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Platform to test, evaluate and observe LLM and voice AI agents, with prompt management and red-teaming for production.
Open-source AI gateway giving dev teams unified access, fallbacks and spend tracking across 100+ LLMs.
AI super-assistant plus enterprise ML platform: ChatLLM for teams and end-to-end model building for enterprises; broad, pricing not shown.
Agentic AI platform with a coding desktop app, CLI, and cloud agents for autonomous software development and office work.
Enterprise AI observability and security platform to monitor, evaluate, and govern agentic and ML systems with guardrails.
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No public pricing
Free trial available
- ✦Scenario-based agent testing
- ✦LLM evaluation and quality scoring
- ✦Observability for cost and latency
- ✦Prompt management with GitHub sync
- ✦Voice AI simulation
- ✦LLM red-teaming and governance
- ✦Unified access to 100+ LLMs in OpenAI format
- ✦Cost/spend tracking per key, user and team
- ✦Budgets and rate limiting
- ✦Automatic provider fallbacks and retries
- ✦Virtual keys and team management
- ✦Logging and observability integrations
- ✦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
- ✦Multi-agent collaboration for end-to-end tasks
- ✦Persistent memory and custom rules
- ✦Extensible skills and plugins
- ✦Rich context across code, images, and directories
- ✦Automatic codebase documentation generation
- ✦Terminal-native CLI and JetBrains IDE plugin
- ✦Cloud-hosted agents for enterprise use
- ✦End-to-end agentic and ML observability
- ✦Real-time guardrails (hallucination, PII, jailbreak)
- ✦Continuous evaluations and custom judges
- ✦Root-cause analysis and decision lineage
- ✦AI governance, risk, and compliance controls
- ✦Flexible SaaS, VPC, or on-prem deployment
- →Catch agent issues before production
- →Evaluate and monitor LLM quality
- →Test voice AI agents at scale
- →Giving developers governed access to many LLMs
- →Attributing and controlling LLM spend
- →Keeping apps running during provider outages
- →Chat with many AI models in one place
- →Build and deploy ML models
- →Automate tasks with AI agents
- →Autonomous feature development in large codebases
- →Terminal-based AI pair programming
- →Cross-department task automation for legal, finance, HR
- →Onboarding developers to unfamiliar codebases
- →Monitoring production AI agents
- →Enforcing safety guardrails on LLM apps
- →Evaluating and debugging model behavior
- →Governance and compliance for enterprise AI