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Side-by-side features, use cases and pricing — because the right pick depends on your job and budget, not just the ranking.
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Union Cloud
✓ verifiedPaid
Cloud-agnostic AI/ML workflow orchestrator that runs pipelines inside a customer's own infrastructure for compute-heavy teams.
25K visits/mo
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CloudKeeper Tuner
✓ verifiedPaid
Automated AWS usage optimization platform giving engineers 150+ recommendations across 50+ services, averaging ~10% savings.
43K visits/mo
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Tiptap Editor 3.0 Beta
✓ verifiedFreemium
Headless, open-source rich-text editor framework with paid add-ons for collaboration, comments, AI editing agents and document conversion.
308K visits/mo
Pricing
Team: $950/month + usage (1,000 concurrent actions, 30-day retention, 1 cluster)
CloudKeeper Tuner: 2% of monthly AWS bill (1% for CloudKeeper AZ/EDP+ customers)
Free trial available
Start: $49/mo (up to 500 cloud documents, 2 environments, 2 dev licenses)
Team: $149/mo (up to 5,000 cloud documents, 3 environments, 5 dev licenses)
Business: $999/mo (up to 50,000 cloud documents, 5 environments, 10 dev licenses)
Free trial available
Core features
- ✦Python-native dynamic workflow authoring
- ✦Automatic failure recovery, caching, and versioning
- ✦Zero Trust architecture keeping data inside customer's cloud
- ✦Real-time inference and agentic-AI workflow support
- ✦High-throughput scaling (tens of thousands of actions per run)
- ✦Local development environment matching production behavior
- ✦150+ recommendations across 50+ AWS services
- ✦Zombie and unused resource cleanup
- ✦Over-provisioned rightsizing
- ✦Idle-resource scheduler
- ✦SpotBot for ECS Fargate spot/on-demand switching
- ✦AWS console extension with Slack/Teams alerts
- ✦Headless, extensible core editor with 100+ extensions
- ✦Real-time collaborative editing with live cursors
- ✦Inline and document comments
- ✦DOCX, ODT and Markdown import/export
- ✦AI Toolkit for building document-editing AI agents
- ✦Prebuilt UI components and editor templates
Use cases
- →ML teams orchestrating training and inference pipelines at scale
- →Biotech/geospatial companies needing GPU-heavy pipeline orchestration
- →Enterprises migrating off Airflow for ML workflow management
- →Teams requiring workflows that never send data outside their own cloud
- →Cutting AWS spend automatically
- →Rightsizing over-provisioned resources
- →Scheduling idle resources off-hours
- →Giving DevOps in-console cost recommendations
- →Building a custom rich-text editor for a SaaS product
- →Adding real-time collaboration to a document app
- →Letting an AI agent edit documents with tracked changes
- →Importing or exporting Word or Markdown content in-app
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