Sold by: Kubiya.ai
Kubiya AI Teammates Platform is an agentic native platform that transforms DevOps workflows, empowering Platform Engineers to automate operations with context-aware, action-driven AI. Unlike chatbots and traditional agents, Kubiya does not just generate responses, it executes tasks, enforces policies, and integrates deeply with your stack.
4.5
Overview
Why It Matters for Platform Engineers:
- Accelerate Time To Automation - Reduce bottlenecks with automated AI workflows, freeing engineers for high-value work within minutes, not days or weeks.
- Seamless Integration - Connects natively with entire tooling ecosystem and protocols such as Docker, MCP, cloud providers, and messaging platforms.
- Enterprise-Grade Security - Built-in policy enforcement OPA, RBAC ABAC, auditability, and secure local deployment.
- Agentic Execution - Unlike basic chatbots, Kubiya follows workflows to completion autonomously.
How It Works Kubiya operates as a fully extensible, multi-agent managed AI framework designed for Platform Engineering.
- Architecture - AI-driven, LLM-agnostic, modular system integrating deeply with infra-as-code, CI CD, and ITSM tools.
- Connectivity - Pre-built integrations with cloud, Kubernetes, Terraform, GitHub, Jira, Slack, and custom APIs.
- Extensibility - Add new AI capabilities as Terraform-defined modules, ensuring repeatability and control.
- Security and Governance - Fine-grained access control, just-in-time privileges, and full audit logging.
- Agentic-Native Execution - AI-driven workflows handle multi-step processes, validations, and decision-making.
Key Use Cases
- Agent Builder Platform - Build AI-powered Platform Engineering Teammates
- AI Workflow Automation - Automate complex operations
- Enterprise AI Security - Secure automation at scale
Highlights
- Agentic Execution - AI That Gets Work Done Unlike chatbots or traditional AI agents that just provide responses, Kubiya executes tasks end-to-end. It automates DevOps workflows, enforces policies, and integrates deeply with infrastructure to handle real-world operations autonomously.
- Seamless Integration Across the DevOps Stack Kubiya connects natively with Kubernetes, Terraform, GitHub, CI CD pipelines, ITSM tools like Jira and ServiceNow, and cloud providers. It enables context-aware automation without disrupting existing processes.
- Enterprise-Grade Security and Compliance With fine-grained access controls RBAC ABAC, policy enforcement OPA, and full audit logging, Kubiya ensures secure, compliant automation at scale. It supports on-premises deployment and just-in-time privilege elevation for governance.
Details
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Delivery method
Deployed on AWS
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Pricing
Pricing is based on the duration and terms of your contract with the vendor, and additional usage. You pay upfront or in installments according to your contract terms with the vendor. This entitles you to a specified quantity of use for the contract duration. Usage-based pricing is in effect for overages or additional usage not covered in the contract. These charges are applied on top of the contract price. If you choose not to renew or replace your contract before the contract end date, access to your entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/month |
|---|---|---|
Standard | Ideal for growing teams looking to automate DevOps workflows with AI teammates. Includes up to 10 AI teammates, 250 function call units per month, and 3 environments, making it a great fit for teams seeking efficient self-service automation. | $0.01 |
Enterprise | Designed for large-scale organizations needing custom AI teammates, unlimited automation capacity, and enterprise-grade security. Offers tailored function call limits, environments, and project configurations to fit specific business needs. | $0.01 |
Dimension | Description | Cost/unit |
|---|---|---|
Standard Plan | Standard Plan - 250 FCUs | $0.002 |
Enterprise Plan | Enterprise Plan - Custom | $0.001 |
Vendor refund policy
All fees are non-cancellable and non-refundable except as required by law.
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Support
Vendor support
For support please reach our to support@kubiya.ai
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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AI generated from product descriptions
AI-Driven Workflow Automation
Multi-agent managed AI framework capable of executing complex multi-step processes with autonomous decision-making capabilities
Infrastructure Integration
Native connectivity with cloud providers, Kubernetes, Terraform, GitHub, Jira, Slack, and custom API ecosystems
Security Governance
Fine-grained access control mechanisms including RBAC, ABAC, OPA policy enforcement, and comprehensive audit logging
Modular Architecture
Extensible, LLM-agnostic system designed with Terraform-defined modules for adding new AI capabilities
Autonomous Task Execution
AI-powered platform that performs end-to-end task completion with context-aware operational workflows
Multi-Cloud Workflow Orchestration
Automate security and IT operations across multiple cloud platforms including AWS, Azure, GCP, Kubernetes, CI/CD, IAM, vulnerability scanners, and threat intelligence platforms
Generative AI Workflow Generation
LLM-powered multi-agent system that generates and contextualizes automation workflows in real-time for threat hunting, misconfiguration prevention, and security findings enrichment
Workflow Development Flexibility
Visual no-code workflow builder with capability to inject custom code for complex logic, supporting both technical and non-technical team members
Policy Enforcement Mechanism
Embedded human approval workflows and policy checks to ensure safe and compliant automation execution
Deployment Isolation
Self-hosted deployment option with full data control and environment isolation to meet compliance and security requirements
Autonomous Agent Architecture
Context-aware AI agents powered by Retrieval-Augmented Generation (RAG) and Bedrock that can understand and act on real-time data across multiple systems
Multi-System Integration Framework
Native support for 4,000+ APIs across cybersecurity, cloud, and IT platforms with comprehensive integration capabilities
No-Code Workflow Generation
AI-driven workflow creation using semantic search, natural language prompts, and contextual step generation without requiring manual scripting
Access Control and Governance
Role-based access control (RBAC), comprehensive audit logging, action approvals, and full execution traceability for secure automation
Dynamic Workflow Orchestration
Support for both deterministic and indeterministic workflows with ability to trigger actions via webhooks, schedulers, and collaborative AI Rooms
Standard contract
Customer reviews
RiteshWalia
Automating repetitive SRE tickets has transformed how our team operates daily
Reviewed on Jan 05, 2026
Review from a verified AWS customer
What is our primary use case?
We were looking for a SRE assistant with Kubiya.ai that could help us with daily routine tasks by automating them or functioning as an AI agent that could perform actions for us. For example, if we received a customer ticket, we wanted to automate that process.
One specific example of a task that Kubiya.ai helps automate for our team is addressing ticket fatigue. We were removing the middleman work for our DevOps engineers. Instead of manually servicing repetitive tickets, such as restarting pods or granting access to particular resources, Kubiya.ai automated this through chat. Our developers gained self-service capabilities without needing to learn Terraform or CLI commands, and our DevOps team members could focus on architecture rather than support tickets.
What is most valuable?
Kubiya.ai functions as a DevOps or SRE assistant for us. It is not merely a chatbot or an LLM interface or exactly ChatGPT, but rather an action-oriented platform. Most AI bots simply chat, whereas Kubiya.ai is a complete agentic platform built to execute code. It connects directly to our Kubernetes , AWS , GitHub , and Jira and other tools to perform end-to-end workflows.
A particularly good example is our ability to spin up a dev environment for payment services. Kubiya.ai triggers the Terraform script, waits for completion, and pastes the URL back in Slack. This end-to-end workflow execution is exceptionally valuable. Additionally, Kubiya.ai is security-oriented. A common concern in AI for DevOps is that a bot could accidentally delete a production database. Kubiya.ai solved this with strict role-based access control and human-in-loop features. If a request appears risky, it can ping a manager for approval before executing.
We saved considerable time regarding productivity with Kubiya.ai. We did not need to hire as many resources for support tickets, and the process was quite smooth.
What needs improvement?
Kubiya.ai's billing structure is somewhat complex. The usage-based pricing charges per function call, which can lead to unpredictable bills if adoption spikes unexpectedly. Enterprise pricing is quite high. The setup complexity could also be improved. There are trust and hallucination risks, though these generally depend on how we use the AI.
For how long have I used the solution?
We have been using Kubiya.ai for the past year.
What do I think about the stability of the solution?
Kubiya.ai is stable. We have not encountered any issues so far, though we may have more insight in the future.
What do I think about the scalability of the solution?
We have not seen any scalability issues with Kubiya.ai. We do not have extensive data in this area, so we are currently satisfied.
How are customer service and support?
We have not reached out to customer support for Kubiya.ai. However, we can share feedback once we have experience with that service.
How would you rate customer service and support?
Which solution did I use previously and why did I switch?
We were using Kubernetes GPT as a previous solution. However, we needed something very quick and an enterprise solution, which is why we chose Kubiya.ai.
How was the initial setup?
My experience with pricing, setup cost, and licensing has been smooth. Currently, we are in the initial phase, so I cannot comment extensively on this area. We are still validating those metrics and will provide additional information once we have gathered more data.
What other advice do I have?
Kubiya.ai is best suited for large engineering teams that suffer from numerous repetitive support tickets. Teams that use Slack, such as ours, have benefited significantly from Kubiya.ai without requiring developers to learn new tools, and we can manage everything directly from Slack.
I would rate Kubiya.ai nine out of ten because it has completely revolutionized the way we operate in Kubernetes. I chose nine out of ten instead of a perfect ten because if you are a small startup or individual, the cost and setup overhead will likely outweigh the time saved. Additionally, you need a zero-trust environment where no automated system is allowed to touch production infrastructure without manual human execution. These factors depend on your specific use cases. In my personal opinion, the cost factor is something to consider heavily.
Others looking into using Kubiya.ai should try it. It is truly a good enterprise solution, and the way it is designed to execute end-to-end agentic AI workflows is exceptional, particularly if you need a SRE assistant. My overall review rating for Kubiya.ai is nine out of ten.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)