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Kiro is a spec-driven agentic coding tool for IDE, CLI and web that turns prompts into specs and catches bugs with property-based tests.
Unified API gateway that routes requests to 400+ LLMs across 70+ providers with failover and no subscription.
AI super-assistant plus enterprise ML platform: ChatLLM for teams and end-to-end model building for enterprises; broad, pricing not shown.
Security platform that guards GenAI apps and AI agents against prompt injection, data leaks and misuse for enterprise teams.
Compliance automation platform that continuously monitors controls and evidence to help companies achieve SOC 2, ISO 27001, and HIPAA.
No public pricing
No public pricing
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No public pricing
- ✦Spec-driven development (requirements, design, tasks)
- ✦Parallel agents, local or cloud
- ✦Property-based and correctness testing
- ✦Works in IDE, CLI, web and mobile
- ✦Multiple models (Claude, open-weight, Auto)
- ✦Headless CLI for CI/CD
- ✦Context from tools like Figma and Terraform
- ✦One unified, OpenAI-compatible API for 400+ models
- ✦Automatic provider failover for higher uptime
- ✦Edge routing for low latency
- ✦Custom data and provider policies
- ✦Pay-as-you-go credits usable across any model
- ✦ChatLLM access to multiple top AI models
- ✦AI agents and automation
- ✦No-code full-stack app creation
- ✦Enterprise generative AI platform
- ✦Structured ML model building
- ✦Optimization and forecasting
- ✦Runtime protection for AI agents and apps
- ✦Prompt-injection and jailbreak prevention
- ✦Data-leakage detection in prompts
- ✦Shadow-AI discovery across apps and browsers
- ✦Policy controls by user, app and action
- ✦AI red-teaming and adversarial testing
- ✦Continuous automated compliance monitoring across frameworks
- ✦Automated evidence collection and audit preparation
- ✦Personnel access and permissions management
- ✦Vendor and third-party risk assessment workflows
- ✦Automated security questionnaire responses
- ✦Public-facing trust center for compliance status
- ✦400+ tool integrations and an API for custom workflows
- →Turning prompts into maintainable, spec-matched code
- →Catching bugs unit tests miss
- →Reviewing PRs and fixing bugs in CI/CD
- →Accessing many LLMs through one integration
- →Adding provider redundancy to AI apps
- →Comparing model price and performance
- →Powering agents and AI-native products
- →Chat with many AI models in one place
- →Build and deploy ML models
- →Automate tasks with AI agents
- →Securing conversational and RAG agents
- →Governing employee use of AI tools
- →Adversarial testing before deploying GenAI
- →Meeting AI compliance requirements
- →Preparing for and maintaining SOC 2 or ISO 27001 certification
- →Automating responses to customer security questionnaires
- →Managing vendor security reviews at scale
- →Centralizing risk management across a growing company