the runtime writes to, since these hold model files and runtime state.
- Store the credential the review application uses in a secrets manager, scope it to this endpoint, and rotate it on a schedule.
- Monitor GPU memory, queue depth, error rates and request latency, and alert on sustained saturation.
- Test runtime and model updates against your evaluation set before rolling them out, as you would any dependency.
With an external endpoint, the security work moves to the provider relationship: how your API keys are controlled, the provider’s retention and training-use terms, data residency, and what happens to your data when you leave. A vendor’s privacy statement describes its practices but is not a legal conclusion. Obligations that depend on jurisdiction or sector should be reviewed with counsel.
A decision sequence
- Classify the code. Decide which repositories may send diffs and context to a third party. If none may, only a local or on-prem model is in scope.
- Confirm the data path. For each candidate, check the application’s endpoint configuration and every connected system listed above.
- Run the evaluation set against each in-scope model, using identical context.
- Set your quality bar and latency budget from your team’s needs, and keep only candidates that meet both.
- Compare total cost at expected volume, keeping model charges separate from operations.
- Operate a self-run model as production infrastructure, and review provider terms for any external endpoint.
Nothing in the available evidence shows that an AI reviewer should replace human review. Keep merge decisions under your team’s controls and treat the tool’s comments as input to a human reviewer, not as an approval gate.
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