Know If Your Deploy Can Continue
Automatic safety checks for your deployments. No configuration needed.
Setup in 10 minutes • Works with any deployment tool • No thresholds to configure
Test the Verdict in Real-Time
Adjust the metrics below and see the verdict change instantly. The API compares your new deployment to your baseline and tells you if it's safe.
Stop Guessing If Your Deploy Is Safe
You deploy a new version at 25% traffic. Five minutes later: error rate spikes, latency doubles, users complain. By the time you notice and rollback, revenue is lost and trust is damaged.
The real problem? You're making go/no-go decisions with gut feeling instead of data.
Teams using DeployVerdict see:
- ↓ 50%fewer failed deployments reaching production
- 3-4 hourssaved per week on deployment monitoring and war rooms
- 100%consistent deployment decisions across all teams
What is DeployVerdict?
DeployVerdict is a deployment safety API that analyzes your metrics and tells you whether to continue, pause, or rollback a deployment.
Unlike traditional monitoring tools that just show you graphs, DeployVerdict makes the decision for you. You send a single API request with your metrics (error rate, latency, success rate, etc.), and you get back a clear verdict: SAFE, WARNING, or STOP.
It works by comparing your current deployment to a baseline. If error rate increases by 20%, latency spikes by 50%, or resource usage hits 90%, the API detects these changes and returns a STOP verdict with the exact reason.
Who uses it? CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins), deployment automation tools (Spinnaker, Argo Rollouts), and AI agents that make autonomous deployment decisions (n8n workflows, MCP agents, custom automation scripts).
Common Deployment Problems We Solve
Manual deployment approval bottlenecks
Problem: Every deployment requires a senior engineer to manually check dashboards, compare metrics, and approve continuation. This blocks your team and creates single points of failure.
How DeployVerdict helps: Automates the approval decision. Your pipeline calls the API at each rollout stage (5%, 10%, 25%, 50%), gets a verdict, and continues or stops automatically.
Inconsistent go/no-go criteria across teams
Problem: Each team has different deployment rules. Team A stops on 5% error rate, Team B tolerates 10%. During incidents, nobody knows which criteria were actually used.
How DeployVerdict helps: Single source of truth for all teams. Same logic, same thresholds, auditable decision trail. Your incident reports can reference the exact API response that triggered the rollback.
Blind deployments with no safety checks
Problem: You deploy and hope for the best. By the time errors appear in your monitoring system, users are already impacted. Rollback happens 10-15 minutes too late.
How DeployVerdict helps: Real-time safety checks at every rollout stage. The API detects degradation in seconds, not minutes. Your automation can rollback before most users even see the problem.
AI agents making risky deployment decisions
Problem: You built an automation workflow (n8n, Zapier, custom script) that deploys code autonomously. But the agent has no way to verify if the deployment is actually safe.
How DeployVerdict helps: Acts as an external judge for your agent. The agent calls the API, gets a structured JSON response, and can interpret the verdict without any custom logic. Works with MCP agents, n8n workflows, and any automation tool.
How It Works (3 Simple Steps)
Send your deployment metrics
Make a single POST request with your current metrics: error rate, latency, success rate, resource usage, and traffic percentage. Include your baseline (metrics from before the deployment) so the API can calculate the change.
API analyzes the changes
The API compares your new deployment to the baseline. If errors increased significantly, latency spiked, or resources are saturated, it calculates an overall risk score. The decision is deterministic: same metrics always produce the same verdict.
Get a clear verdict in milliseconds
You receive SAFE (continue deploying), WARNING (monitor closely), or STOP (rollback immediately). The response includes the exact reason (like "error_rate_spike" or "resource_saturation") and a suggested action.
What Metrics Does It Check?
The API looks at 7 key signals. Think of it like a car dashboard: each light tells you something different, but together they show if your engine is about to fail.
Error Rate
Percentage of failed requests (5xx errors). If this jumps from 1% to 15%, something is clearly broken.
Success Rate
How many business actions succeed (payments, signups, orders). A system can respond without errors but still fail to do its job.
Latency (p95)
Response time for 95% of requests. If this doubles, users will notice slowness even if nothing crashes.
Resource Saturation
CPU, memory, or database connections hitting limits. Like a car engine overheating: if ignored, everything cascades into failure.
Show 3 more metrics...
Request Volume
How much traffic you observed. Prevents false alarms on low traffic (10 errors out of 20 requests isn't the same as 10 out of 1000).
Exposure Percent
What percentage of users see the new version. An error at 5% exposure is less critical than at 100%.
Baseline Comparison
The API compares everything to your pre-deployment state. This is why it works without knowing your system: the delta is what matters.
Who Uses DeployVerdict?
Development teams doing progressive rollouts
You deploy to 5% of users first, wait 5 minutes, then 10%, then 25%, and so on. At each stage, your CI/CD pipeline (GitHub Actions, GitLab CI, Jenkins) calls DeployVerdict. If the verdict is SAFE, it continues. If STOP, it rolls back automatically.
Result: No more manual approval gates. Deployments happen 24/7 without needing a human to watch dashboards.
Automation builders using n8n, Zapier, or custom scripts
You built a workflow that automatically deploys code when tests pass. But the workflow has no way to check if the deployment actually worked. DeployVerdict becomes the safety check: your workflow calls the API, gets a verdict, and decides whether to continue or alert your team.
Result: Your automation can run safely without risking blind deployments.
AI agents making autonomous decisions
You're building an AI agent (using MCP, LangChain, or custom LLM workflows) that deploys code autonomously. The agent needs an external judge to verify that deployments are safe. DeployVerdict provides structured JSON responses the agent can parse without extra logic.
Result: Your agent has a reliable safety mechanism instead of guessing based on logs.
What Does the API Response Look Like?
Clean JSON that's easy to parse for humans and machines.
{
"verdict": "WARNING",
"reason": "latency_degradation",
"confidence": 0.72,
"suggested_action": "Monitor latency before continuing",
"summary": "Caution advised at 25% exposure.",
"details": {
"error_rate_signal": 0.0,
"latency_signal": 0.5,
"risk_score": 0.18
}
}What Reasons Can the API Return?
9 possible reasons, ranked by severity. The API always picks the most critical one.
| Reason | Category | Verdict |
|---|---|---|
| resource_saturation | Critical | STOP |
| success_rate_drop | Critical | STOP |
| error_rate_spike | Critical | STOP |
| combined_degradation | Intermediate | WARNING |
| high_exposure_risk | Intermediate | WARNING |
| latency_degradation | Intermediate | WARNING |
| conflicting_signals | Precaution | WARNING |
| insufficient_data | Precaution | WARNING |
| stable_conditions | Nominal | SAFE |
Common Questions
Does the API trigger rollbacks automatically?
No. DeployVerdict only provides recommendations. Your CI/CD pipeline or automation tool makes the final decision. This keeps you in control and reduces liability: the API advises, you decide.
Do I need to configure thresholds for my application?
No. The API uses universal thresholds that work for most systems. It analyzes relative changes (deltas) instead of absolute values, so it adapts to your baseline automatically. You can start using it in 10 minutes without any tuning.
Can I use this with n8n workflows or AI agents?
Yes. The API returns structured JSON with predictable fields (verdict, reason, confidence). Your n8n workflow or AI agent can parse the response and take action without custom logic. Works with MCP agents, LangChain, Zapier, Make, and any tool that can call a REST API.