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Atlassian's Git repository hosting for teams with built-in CI/CD pipelines and tight Jira integration for code review and deployment.
Cloud deployment platform for developers that auto-detects code and frameworks to ship apps, servers, and AI-hub services with one push.
Open-source asset-based data orchestrator, with Dagster+ cloud, for building, observing and delivering reliable data and AI pipelines.
AI-driven platform to visually design multi-cloud infrastructure and auto-generate Terraform code with built-in CI/CD.
No public pricing
Free trial available
Free trial available
Free trial available
Free trial available
- ✦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
- ✦Automatic language and framework detection and deployment
- ✦Git-push CI/CD with zero configuration
- ✦Auto-scaling compute resources
- ✦Built-in object storage similar to S3
- ✦One-click managed VPS purchase
- ✦Unified AI Hub API for multiple AI models
- ✦Domain and DNS management
- ✦In-browser file management console
- ✦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
- ✦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
- →Source code management
- →CI/CD automation
- →Team code review
- →DevOps for Jira-based teams
- →Developers deploying apps without manual server config
- →Teams wanting predictable, fixed-plan hosting costs
- →Startups needing quick CI/CD pipelines
- →Projects needing bundled AI model access alongside hosting
- →Orchestrate ETL/ELT and dbt pipelines
- →Monitor data health and lineage
- →Build AI/ML data pipelines
- →Run reliable, observable data platforms
- →Designing and deploying cloud infrastructure visually
- →Migrating to Infrastructure as Code
- →Standardizing Terraform modules and naming
- →Detecting drift between design and live cloud