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Open-source asset-based data orchestrator, with Dagster+ cloud, for building, observing and delivering reliable data and AI pipelines.
AI DevSecOps platform running 32 parallel scanners with an AI engine that cuts false positives and auto-generates fix PRs.
FinOps tool that maps cloud and AI spend to the code driving it and auto-generates cost-cutting pull requests.
Diagnoses Kubernetes issues in plain English; well-known open-source developer tool.
Continuously analyzes MySQL, MariaDB, and PostgreSQL workloads to recommend and safely apply configuration and query fixes.
Free trial available
Free trial available
No public pricing
Free trial available
No public pricing
Free trial available
- ✦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
- ✦32 scanners (SAST, SCA, DAST, IaC, secrets, container)
- ✦Securitron AI false-positive filtering
- ✦AI auto-remediation with fix PRs
- ✦ASPM and CSPM posture management
- ✦CI/CD, IDE and MCP integrations
- ✦On-premises deployment option
- ✦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
- ✦AI-Powered Analysis of Kubernetes clusters
- ✦Data Anonymization
- ✦Support for multiple AI providers (OpenAI, Azure, Google, etc.)
- ✦Auto Remediation of common Kubernetes issues
- ✦Claude Desktop Integration
- ✦Fine-Grained Control & Guardrails
- ✦Local AI Models support
- ✦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
- →Orchestrate ETL/ELT and dbt pipelines
- →Monitor data health and lineage
- →Build AI/ML data pipelines
- →Run reliable, observable data platforms
- →Scanning code and cloud for vulnerabilities
- →Reducing false-positive triage
- →Auto-fixing findings via pull requests
- →Meeting compliance (ISO 27001, PCI DSS, SOC2)
- →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
- →Diagnosing and fixing Kubernetes issues with AI-driven insights
- →Automated troubleshooting and remediation of cluster problems
- →Enhancing Kubernetes management with Claude Desktop integration
- →Analyzing cluster state and identifying potential problems
- →Improving Kubernetes workflows
- →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