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Continuously analyzes MySQL, MariaDB, and PostgreSQL workloads to recommend and safely apply configuration and query fixes.
Automated AWS usage optimization platform giving engineers 150+ recommendations across 50+ services, averaging ~10% savings.
Atlassian's Git repository hosting for teams with built-in CI/CD pipelines and tight Jira integration for code review and deployment.
FinOps tool that maps cloud and AI spend to the code driving it and auto-generates cost-cutting pull requests.
Agentic AI SRE using dynamic code analysis to find, root-cause, and remediate code and infrastructure issues before production.
Free trial available
Free trial available
No public pricing
Free trial available
No public pricing
Free trial available
Free trial available
- ✦Workload-based configuration tuning
- ✦SQL query analytics and optimization suggestions
- ✦Schema optimization (duplicate/unused index detection)
- ✦24/7 automated health and security monitoring
- ✦One-command agent installation
- ✦Human approval required before applying changes
- ✦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
- ✦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
- ✦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
- →Database teams reducing manual tuning workload
- →Hosting providers optimizing customer databases at scale
- →Engineering teams without a dedicated DBA fixing performance issues
- →AWS RDS users tuning managed database instances
- →Cutting AWS spend automatically
- →Rightsizing over-provisioned resources
- →Scheduling idle resources off-hours
- →Giving DevOps in-console cost recommendations
- →Source code management
- →CI/CD automation
- →Team code review
- →DevOps for Jira-based teams
- →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