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Open-source asset-based data orchestrator, with Dagster+ cloud, for building, observing and delivering reliable data and AI pipelines.
Agentic AI SRE using dynamic code analysis to find, root-cause, and remediate code and infrastructure issues before production.
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.
AI-driven platform to visually design multi-cloud infrastructure and auto-generate Terraform code with built-in CI/CD.
Free trial available
Free trial available
Free trial available
No public pricing
Free trial available
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
- ✦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
- ✦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
- ✦Visual multi-cloud architecture designer
- ✦Instant Terraform/OpenTofu code generation
- ✦Drift detection and remediation
- ✦Embedded visual CI/CD engine
- ✦GitOps workflow and RBAC
- ✦AI infrastructure generation from prompts
- →Orchestrate ETL/ELT and dbt pipelines
- →Monitor data health and lineage
- →Build AI/ML data pipelines
- →Run reliable, observable data platforms
- →Reducing incident resolution time
- →Catching performance issues pre-production
- →Enhancing AI code reviews with runtime data
- →Monitoring microservice performance
- →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
- →Designing and deploying cloud infrastructure visually
- →Migrating to Infrastructure as Code
- →Standardizing Terraform modules and naming
- →Detecting drift between design and live cloud