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.
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What is Keywords AI?
Keywords AI is a platform designed to help developers and product managers build, deploy, and maintain AI applications at scale. It combines observability, evaluation tools, prompt optimisation, and a unified gateway for managing multiple large language models in one place. The platform sits between your application and various LLM providers, giving you visibility into how your AI is performing and tools to improve reliability without reworking your entire codebase. It's particularly useful for teams moving beyond simple prototypes to production systems where monitoring costs, latency, and output quality matters.
Key Features
LLM gateway
Route requests to different language models through a single interface, with load balancing and fallback options
Observability
Track API calls, costs, latency, and token usage across all your LLM integrations
Prompt evaluation
Test and compare different prompts against your data to measure performance before deployment
Prompt optimisation
Iteratively improve prompts based on real-world performance data
Monitoring dashboard
View metrics and alerts to catch issues in production quickly
Pros & Cons
Advantages
- Gives you detailed visibility into LLM behaviour and costs, which is important for controlling expenses
- Works with multiple LLM providers without forcing vendor lock-in
- Freemium model lets you try the core features without committing to a paid plan
- Reduces the overhead of managing different API integrations across your team
Limitations
- Requires integrating another service into your application pipeline, adding a dependency
- Learning curve for teams new to LLM observability and optimisation workflows
Use Cases
Monitoring costs and performance of LLM-powered customer support chatbots
A/B testing different prompts for content generation at scale
Tracking reliability metrics for AI features in production applications
Optimising latency and quality trade-offs when using multiple LLM providers
Debugging unexpected behaviour in deployed AI models