Keywords AI
The enterprise-grade software to build, monitor, and improve your AI application. Keywords AI is a full-stack LLM engineering platform for developers and PMs.
What is Keywords AI?
Keywords AI is an enterprise-grade LLM engineering platform designed to help developers and product managers build, deploy, and optimise AI applications with confidence. It provides a unified solution that combines observability, evaluation tools, prompt optimization, and an LLM gateway, eliminating the need to stitch together multiple tools. The platform enables teams to monitor AI application performance in real-time, identify issues quickly, and continuously improve model outputs through systematic testing and optimization. Keywords AI is particularly valuable for organizations moving beyond proof-of-concept AI applications and needing production-grade monitoring and reliability tools.
Key Features
Unified LLM Gateway
Route requests across multiple LLM providers with a single integration point
Observability & Monitoring
Track token usage, latency, costs, and performance metrics across all LLM calls
Prompt Optimization
Test, version, and optimise prompts systematically to improve output quality
Evals Framework
Set up automated evaluations and benchmarks to measure AI application performance
Error Tracking & Debugging
Identify and diagnose issues in AI responses with detailed logging
Cost Management
Monitor and optimise LLM spending across different models and providers
Pros & Cons
Advantages
- All-in-one platform reduces tool fragmentation and integration overhead
- Enterprise-grade reliability with focus on production-ready AI applications
- Freemium model allows teams to get started without upfront investment
- Unified gateway simplifies multi-model LLM management
- Built for both developers and product managers with accessible interfaces
Limitations
- Learning curve for teams new to LLM engineering best practices
- Enterprise features may require higher-tier plans for advanced customization
- Pricing details for paid tiers not clearly published, requiring contact with sales
Use Cases
Production AI applications requiring real-time monitoring and reliability
Teams optimising LLM prompts across multiple models and providers
Cost-conscious organizations tracking and reducing AI infrastructure spending
Quality assurance for AI-powered customer-facing features
Multi-model deployments requiring centralized gateway and observability