AI code review pricing looks simple until you try to compare two tools. One charges per developer. Another charges per pull request. A third lets you bring your own model key and sends you the inference bill later.
The invoice is only part of the answer. A useful comparison includes the engineer time a review still takes, the cost of running the service, and the pull requests that consume capacity without producing a useful finding.
This is the model I use when comparing AI-assisted review with the manual baseline.
Start with the cost your team already pays
Before looking at a product's price, estimate what review costs today.
manual baseline = monthly pull requests
* manual minutes per pull request / 60
* loaded engineering cost per hourThe loaded hourly cost is not the engineer's salary divided by working hours. It can include salary, benefits, taxes, equipment, management and the other costs your company uses when it values engineering time. Use the number your finance or planning process already uses. If you only need a rough comparison, state the assumption and keep it editable.
The manual baseline is not a claim that every minute disappears when an AI reviewer arrives. It is the amount of review time you are trying to change.
Model the assisted workflow
Most teams still have a human review after an AI pass. The calculation needs to represent that.
assisted pull requests = monthly pull requests * AI-assisted share
assisted labor cost = (
assisted pull requests * assisted minutes per pull request
+ remaining pull requests * manual minutes per pull request
) / 60 * loaded engineering cost per hour
AI service cost = assisted pull requests * AI cost per pull request
assisted total cost = assisted labor cost + AI service costThe difference between the manual baseline and the assisted total is the modeled savings. If it is negative, the workflow costs more under those assumptions. That is useful information. It may mean the tool is too expensive, the review does not save enough time, or the team is not using it on enough changes to matter.
The AI code review cost calculator uses this model. Its inputs include monthly pull requests, manual and assisted review minutes, loaded hourly cost, AI-assisted share and AI service cost per pull request.
A worked example
The calculator starts with these defaults:
- 96 pull requests per month
- 35 minutes of manual review per pull request
- 15 minutes for an AI-assisted review
- $80 loaded engineering cost per hour
- 70% of pull requests use the assisted workflow
- $2 of AI service cost per assisted pull request
Those assumptions produce a manual baseline of about $4,480 per month. The assisted workflow uses about 33.6 engineering hours instead of 56, then adds about $134 in AI service cost. The modeled assisted total is about $2,822, leaving roughly $1,658 in monthly savings.
That result is not a promise. Change the review times and the answer changes with them. If the AI comments create another investigation step, include that time in the assisted minutes. If a reviewer still spends 35 minutes on every pull request, the tool has not reduced the labor cost even if it found a bug.
Four ways vendors charge
Per seat
Per-seat pricing is easy to budget. It can work well when many engineers open pull requests and the plan includes enough review volume for the whole team.
The weakness is that headcount becomes the billing unit even when review volume is uneven. A team with ten engineers and twenty pull requests may pay for capacity it does not use. A team with ten engineers and hundreds of large pull requests may hit usage limits even though the seat count looks small.
Ask what counts as a seat, whether outside contributors consume one, and what happens when the included review allowance runs out.
Per pull request or changed line
Usage-based pricing follows the work more closely. It can be a better fit for teams with a small number of repositories or irregular review volume.
Read the definition carefully. A provider may count every review attempt, every changed line, every generated comment or only a completed review. Retries, updates after a push and large generated files can change the bill. The pricing page may also use a different line-count definition than the one your team uses for review planning.
Credits or bundled reviews
Credits make a plan look predictable while leaving the important question unanswered: how many credits does a real pull request use?
Take a sample of recent changes and estimate the credit use for each. Separate small bug fixes, large feature changes, dependency updates and generated files. A monthly allowance that looks generous for small changes may disappear quickly on a few large ones.
Bring your own key
BYOK moves the model account and its bill to your team. That can remove a markup or let you choose a provider that fits your budget.
It does not make review free. You still pay for input and output tokens, retrieval calls, retries, storage, the review service and the engineering time spent reading findings. You also need to understand the data path. The BYOK privacy guide explains why owning the key is different from keeping source code inside your own network.
Self-hosting has an invoice and an owner
Self-hosting can reduce or remove a vendor subscription, but it adds work that a hosted price includes:
- model or inference costs
- compute, storage and a database
- queues, webhooks and scheduled jobs
- backups and monitoring
- secret rotation and access reviews
- upgrades and security fixes
- time spent diagnosing failed reviews
The self-hosted versus cloud guide covers the operating trade-off in more detail. For the cost model, put an hourly value on the person who owns the service. "We already have Kubernetes" is not the same as "this service costs nothing."
Self-hosting becomes easier to justify when the data boundary is mandatory, review volume is high or the team already operates similar services. At low volume, a subscription can be cheaper than the first migration and incident.
The cost per pull request is not constant
Two pull requests with the same number of changed lines can require different amounts of work. One changes a label. The other changes a shared authorization helper used by five services.
Cost can rise when a review needs to:
- read related functions and their callers
- inspect schemas, types or configuration
- check tests and repository rules
- search official library documentation
- retry after a provider timeout
- re-run after the pull request changes
That extra context may improve the review. It also consumes tokens, requests and time. A cheaper diff-only review is not automatically cheaper if it misses the bug and leaves a human to repeat the investigation.
Track both review spend and review outcomes. A useful measure is cost per accepted finding, not cost per comment. A comment that nobody trusts is an expense even when the provider charges nothing for it.
Find the break-even price
The calculator reports a break-even AI cost per assisted pull request. It asks how much room remains after the labor savings are accounted for.
break-even AI cost per assisted PR =
(manual baseline cost - assisted labor cost)
/ assisted pull requestsIf your service costs less than that number, the model predicts labor savings under the current assumptions. If it costs more, you need better time savings, a higher assisted share or a different workflow.
Do not count a defect's value as guaranteed savings. A serious bug may be expensive, but you cannot assume the reviewer would have found it or that a human would have missed it. Track defects separately and treat them as evidence about quality, not as a convenient line in the cost calculation.
Get numbers you can defend
Use your own workflow rather than copying a vendor's example.
- Count merged pull requests over a representative period.
- Measure how long reviewers spend on small, medium and large changes.
- Estimate the portion that an AI reviewer will actually handle.
- Record the model, provider and average service cost for each review.
- Add time spent checking, correcting or dismissing AI findings.
- Recalculate after the team has enough reviews to see whether behavior changed.
Keep the inputs visible. If the result depends on an $80 hourly cost, 70% adoption and $2 per review, the person reading the estimate should be able to change all three.
The cost calculator is useful for the first pass. Then replace its defaults with measured values from your repositories.
Frequently asked questions
Is AI code review cheaper than human review?
It can be, but the answer depends on review time, AI adoption, service cost and the time engineers spend investigating findings. Compare the assisted workflow with the manual baseline instead of comparing a subscription with zero.
What should AI cost per pull request include?
Include model calls, retrieval or tool calls, retries, provider charges and any subscription cost allocated to the review. If you run the service yourself, include infrastructure and the time spent operating it.
Does BYOK make AI code review free?
No. BYOK changes who pays the model provider and who controls the account. It does not remove token costs, review-service costs or engineering time.
Is per-seat pricing or usage-based pricing better?
Use the model that matches your volume. Per-seat pricing is easier to budget. Usage-based pricing follows pull-request activity more closely. Calculate both with your own volume and large-change mix before choosing.
What is the fastest way to estimate our cost?
Start with the AI code review cost calculator, then replace the defaults with your monthly pull requests, measured review minutes, loaded hourly cost, expected adoption and service estimate.