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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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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The New GitBook
✓ verifiedFreemium
Documentation platform for publishing accurate, AI-ready docs sites, with Git sync and an MCP server for AI tools.
653K visits/mo2.9K saves
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exa.ai
✓ verifiedFreemium
Search, crawling and research API built for AI agents, with token-efficient results and structured web-data enrichment.
761K visits/mo1.7K saves
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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
Pricing
CloudKeeper Tuner: 2% of monthly AWS bill (1% for CloudKeeper AZ/EDP+ customers)
Free trial available
No public pricing
Free trial available
Free: $0 (20,000 requests/mo)
Search: $7/1k requests
Contents: $1/1k pages
Deep Search: $12-15/1k requests
Monitors: $15/1k requests
Agent: $0.012-$1.00/run
Team: $950/month + usage (1,000 concurrent actions, 30-day retention, 1 cluster)
Core features
- ✦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
- ✦Publish structured documentation sites
- ✦Git sync for docs-as-code workflows
- ✦AI setup agent to build and import docs
- ✦GitBook MCP server for AI access
- ✦Enterprise controls
- ✦Free tier to start
- ✦Web search API tuned for agents
- ✦Full-page contents with token-efficient highlights
- ✦Asynchronous agents for deep research and enrichment
- ✦Structured outputs with grounded citations
- ✦Web monitors that track new events on a schedule
- ✦Zero data retention and SOC 2 Type II controls
- ✦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
Use cases
- →Cutting AWS spend automatically
- →Rightsizing over-provisioned resources
- →Scheduling idle resources off-hours
- →Giving DevOps in-console cost recommendations
- →Publish product and API documentation
- →Maintain docs-as-code with Git sync
- →Make docs consumable by AI assistants
- →Import existing docs into a hosted site
- →Give coding agents current docs and repo context
- →Power chatbots with real-time web answers
- →Enrich company and people data at scale
- →Monitor the web for fresh events
- →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
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