AI Deployment Verification
AI-powered deployment verification that monitors every deployment and catches issues within minutes. Zero configuration required.
See setup docs→The Deployment Blind Spot
Modern engineering teams deploy to Kubernetes dozens of times per day. Each deployment is a potential incident waiting to happen: a subtle memory leak, a misconfigured environment variable, or a breaking API change that only manifests under production load.
Traditional deployment monitoring falls short. Health checks only catch crashes. Canary deployments require complex traffic splitting infrastructure. Manual verification doesn't scale when you're shipping multiple times daily. The result? Teams discover deployment issues through user complaints, not proactive detection.
Post-mortems consistently reveal the same pattern: the signals were there—in the logs, traces, and metrics—but no one connected them to the deployment that caused the regression. By the time someone notices, users have already been impacted for hours.
What is AI Deployment Verification?
AI Deployment Verification is an automated process that analyzes every Kubernetes deployment to detect breaking changes before they impact users. Instead of relying on basic health checks or manual monitoring, it uses AI to correlate multiple data sources and identify regressions within minutes of deployment.
The verification process compares pre and post-deployment telemetry to catch issues like increased error rates, latency spikes, memory leaks, and failed downstream calls. It delivers a clear verdict—healthy or degraded—with specific evidence so teams can act immediately.
Every Signal, One Verdict
Guardian checks code diffs, logs, traces, metrics, and profiling data to verify every deployment.
Detection
Guardian automatically detects deployments by monitoring Kubernetes manifest changes across all your clusters. When a deployment, statefulset, or daemonset spec changes, verification triggers instantly. No webhooks, annotations, or CI/CD integration required.
Data Collection
Metoro's eBPF-based agent continuously collects logs, distributed traces, metrics, and profiling data from your services. When a deployment is detected, Guardian gathers telemetry from both before and after the change for comparison analysis.
AI Analysis
Guardian's AI correlates code diffs from GitHub with observed telemetry changes. It identifies new error patterns, latency regressions, memory anomalies, and other signals that indicate a problematic deployment—distinguishing real issues from normal variance.
Verdict
Within 10 minutes of deployment, Guardian delivers a clear verdict: healthy or concerning. Failed verifications include specific evidence—the exact metrics that changed, relevant log excerpts, and links to affected traces—so you can act immediately.
Every Signal, Analyzed
Guardian doesn't just check if your pods are running. It analyzes six distinct data sources to understand whether your deployment is truly healthy.
Logs
Detects new error patterns, increased exception rates, and warning messages that weren't present before the deployment. Surfaces the specific log lines causing concern.
Traces
Analyzes distributed traces to identify latency regressions, increased error rates on specific endpoints, and failures in downstream service calls introduced by the change.
Metrics
Monitors CPU usage, memory consumption, request rates, and custom application metrics. Flags anomalies that deviate significantly from pre-deployment baselines.
Profiling
Continuous profiling reveals CPU hotspots, memory allocation patterns, and potential leaks. Catches performance regressions that don't show up immediately in metrics.
Code Diffs
GitHub integration provides context about what changed. Guardian correlates code modifications with observed telemetry changes to pinpoint the likely cause of issues.
Kubernetes Events
Monitors pod restarts, OOMKills, failed probes, and scheduling issues. Catches infrastructure-level problems that indicate deployment instability.
Real Problems, Caught Early
Some of the issues Guardian catches within minutes of deployment—before they become incidents.
Memory Leaks
A new feature introduces a memory leak that slowly grows over hours. Traditional monitoring won't alert until OOMKill.
Guardian catches it: Profiling shows memory allocation increasing 3x faster than baseline within 10 minutes of deployment.
API Regressions
A refactor accidentally changes response format on an endpoint used by a downstream service. Integration tests passed because they test the happy path.
Guardian catches it: Traces show 500 errors from the payments service correlating with the API change in the code diff.
Performance Degradation
A database query optimization actually makes things worse under production load patterns. Staging didn't catch it because the data volume is different.
Guardian catches it: P95 latency on the affected endpoint jumped 40% immediately after deployment.
Works With Your Stack
Guardian integrates with the tools you already use to keep your team informed and your deployments safe.
Kubernetes
Native detection of deployments, statefulsets, and daemonsets across all clusters. Zero configuration required.
Slack
Instant notifications and verification results routed to the right channels with @here for critical failures.
PagerDuty
Auto-create incidents when Guardian detects failures. Full context included for faster triage.
GitHub
Link repos for code diff analysis. Correlate code changes with production issues.
Beyond Basic Health Checks
Traditional deployment verification approaches have significant gaps. Guardian fills them.
Manual Verification
LimitedDoesn't scale with deployment frequency. Engineers can't manually check logs, metrics, and traces for every deployment when you ship 20+ times per day.
Basic Health Checks
LimitedOnly catches crashes and complete failures. Subtle regressions like increased latency, memory leaks, or elevated error rates slip through.
Canary Deployments
ComplexRequires traffic splitting infrastructure, service mesh configuration, and careful rollout management. Not all teams have this capability.
Post-deploy Monitoring
ReactiveImpossible to write alerts for every failure mode. High-cardinality signals like new error messages or log patterns can't be pre-defined—you don't know what to monitor until it breaks.
Real-time Slack Alerts
Get notified the moment a deployment is detected, see exactly what changed, and receive the verification verdict—all in Slack.
- ✓Instant deployment alerts with environment and namespace context
- ✓AI-generated explanation of why verification is needed
- ✓Clear healthy/degraded verdict with supporting evidence
- ✓Direct links to full investigation details
Complete Deployment History
Track every deployment across all your clusters with a visual timeline showing verification status and history.
- ✓Chronological view of all deployments with status indicators
- ✓Expandable cards showing image changes (old → new tags)
- ✓Quick links to full Guardian investigation details
- ✓Filter by environment, namespace, or service
Deploy with Confidence
Stop worrying about breaking changes. Let Guardian verify every deployment automatically.
SUPPORT
Frequently Asked Questions
Everything you need to know about the product and billing. Can't find the answer you're looking for? Ask us on our Slack Community.
How does Deployment Verification detect deployments?
Metoro automatically detects every deployment across all your Kubernetes clusters by monitoring changes to your Kubernetes manifests. When the spec of a deployment, stateful set, or daemon set changes, Metoro triggers a verification. No configuration, annotations, or webhook setup required - it works out of the box the moment you install Metoro.
How does the code get analyzed during verification?
Metoro integrates with GitHub. Every service can be associated with a repository. When Metoro sees that a manifest updates the tag of an image, we look through the corresponding tags in the repository to understand how the version changed and what code was modified since the last deployment.
How does Metoro know about my logs / traces / metrics / profiling information for my services?
Metoro collects logs, traces, metrics, and profiling data from your services automatically through our node agent. You just install a single helm chart and we collect everything we need.
How quickly does verification complete after a deployment?
Guardian waits 5 minutes after detecting a deployment to allow sufficient time for data to be collected about the new deployment. After that, the investigation takes about another 5 minutes. Total time from deployment to verdict is approximately 10 minutes.
Can I customize which deployments get verified?
Yes! You can set up filters based on environment, namespace, or service name. You can also use Kubernetes annotations to disable verification for specific workloads or exclude certain config paths from triggering verification. This is often popular for non-critical services.
Does Guardian work with ArgoCD, Flux, or other GitOps tools?
Yes. Guardian detects deployments at the Kubernetes level by monitoring manifest changes, so it works with any deployment tool—ArgoCD, Flux, Helm, kubectl, or custom CI/CD pipelines. No special integration is required.
What if I already use canary deployments?
Guardian complements canary deployments by providing deeper analysis. While canary deployments help limit blast radius, Guardian analyzes logs, traces, metrics, and profiling data to catch issues that basic canary metrics might miss—like memory leaks or subtle API regressions.
What languages and frameworks does Guardian support?
Guardian works with any language or framework running on Kubernetes. Our eBPF-based agent collects telemetry at the system level, so there's no language-specific instrumentation required. We support Go, Java, Python, Node.js, Ruby, .NET, and more.
Can I integrate verification results into my CI/CD pipeline?
Yes. Guardian provides webhooks and API access to verification results. You can use these to gate subsequent deployments, trigger rollbacks, or integrate with your existing deployment automation.
What happens if Guardian flags a false positive?
You can mark verifications as false positives directly in the UI. Guardian learns from this feedback to improve future analysis. You can also adjust sensitivity thresholds and configure which signals to prioritize for specific services.
How does Guardian compare to manual QA or staging environments?
Manual QA and staging catch many issues, but they can't replicate production traffic patterns, data volumes, or real-world load. Guardian analyzes actual production behavior after deployment, catching issues that only manifest under real conditions.
How is deployment verification priced?
Currently we offer deployment tracking and verification for free with any paid Metoro plan. This includes unlimited deployments, all integrations, and full access to verification history.