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Open-source asset-based data orchestrator, with Dagster+ cloud, for building, observing and delivering reliable data and AI pipelines.
Cloud-agnostic AI/ML workflow orchestrator that runs pipelines inside a customer's own infrastructure for compute-heavy teams.
AI-driven platform to visually design multi-cloud infrastructure and auto-generate Terraform code with built-in CI/CD.
Serverless cloud browser API with persistent sessions, built-in stealth and proxies, for AI agents, automation and scraping.
Local deep packet inspection and network intelligence giving businesses full visibility into application, VPN and Tor traffic.
Free trial available
Free trial available
Free trial available
No public pricing
- ✦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
- ✦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
- ✦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
- ✦Serverless cloud Chromium browsers
- ✦Persistent logged-in sessions
- ✦Built-in stealth / anti-detect fingerprinting
- ✦Residential & datacenter proxies in 150+ countries
- ✦Playwright/Puppeteer WebSocket connection
- ✦Headful mode for live inspection
- ✦Local deep packet inspection agent
- ✦Application identification data feeds
- ✦VPN and Tor IP datasets
- ✦Network informatics and analytics
- ✦Developer documentation
- →Orchestrate ETL/ELT and dbt pipelines
- →Monitor data health and lineage
- →Build AI/ML data pipelines
- →Run reliable, observable data platforms
- →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
- →Designing and deploying cloud infrastructure visually
- →Migrating to Infrastructure as Code
- →Standardizing Terraform modules and naming
- →Detecting drift between design and live cloud
- →Running AI browser agents
- →Web scraping at scale
- →Automating logged-in workflows
- →Debugging automation in live sessions
- →Classify application traffic on a network
- →Detect VPN and Tor usage
- →Feed DPI data into security and analytics tools
