Continuous deployment, AI-assisted or not

AI for Software Development Needs a Safety Net

Ship AI-assisted code behind SLO-gated rollouts that auto-revert before your error budget breaks, not after.

Start free pilot See how it works
SRE monitoring an AI-assisted deployment rollout dashboard at night
What actually gates the rollout

Six things a static percentage rollout cannot do

SLO-gated auto-rollback

Reverts the moment error budget burn crosses your threshold. No one has to notice the alert first.

Real-time budget burn

Watch error budget consumption per service and per rollout stage, updated as traffic hits the canary.

Risk-scored deploys

Flags high blast-radius diffs, AI-authored or not, before they reach 100 percent of traffic.

Canary, ring, or percentage

Pick the rollout pattern that fits the service. Change it per team without a platform ticket.

Blast radius controls

Scope every flag to a service, a region, or a single account. Contain the damage before it spreads.

Full deploy audit trail

Every flag flip, gate, and rollback logged with who or what triggered it, AI Pilot included.

Setup

How it works

Five steps between a merged pull request and a contained incident.

  1. 1

    Connect your pipeline

    Wire upstreamapi into your CI/CD via webhook or API. It runs alongside your existing feature flag setup.

  2. 2

    Define the SLO budget

    Set the error rate and latency thresholds that actually matter for the service you are shipping.

  3. 3

    Ship behind a flag

    Push the change, human-written or AI-assisted, behind a flag instead of straight to full traffic.

  4. 4

    AI Pilot watches the rollout

    It tracks budget burn continuously across each stage, not just a fixed five-minute canary window.

  5. 5

    Auto-rollback fires first

    If the budget breaks, the flag reverts before an alert reaches a human. The post-mortem starts already contained.

Use case

Ship AI-generated code without babysitting the rollout

Your team merges more pull requests since adding an AI coding assistant. DORA's 2025 State of AI-assisted Software Development report found the same pattern industry-wide: pull requests merged per engineer up 98 percent, incidents per pull request up 242.7 percent. upstreamapi does not slow the AI down. It puts a gate between the merge and the blast radius, so a bad AI-authored diff burns a canary's error budget, not the whole fleet.

  • Works with any CI/CD, not one vendor's pipeline only
  • Gates rollout velocity to error budget, not a fixed timer
  • Flags diffs with an unusual complexity delta for tighter canary stages
Engineer laptop screen showing an error budget burn-down chart during a deploy
Use case

See the incident before your customers do

A rollback that fires in the first two minutes of a canary stage never reaches a status page. upstreamapi watches the SLO budget stage by stage and reverts automatically, then hands the on-call engineer a timeline: what shipped, what burned, and what already got reverted, before they even open a dashboard.

  • Timeline view ties every incident back to the triggering deploy
  • Works for AI-authored and human-authored changes alike
  • Cuts the time spent proving which change caused the spike
Two platform engineers reviewing an alert spike and automatic recovery graph
Sourced, not invented

The AI productivity trade nobody priced in

+98%
more pull requests merged per engineer since AI coding tools spread (DORA 2025)
+242.7%
more incidents per pull request over the same period (DORA 2025)
84%
of developers now use or plan to use AI coding tools (Stack Overflow 2025 survey)
29%
trust AI output accuracy, down from 40% a year earlier (Stack Overflow 2025 survey)

Common questions

Does AI actually make software delivery riskier?
Per DORA's 2025 State of AI-assisted Software Development report, yes, on average: incidents per pull request rose 242.7% as AI adoption grew, even as throughput rose too. The fix is not less AI. It is gating rollout velocity to your SLO budget so a bad diff burns a canary, not the fleet.
How is an SLO-gated rollout different from a manual canary?
A manual canary usually runs on a fixed timer: five minutes at five percent, then a human decides. An SLO-gated rollout watches error budget burn continuously and reverts automatically the moment the budget breaks, whether that takes ninety seconds or nine minutes.
What happens when an AI-authored change trips the gate?
The flag reverts to the last known-good state before the error budget fully burns. The engineer on call gets a timeline showing what shipped, when it started burning budget, and that the rollback already ran, instead of a blank incident channel.
Do we need to replace our existing feature flag system?
No. upstreamapi connects to your CI/CD via webhook or API and can run alongside an existing flag provider during migration. Most teams move service by service instead of all at once.
How does auto-rollback avoid reverting on noisy, harmless spikes?
The gate compares burn rate against your defined SLO threshold over a rolling window, not a single data point, so a brief blip does not trigger a revert. You set the threshold and the window per service.
What does this cost?
Pricing depends on the number of services you gate and your rollout volume. Start the signup flow and you will see a plan built around your actual traffic, not a generic seat count.
Is our deployment and code data secure?
upstreamapi only sees what your CI/CD sends it: deploy events, error rates, and flag state. It does not require read access to your source repository to gate a rollout.
Can we use this without an AI coding assistant on the team?
Yes. The SLO gate does not care who or what wrote the diff. Teams shipping entirely human-written code use the same auto-rollback to cut MTTR on ordinary rollouts.

Start Your First SLO-Gated Rollout

Free pilot on one service. Keep your existing CI/CD and feature flag setup while you try it.

Start free pilot Read the FAQ