# AI Code Generator Rollout Simulator: Budget Burn Check URL: https://upstreamapi.com/tools/ai-code-generator-rollout-simulator Type: tool Locale: en Published: 2026-11-08 Updated: 2026-11-08 --- > Model a bad AI-written change across a canary ladder and see the error budget it burns with a human watching versus an automated SLO gate. *Free rollout planner* ## Simulate how far an AI code change spreads before rollback Paste in your traffic, SLO and ladder. The tool shows how much error budget a bad change from an AI code generator burns under a single deploy, a human watching a dashboard, and an automated gate. ## AI code rollout simulator Every field changes the result live. Defaults describe a service at 200 requests per second with a 99.9% SLO and a change that fails 20% of requests. *[Interactive widget — see the live page for the full experience]* ## Three lines of arithmetic, no hidden model ### Error budget Budget = (1 minus SLO) times average requests per second times 2,592,000 seconds in 30 days. At 200 req/s and 99.9%, that is 518,400 failed requests you are allowed per month. ### Exposure over time The ladder gives the share of traffic on the new code at each minute. The simulator integrates that share from the first request until your notice time plus your revert time, so a slow detection pushes the change into later, bigger stages. ### Failed requests Failed = requests per second times your failure share times the exposure integral. Dividing by the budget gives the percentage of the month one bad change costs. Three detection modes are compared on the same ladder. ## From an AI-generated diff to a rollout plan 1. **Pick the pessimistic inputs** — For a change written by an AI code generator and reviewed quickly, assume a high failure share if it is wrong, and a human notice time that matches your slowest recent incident, not your best one. 2. **Compare the three scenarios** — The single deploy shows the worst case. The staged human case shows what your ladder buys you today. The automated gate shows what a gate on your SLO metric would change. 3. **Copy the plan into the runbook** — The copy button exports the stages and the worst-case budget burn as plain text, ready to paste into a change ticket or a pull request description. *Why the ladder matters* ## The first stage is where most of the budget is saved A change that fails 20% of requests at 1% traffic produces failures on 0.2% of requests. The same change at 100% fails one request in five. The gap between those two numbers is the whole argument for a progressive rollout. When the code comes from a generator that can produce hundreds of lines in seconds, review time per line drops, and the first stage becomes the place where you find out. - A 1% first stage limits the blast radius by 99% before anything else is measured. - Soak time only helps if your signal can show a regression inside that window. - Reverting faster is worth as much as noticing faster: both add to the exposure integral. ## What to do with the three numbers ### Single deploy If this number is above 5% of the monthly budget for a plausible failure, a one-shot deploy is a decision to spend your budget on one change. Few teams make that choice on purpose. ### Staged, human This is your current reality if on-call watches graphs. If the peak exposure shows 100%, your ladder is cosmetic: the bad change reaches everyone before anyone reacts. ### Staged, automated gate This is the case for a gate that reads your SLO metric and reverts without asking. The value is only real if the gate trips on the same signal your SLO uses, so test it with a deliberately bad change. ## Common questions ### Is this AI code generator rollout simulator free? Yes. It runs entirely in your browser, needs no signup and sends nothing to a server apart from an anonymous usage beacon. It is not an AI code generator itself: it simulates how far a bad change spreads once code from one reaches your pipeline. ### Where do the numbers come from? From arithmetic you can check by hand. The error budget is (1 minus your SLO) times the requests in 30 days. Failed requests are your traffic times the failure share times the traffic fraction the new code receives, integrated over the time until the revert finishes. No benchmark is baked in; every assumption is an input. ### Why model AI-generated code differently from human-written code? The math is identical. What changes is the prior: a large diff produced in minutes has had less human review per line, so the failure share and the time to notice deserve pessimistic values. Set both from your own incident history, not from this page. ### What does the simulator assume that real traffic does not do? It assumes steady traffic, a regression that fails the same share of requests from the first request, and a revert that stops the damage instantly once it completes. Real incidents have ramp-up, partial failures and slow rollbacks, so treat the result as a floor on the damage, not a forecast. ### What is a good time to notice for an automated SLO gate? It depends on your metric resolution and alert window. A gate that evaluates a one-minute error rate against a threshold can trip in two to five minutes. If your alerting only pages after a 30 minute burn-rate window, enter that value instead, and the simulator will show what it costs. ### Does a longer soak time always help? No. A longer soak gives the gate more data per stage but also keeps a bad change at a given traffic share for longer. If the gate notices within two minutes, a ten minute soak mostly adds waiting. If it needs fifteen, a five minute soak lets the change reach 25% or more first. ### Can I use this for feature flag rollouts and not only deploys? Yes. A percentage rollout of a flag behaves the same way as a canary deploy for this calculation: a traffic fraction, a soak time, a detection delay and a revert time. Replace the revert time with your flag kill-switch latency. ## Want the gate to do the reverting for you? upstreamapi runs progressive rollouts with SLO gates and auto-rollback, so the third column is the one you ship with. *Call to action: See upstreamapi* ## FAQ ### Is this AI code generator rollout simulator free? Yes. It runs entirely in your browser, needs no signup and sends nothing to a server apart from an anonymous usage beacon. It is not an AI code generator itself: it simulates how far a bad change spreads once code from one reaches your pipeline. ### Where do the numbers come from? From arithmetic you can check by hand. The error budget is (1 minus your SLO) times the requests in 30 days. Failed requests are your traffic times the failure share times the traffic fraction the new code receives, integrated over the time until the revert finishes. No benchmark is baked in; every assumption is an input. ### Why model AI-generated code differently from human-written code? The math is identical. What changes is the prior: a large diff produced in minutes has had less human review per line, so the failure share and the time to notice deserve pessimistic values. Set both from your own incident history, not from this page. ### What does the simulator assume that real traffic does not do? It assumes steady traffic, a regression that fails the same share of requests from the first request, and a revert that stops the damage instantly once it completes. Real incidents have ramp-up, partial failures and slow rollbacks, so treat the result as a floor on the damage, not a forecast. ### What is a good time to notice for an automated SLO gate? It depends on your metric resolution and alert window. A gate that evaluates a one-minute error rate against a threshold can trip in two to five minutes. If your alerting only pages after a 30 minute burn-rate window, enter that value instead, and the simulator will show what it costs. ### Does a longer soak time always help? No. A longer soak gives the gate more data per stage but also keeps a bad change at a given traffic share for longer. If the gate notices within two minutes, a ten minute soak mostly adds waiting. If it needs fifteen, a five minute soak lets the change reach 25% or more first. ### Can I use this for feature flag rollouts and not only deploys? Yes. A percentage rollout of a flag behaves the same way as a canary deploy for this calculation: a traffic fraction, a soak time, a detection delay and a revert time. Replace the revert time with your flag kill-switch latency.