Choosing between self-hosted and cloud AI code review is not a contest between “secure” and “easy”. Both can be run securely, and both can become awkward when chosen for the wrong reason.
The actual decision is about control. Who operates the review service? Where is repository data processed? Which model providers can receive it? Who handles upgrades, scaling, logs, encryption and incident response?
This guide compares the two deployment models so the trade-off is explicit instead of being decided by whichever option was easiest to trial.
The short answer
Choose cloud AI code review when you want fast setup, minimal operations and predictable product support, and your organization permits source code to be processed by the selected vendors under acceptable data terms.
Choose self-hosted AI code review when infrastructure control, network isolation, data residency, custom model routing or auditability outweigh the cost of operating the service yourself.
Choose a tool that supports both when requirements may change or different repositories need different controls.
Side-by-side comparison
| Decision area | Cloud | Self-hosted |
|---|---|---|
| Setup | Usually minutes | Deployment and integration required |
| Operations | Vendor manages service | Your team manages service |
| Data path | Vendor and configured model provider | Infrastructure and model path you choose |
| Model choice | Vendor-selected or supported providers | Potentially commercial, private or local models |
| Upgrades | Automatic | Scheduled and tested by your team |
| Scaling | Vendor responsibility | Your responsibility |
| Customization | Product configuration | Source, deployment and network can be modified |
| Audit scope | Vendor controls plus your configuration | Your application, infrastructure and model controls |
| Cost shape | Subscription, seats, PRs or usage | Infrastructure, model usage and engineering time |
| Best fit | Teams prioritizing speed and convenience | Teams prioritizing control and isolation |
Start by mapping the data flow
“Self-hosted” does not automatically mean “no code leaves our network.” The application may run in your VPC while sending prompts to an external model API. Conversely, a cloud service may have enterprise controls and model-provider agreements that meet your requirements.
Draw the complete path for repository data:
- Git provider sends a webhook.
- The reviewer fetches the diff and relevant files.
- Context is stored or held temporarily.
- A prompt is sent to a model.
- Findings are stored, filtered and posted to the pull request.
- Logs, traces, backups and analytics may retain related metadata.
At every step, identify which organization controls the system, where processing happens and what is retained. Check whether the content can be used for training, which subprocessors receive it and whether retention can actually be disabled or verified.
The answer should describe the whole path, not only where the dashboard runs.
Privacy and source-code control
Source code may contain credentials, proprietary algorithms, customer identifiers, internal URLs and security architecture. Even when secrets are managed correctly, repository context is sensitive business information.
Cloud review requires trusting the review vendor and any model provider involved. Evaluate contracts, retention policies, training policies, subprocessors and breach-notification terms.
Self-hosting reduces the number of parties that must receive repository data, particularly when paired with a model running inside your controlled environment. It also lets you enforce outbound network restrictions and private connectivity.
But self-hosting transfers responsibility rather than making it disappear. A public storage bucket, overly broad service account or verbose log can expose code just as effectively as a bad vendor decision.
Security and threat model
Cloud vendors may provide mature security operations, hardened infrastructure and third-party audits that a small engineering team cannot reproduce economically. Self-hosting provides deeper control over network boundaries, identity, keys and audit logs.
Evaluate both models against the same threats: unauthorized repository access, compromised installation tokens, sensitive telemetry, excessive network access and model-provider retention. Public repositories also need a plan for malicious content arriving through untrusted pull requests.
For self-hosting, confirm that the project documents secrets management, webhook verification, least-privilege Git permissions, upgrade procedures and network egress. “It runs in Docker” is a packaging fact, not a security program.
Compliance and data residency
Some organizations must keep source code or associated data within a specific country, cloud account or network zone. Others need a clear audit trail showing every system that processes it.
Self-hosting can simplify the architecture presented to auditors because infrastructure controls remain inside the organization’s existing compliance boundary. It can also complicate the audit because your team now owns more controls.
Cloud can be easier when the vendor already provides the agreements, certifications, regions and audit materials your program requires.
Do not choose based on the word “compliant.” Map each concrete requirement to a control and an owner.
Model flexibility
A cloud reviewer may choose and tune models on your behalf. That is convenient: models change quickly, and evaluating them is real engineering work.
A self-hosted, model-agnostic reviewer may connect to a commercial API, your existing AI gateway, a model in your cloud account or a fully local model. Some teams also route different repositories to different models based on sensitivity and cost.
Model freedom matters for more than privacy. It affects review quality, latency, cost, context limits and availability. It also means your team owns evaluation. The model with the most impressive coding demo is not automatically the best at finding subtle pull-request bugs.
Operational work
Cloud review usually requires installing a GitHub App, selecting repositories and configuring rules. The vendor handles queues, scaling, database maintenance, model failover, upgrades and monitoring.
With self-hosting, somebody must own deployment, the database and queue, backups, secret rotation, monitoring and upgrades. Model failures and bursts of pull requests also become your capacity problem. The workload may be small, but it is never zero, so assign an owner before deployment.
Cost: include engineering time
Cloud pricing may be per seat, repository, pull request, changed line or model usage. Calculate cost using your actual number and size of pull requests, not only developer headcount.
The self-hosted bill includes more than compute. Add model usage or inference hardware, storage, observability, backups and the engineering time spent maintaining them. Security review, compliance evidence and delayed upgrades are costs too, even when they never appear on a cloud invoice.
At scale, self-hosting can provide cost control and eliminate per-seat pricing. At smaller scale, one afternoon of infrastructure work may exceed months of subscription fees.
The cheapest invoice is not necessarily the lowest total cost.
Reliability and support
In cloud deployments, the vendor owns service availability and upgrades. Examine status history, support response expectations and behavior when a model provider is unavailable.
In self-hosted deployments, you control maintenance windows and can keep a known version, but recovery is your responsibility. Before choosing a project, check its releases and migration notes, health checks, backup documentation and rollback process. Reproducible images and some form of support become important the first time an upgrade goes wrong.
Open source gives you the right to fix a problem. It does not guarantee that someone is awake to fix it at 3 a.m.
A decision framework
Score each statement from 0 (“not important”) to 3 (“mandatory”).
Signals favoring cloud
Cloud is usually the better fit when you need to start quickly, do not want another production service to operate and are already allowed to use the relevant SaaS and model providers. It also makes sense when vendor support and automatic model evaluation matter more than infrastructure control.
Signals favoring self-hosting
Self-hosting becomes attractive when repository data must stay in a particular environment, you need a private model or network egress has to be controlled. It is easier to justify when your team already operates the required platform and needs source-level customization, auditability or a fixed version for long periods.
Any mandatory requirement should outweigh a pile of minor conveniences. If code cannot leave a particular network, the scoring exercise is already over.
Questions to ask every vendor or project
Start with the data path. Ask what repository content is stored or logged, whether it is used for training, which Git permissions are required and whether inference can run without external network access.
Then check how complete the self-hosted product actually is. Some projects reserve important features for cloud, restrict model choice or make it difficult to export configuration. Ask directly which features differ and whether you can bring your own provider or key.
Finally, look at operations: how secrets are rotated, how migrations and security patches are delivered, what happens when the model is unavailable and whether you can move between deployment modes without rebuilding your review configuration.
A hybrid path is often practical
The decision does not have to apply uniformly to the whole company. A team might use cloud review for public and lower-sensitivity repositories while self-hosting for regulated or proprietary code. Another team may start in cloud to evaluate usefulness, then move to self-hosting once requirements and volume are understood.
That path works best when the tool uses the same review rules and workflow in both environments. Otherwise migration becomes a second product evaluation disguised as deployment work.
Choose control deliberately
Cloud AI code review optimizes for speed and simple operations. Self-hosted AI code review optimizes for control over infrastructure, the data path and the model. Neither is automatically the more responsible choice. Responsibility comes from understanding the data flow and owning the controls your chosen model needs.
Scopy AI is open source and supports both managed cloud use and self-hosting, so teams can choose based on repository requirements rather than adopting an entirely different review workflow. For more background, read the practical guide to self-hosted AI code review or learn what AI code review does before evaluating deployment options.