Kura
AI DevOps copilot for AWS cloud infrastructure management and incident response.
Kura is an AI DevOps copilot that helps software teams manage and optimize cloud infrastructure. It is a chatbot and workspace that integrates directly with a live index of your cloud from AWS, GCP and Azure, answering questions with links and live information, generating code, provisioning and scaling resources, and responding to incidents through natural language while keeping teams in full control.
Kura runs pre-packaged and user-defined runbooks, including scheduled checks that proactively surface issues such as over or under-utilized resources. It was founded in 2024 in San Francisco by Trevor Reed and Mark Dawson, engineers from Google and NASA, is Y Combinator-backed, and is onboarding pilot customers.
Vendor details
Canonical URL
https://www.usekura.com
Category
SRE / DevOps agent
Company status
independent
Use cases & customers
Primary use cases
Target customers
Deployment options
Integrations
Live index of your cloud infrastructure across AWS, GCP and Azure.
Sources & related URLs
Research sources
Agentic Index coverage score
7.0 / 14 capabilities · 50%
| Integrations & Tool Calling | Full |
|---|---|
|
Integrates directly with a live index of your cloud infrastructure across AWS, GCP and Azure, Kura site 2026-07-22 Sourceusekura.comread 2026-07-22 |
|
| Workflow Orchestration | Full |
|
Runs pre-packaged and user-defined runbooks and provisions infrastructure and responds to incidents through natural language, Kura site 2026-07-22 Sourceusekura.comread 2026-07-22 |
|
| Knowledge Grounding & RAG | Full |
|
Integrates with a live index of your cloud infrastructure to answer questions with links and live information, grounding responses in current cloud state, Kura site 2026-07-22 Sourceusekura.comread 2026-07-22 |
|
| Human Oversight & Guardrails | Full |
|
A copilot model that keeps engineers in full control of their critical infrastructure, Kura site 2026-07-22 Sourceusekura.comread 2026-07-22 |
|
| Security, Identity & Governance | Partial |
|
Emphasizes full customer control of infrastructure, but specific security certifications are not documented and the product is early stage, Kura site 2026-07-22 Sourceusekura.comread 2026-07-22 |
|
| Observability & Auditability | Partial |
|
Provides real-time insights and visibility over a live index of your cloud, though it is a copilot layer over cloud provider telemetry rather than its own observability platform, Kura site 2026-07-22 Sourceusekura.comread 2026-07-22 |
|
| Memory & State Persistence | Unable to verify |
|
No persistent memory or cross-incident learning architecture is documented, Kura site 2026-07-22 Sourceusekura.comread 2026-07-22 |
|
| Deployment & Data Residency | Partial |
|
SaaS that connects to your cloud with an emphasis on retaining control; self-host or residency specifics are not documented, Kura site 2026-07-22 Sourceusekura.comread 2026-07-22 |
|
| Prebuilt Agents, Templates & Packs | Partial |
|
Ships pre-packaged runbooks alongside user-defined ones, a runbook library rather than a broad prebuilt agent catalog, Kura site 2026-07-22 Sourceusekura.comread 2026-07-22 |
|
| Triggers & Channel Coverage | Partial |
|
Mainly user-initiated through chat, with scheduled runbook checks that proactively surface issues via notifications; broad alert-channel triggering is not documented, Kura site 2026-07-22 Sourceusekura.comread 2026-07-22 |
|
| Model Flexibility & Routing | Unable to verify |
|
No customer facing model choice or routing documented, Kura site 2026-07-22 Sourceusekura.comread 2026-07-22 |
|
| APIs, SDKs & MCP Extensibility | Partial |
|
Supports user-defined runbooks for customization, though a public API, SDK or MCP extensibility surface is not documented, Kura site 2026-07-22 Sourceusekura.comread 2026-07-22 |
|
| Testing, Debugging & Optimization | Unable to verify |
|
No agent evaluation or testing tooling documented, Kura site 2026-07-22 Sourceusekura.comread 2026-07-22 |
|
| Browser & Computer Use | Unable to verify |
|
Operates through chat and cloud APIs rather than browser or computer interface control, Kura site 2026-07-22 Sourceusekura.comread 2026-07-22 |
|
The Agentic Index coverage score grades every vendor Full, Partial or Unable to verify against the same 14 buyer facing capabilities, from public evidence only. Each capability links to how all vendors in the index score on it. How this evidence is graded
Pricing
Contact founders (pilot / early stage)
What is public
No public pricing; early-stage YC company onboarding pilot customers directly.
Variable cost rationale
Early stage; no public pricing, pilot engagements arranged directly with founders.
Sales call required
Yes, required for paid access
Free / trial
Pilot program
Related vendors
- Alertd — Agentic AI SRE and DevOps platform for AWS that analyzes cost,…
- Anyshift — AI SRE agent that investigates production incidents by tracing…
- Avesha — Autonomous AI SRE platform whose coordinated agent swarm monitors,…
- Beeps — Agent native on call platform that routes incidents to humans and AI…
- Better Stack — AI native incident management with built in on call and status…
- Bluebricks — Context and control layer that lets AI agents operate cloud…
Alternatives to Kura
The closest documented capability profiles to Kura among SRE and DevOps agents tracked by Agentic Index, ordered by similarity on the same 14 point evidence the rankings use. No vendor pays for placement.
- Ciroos AI8.0 / 14Fuller documented coverage on Triggers & Channel Coverage and APIs, SDKs & MCP Extensibility
- Harness8.0 / 14Adds documented Memory & State Persistence
- Parity7.0 / 14Fuller documented coverage on Triggers & Channel Coverage
- SRE.ai8.0 / 14Adds documented Memory & State Persistence and Testing, Debugging & Optimization
- Anyshift8.5 / 14Adds documented Memory & State Persistence and Testing, Debugging & Optimization
- Beeps6.0 / 14Fuller documented coverage on Triggers & Channel Coverage
Similarity is computed from each vendor's Agentic Index coverage score evidence, axis by axis, not from the totals. How this evidence is graded