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Defang logo
Defang
✓ verifiedFreemium

Developer tool that deploys Docker Compose apps (with LLMs and databases) into your own AWS, GCP or Azure account via one command.

20K visits/mo
Digma.ai logo
Digma.ai
✓ verifiedFreemium

Agentic AI SRE using dynamic code analysis to find, root-cause, and remediate code and infrastructure issues before production.

13K visits/mo
Bitbucket logo
Bitbucket
✓ verifiedFreemium

Atlassian's Git repository hosting for teams with built-in CI/CD pipelines and tight Jira integration for code review and deployment.

13M visits/mo
Middleware logo
Middleware
✓ verifiedFreemium

Full-stack observability platform with an AI SRE agent that detects, debugs, and auto-fixes issues across infra, apps, and users.

47K visits/mo713 saves
Pricing
Starter: $0 (1 cloud account)
Pro: $49/mo (1 account, +$29/mo per extra)
Enterprise: $499/mo (3 accounts, +$49/mo per extra)
Free for Developers: $0 (local, single user)
Teams: $450/month (5 microservices, unlimited users)

Free trial available

No public pricing

Free trial available

No public pricing

Core features
  • One-command deploy from Docker Compose
  • Deploys into your own or a customer's cloud account
  • Native managed LLM access (Bedrock/Vertex/Azure AI)
  • Managed Postgres, MongoDB and Redis
  • Auto-configured IAM, VPC, TLS and load balancing
  • Open-source CLI and cloud providers
  • Dynamic Code Analysis engine
  • Automated root-cause analysis and remediation
  • Pull-request and config fix suggestions
  • MCP server for AI-assisted code review
  • Observability and data-source integrations
  • Runs locally or on-prem/private cloud
  • Git repository hosting
  • Bitbucket Pipelines CI/CD
  • Pull requests and code review
  • Native Jira integration
  • Branch permissions and access controls
  • IP allowlisting and security features
  • Infrastructure and application performance monitoring
  • Log monitoring with AI insights
  • Real user monitoring
  • OpsAI SRE agent for detection and auto-fix
  • Synthetic and browser testing
  • LLM observability
Use cases
  • Shipping AI agents and web apps to production
  • Deploying the same app across many customer clouds
  • Agencies deploying into client cloud accounts
  • Avoiding hand-written Terraform or Kubernetes
  • Reducing incident resolution time
  • Catching performance issues pre-production
  • Enhancing AI code reviews with runtime data
  • Monitoring microservice performance
  • Source code management
  • CI/CD automation
  • Team code review
  • DevOps for Jira-based teams
  • Monitor full-stack app and infra health
  • Debug incidents faster with AI
  • Correlate frontend and backend issues
  • Observe Kubernetes and cloud environments
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