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Agentic AI platform ('Aiden') that automates incident response, infrastructure-as-code and observability tasks with policy-based governance.
Cloud-agnostic AI/ML workflow orchestrator that runs pipelines inside a customer's own infrastructure for compute-heavy teams.
Full-stack observability platform with an AI SRE agent that detects, debugs, and auto-fixes issues across infra, apps, and users.
Atlassian's Git repository hosting for teams with built-in CI/CD pipelines and tight Jira integration for code review and deployment.
AI coding platform routing many agents and models through one encrypted, usage-based endpoint with CLI, IDE and multi-agent execution.
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No public pricing
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- ✦Automated service discovery and dependency topology mapping
- ✦SLO-based alert triage and prioritization
- ✦AI-driven root cause analysis with pre-built workflows
- ✦Human-approved remediation with full audit trails
- ✦Works alongside existing tools like Datadog, Grafana, New Relic
- ✦Governance and policy enforcement layer for agent actions
- ✦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
- ✦Infrastructure and application performance monitoring
- ✦Log monitoring with AI insights
- ✦Real user monitoring
- ✦OpsAI SRE agent for detection and auto-fix
- ✦Synthetic and browser testing
- ✦LLM observability
- ✦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
- ✦Unified encrypted inference endpoint
- ✦Multi-agent parallel execution
- ✦CLI, IDE, and API access
- ✦App builder and remote coding agents
- ✦Chairman LLM output evaluation
- ✦35+ IDE integrations
- →SRE teams reducing mean-time-to-resolution during incidents
- →Platform engineers wanting policy-governed AI infrastructure management
- →Enterprises needing SOC 2 / PCI / HIPAA-compliant AI operations
- →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
- →Monitor full-stack app and infra health
- →Debug incidents faster with AI
- →Correlate frontend and backend issues
- →Observe Kubernetes and cloud environments
- →Source code management
- →CI/CD automation
- →Team code review
- →DevOps for Jira-based teams
- →Automating refactors, tests, and migrations
- →Running competing AI coding agents
- →Building apps from prompts
- →Integrating agents into CI/CD