Opsy augments DevOps, it doesn't replace it. Describe the task in words - Opsy AI assembles a production-ready configuration: Helm, Dockerfile, manifests and a pipeline by one standard. For teams of 10-100 developers on Kubernetes, GitLab, GitHub or Azure DevOps.
No need to write YAML or Helm by hand. Opsy understands the project context and applies best practices automatically.
Deploy in English or Russian. No need to learn YAML or Helm charts.
Go, Python, Node.js, Java/Maven, Rust, .NET, Nuxt - the type is detected from manifests and the config is optimized for it.
Security contexts, health probes, resource limits, network policies, PDB, anti-affinity, seccomp - out of the box.
"deploy project to production"
"use configmap project-prod"
"add ingress api.example.com"
"scale to 5 replicas"
"memory limit 2Gi; cpu limit 2000m"
curl -X POST "$OPSY_API/command" \
-H "Content-Type: application/json" \
-d '{
"command": "deploy myapp to production",
"execute": true,
"image": "my-registry/myapp:v1.0"
}'
curl -X POST "$OPSY_API/command" \
-H "Content-Type: application/json" \
-d '{
"command": "deploy project to production; use configmap project-prod; add ingress api.example.com; memory limit 2Gi; cpu limit 2000m; replicas 3",
"execute": true,
"image": "my-registry/project:v2.0"
}'
# 1. Dry-run (config check)
curl -X POST "$OPSY_API/command" \
-d '{"command": "deploy app to prod", "execute": false, "image": "app:v1.0"}'
# 2. Get task_id and check values.yaml
curl "$OPSY_API/values/{task_id}"
# 3. Deployment
curl -X POST "$OPSY_API/deploy" \
-d '{"task_id": "{task_id}", "app_name": "app", "namespace": "prod"}'
# 4. On error - automatic rollback
helm rollback app {previous-revision} -n prod
POST /command - process natural-language commandsPOST /prepare-environment - generate configurationPOST /deploy - deploy via HelmPOST /retry/{task_id} - retry a failed taskGET /status/{task_id} - task statusGET /values/{task_id} - get values.yamlGET /stats - system statisticsPOST /cleanup - clean up old tasksPOST /detect-project - detect the project typeGET /healthz - health checkGET /livez - liveness probeGET /readyz - readiness probeGET /metrics - Prometheus metricsThe LLM generates configurations and applies best practices automatically, based on the project type.
Deploy with simple commands. The entry barrier is a single line of text.
Security best practices, health probes, resource limits and high availability by default.
Many project types, and all outputs are validated via Helm before deployment.
Non-root containers, seccomp profiles, network policies, RBAC - applied automatically.
Prometheus metrics, health endpoints and automatic health checks.
Opsy AI features are available from the dashboard: deploy, clusters and monitoring in one place.
Pick a GitLab project, a branch, describe the task - AI does the rest. Real-time progress.
Node health, CPU/RAM, deployments and pods. Scaling and rollback in one click.
Deploy statistics, daily charts, success rate by project and full history.
Version comparison and rollback.
Replicas and autoscaling via UI.
Real-time statuses and alerts.
Want to see how Opsy AI automates deploying your applications? We'll set it up in a test environment and show it on your workloads.
Demo mode is available - explore the capabilities right now.