Compare tools
Side-by-side features, use cases and pricing — because the right pick depends on your job and budget, not just the ranking.
⇄ Comparison dimension — pick the market you're actually shopping in
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Zeabur
✓ verifiedFreemium
Cloud deployment platform for developers that auto-detects code and frameworks to ship apps, servers, and AI-hub services with one push.
455K visits/mo72 saves
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Dagster
✓ verifiedFreemium
Open-source asset-based data orchestrator, with Dagster+ cloud, for building, observing and delivering reliable data and AI pipelines.
152K visits/mo
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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
Free: $0/mo (1 manageable server)
Dev: $5/mo (first 14 days free, 3 servers)
Pro: $19/mo (first 14 days free, 10 servers)
Team: $79/mo (3 seats included, +$24/seat/mo)
Free trial available
Solo: $10/mo + $0.040/credit (1 user)
Starter: $100/mo + $0.035/credit (up to 3 users)
Free trial available
No public pricing
Core features
- ✦Automatic language and framework detection and deployment
- ✦Git-push CI/CD with zero configuration
- ✦Auto-scaling compute resources
- ✦Built-in object storage similar to S3
- ✦One-click managed VPS purchase
- ✦Unified AI Hub API for multiple AI models
- ✦Domain and DNS management
- ✦In-browser file management console
- ✦Asset-based pipeline orchestration
- ✦Built-in lineage and data-quality checks
- ✦Data catalog with asset metadata
- ✦Native dbt, Snowflake and Fivetran integrations
- ✦Branch deployments and hybrid deployment
- ✦Open-source core plus managed Dagster+ cloud
- ✦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
- →Developers deploying apps without manual server config
- →Teams wanting predictable, fixed-plan hosting costs
- →Startups needing quick CI/CD pipelines
- →Projects needing bundled AI model access alongside hosting
- →Orchestrate ETL/ELT and dbt pipelines
- →Monitor data health and lineage
- →Build AI/ML data pipelines
- →Run reliable, observable data platforms
- →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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