Compare tools
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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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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Frugal
✓ verifiedFree trial
FinOps tool that maps cloud and AI spend to the code driving it and auto-generates cost-cutting pull requests.
9.7K visits/mo
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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
Pricing
CloudKeeper Tuner: 2% of monthly AWS bill (1% for CloudKeeper AZ/EDP+ customers)
Free trial available
No public pricing
Free trial available
Free for Developers: $0 (local, single user)
Teams: $450/month (5 microservices, unlimited users)
Free trial available
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
- ✦Maps cloud costs to the code that drives them
- ✦Cost-impact review on every pull request
- ✦Frugalbot agents that generate cost-reducing PRs
- ✦Coverage of storage, logs, AI APIs, serverless and databases
- ✦GitHub and GitLab workflow integrations
- ✦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
Use cases
- →Cutting AWS spend automatically
- →Rightsizing over-provisioned resources
- →Scheduling idle resources off-hours
- →Giving DevOps in-console cost recommendations
- →Cut usage-based cloud and AI bills
- →Add cost reviews to the development workflow
- →Guide developers and AI agents to write cheaper code
- →Find savings that infrastructure right-sizing misses
- →Reducing incident resolution time
- →Catching performance issues pre-production
- →Enhancing AI code reviews with runtime data
- →Monitoring microservice performance
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