> ## Documentation Index
> Fetch the complete documentation index at: https://docs.weaveos.com/llms.txt
> Use this file to discover all available pages before exploring further.

# CI deploy success rate

> Percentage of production deployments that complete successfully

## How it works

CI deploy success rate measures the percentage of production deployments that complete successfully. This is a key indicator of deployment process stability and code quality.

Higher success rates indicate:

* Reliable deployment pipelines
* Comprehensive pre-deployment testing
* Stable infrastructure and configuration
* Well-defined rollback procedures

## Calculation

The metric calculates the ratio of successful deployments to total deployment attempts.

**Formula:**

```
Success Rate = (Successful Deployments / Total Deployments) × 100
```

### What counts as successful

A deployment is considered successful when:

* The CI/CD workflow completes without errors
* All deployment steps execute successfully

### What counts as failed

A deployment is considered failed when:

* The CI/CD workflow exits with an error status
* Deployment steps fail or timeout

## Industry benchmarks

| Performance Level | Success Rate |
| - | - |
| **Elite** | 95-100% |
| **High** | 90-95% |
| **Medium** | 80-90% |
| **Low** | Below 80% |

<Warning>
  Success rates below 85% indicate significant deployment process issues that should be addressed urgently.
</Warning>

## Why it matters

Deployment success rate impacts:

* **Developer productivity:** Failed deployments waste time and disrupt workflow
* **User experience:** Failed deployments can cause outages or degraded service
* **Team morale:** Frequent deployment failures create anxiety and reduce confidence
* **Business continuity:** Unreliable deployments increase risk and slow feature delivery

## Common causes of low success rates

1. **Insufficient testing:** Code reaches production without adequate test coverage
2. **Environment drift:** Differences between staging and production environments
3. **Configuration errors:** Missing or incorrect environment variables
4. **Infrastructure issues:** Unreliable deployment infrastructure or networking
5. **Complex deployments:** Too many manual steps or dependencies
6. **Flaky tests:** Unreliable tests that fail intermittently

## Improving success rates

To increase deployment success rates:

1. **Strengthen pre-deployment testing:**
   * Expand unit and integration test coverage
   * Run end-to-end tests before deployment
   * Use smoke tests immediately after deployment

2. **Standardize environments:**
   * Use infrastructure-as-code (Terraform, CloudFormation)
   * Containerize applications (Docker, Kubernetes)
   * Maintain environment parity between staging and production

3. **Improve deployment automation:**
   * Eliminate manual deployment steps
   * Implement automated rollback triggers
   * Use blue-green or canary deployments

4. **Monitor deployment health:**
   * Set up deployment health checks
   * Monitor key metrics during deployment
   * Define clear success/failure criteria

5. **Learn from failures:**
   * Conduct blameless postmortems
   * Document common failure patterns
   * Update runbooks and automation

<Note>
  Balance success rate with **deploy frequency**. Very high success rates (99%+) combined with low deploy frequency may indicate overly conservative processes that slow down delivery.
</Note>

## Technical details

<AccordionGroup>
  <Accordion icon="check-circle" title="How are successful deployments identified?">
    Weave identifies successful deployments through CI/CD platform status codes:

    **GitHub Actions:**

    * Workflow conclusion: `success`
    * All required jobs completed successfully

    **CircleCI:**

    * Workflow status: `success`
    * All deployment jobs passed

    **Jenkins:**

    * Build result: `SUCCESS`
    * All deployment stages passed
  </Accordion>

  <Accordion icon="xmark-circle" title="How are failed deployments identified?">
    Failed deployments are detected through:

    1. **Explicit failures:** Workflow/pipeline/build exits with failure status
    2. **Timeouts:** The CI/CD platform reports the run as timed out (for example, a GitHub Actions conclusion of `timed_out`)

    Cancelled and skipped runs count as deployment attempts but not as successes, so they lower the success rate. The **Deploy results** chart shows only successful and failed runs.
  </Accordion>

  <Accordion icon="chart-line" title="What data sources are used?">
    CI deploy success rate uses data from:

    * **GitHub Actions:** Workflow run conclusions
    * **GitLab CI/CD:** Pipeline statuses
    * **Azure DevOps:** Build and classic release deployment results
    * **CircleCI:** Workflow statuses
    * **Jenkins:** Build results
    * **Argo CD:** Application sync results
  </Accordion>
</AccordionGroup>

## Public API

Available from [`GET /metrics`](/api-reference/metrics/get-metrics) as `ci_deploy_success_rate`: a 0–1 fraction of CI production deploys that succeeded. Merge-as-deploy rows are excluded. Change failure rate is `1 - ci_deploy_success_rate`. Requires a CI/CD connection and production-classified workflows. `group_by` is not supported; `teams` and `accounts` filters still apply.

## Related metrics

* **[CI deploy frequency](/metrics/ci-deploy-frequency):** How often you deploy to production
* **[CI deploy MTTR](/metrics/ci-deploy-mttr):** How quickly you recover from failed deployments
* **[PR cycle time](/metrics/pr-cycle-time):** Time from first commit to merge


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