Fluiq

Fluiq provides comprehensive operational visibility and optimization for large language model applications. Key features include: * Automatic instrumentation for major language model frameworks and SDKs with minimal code change * Continuous evaluation that gates code merges based on performance metrics like hallucination rates * Cross-pipeline benchmarking to compare application performance against industry peers * Real-time cost intelligence with token attribution and latency tracking This platform moves beyond basic dashboards, offering a unified solution for observing, testing, and optimizing language model applications from development to production. It replaces the need for multiple monitoring tools by integrating cost tracking, regression testing, and performance recommendations into a single instrumented pipeline. Teams can monitor per-node token spend, track latency percentiles, and receive alerts on cost or performance spikes. The testing capabilities are robust, enabling automatic regression evaluations for factors like hallucination, faithfulness, and relevancy. Production failures are converted into permanent regression tests, and GitHub Actions can gate merges based on predefined evaluation thresholds. For optimization, Fluiq provides actionable recommendations like semantic caching impact estimates, automatic routing of simple queries to more efficient models, and embedding deduplication across pipelines. Built for engineering teams and developers working with complex language model deployments, Fluiq ensures application stability and efficiency. It supports a wide range of frameworks and SDKs, providing a framework-agnostic approach to performance enhancement and cost management. Optimize your language model pipelines and see how your cache hit rate, latency, and hallucination frequency benchmark against similar deployments.