Hundreds of AI models through one API — flexible, affordable conversation engine access for Fluents.
Fluents + AI/ML API: Broad Model Access for Your Voice AI Stack
AI/ML API aggregates access to hundreds of AI models — LLMs, image generation, speech, and more — through a single unified endpoint with competitive pricing. For Fluents teams that want access to a wide range of conversation engine options without signing separate agreements with each model provider, AI/ML API simplifies the model access layer significantly.
Connect your Fluents deployment to AI/ML API and gain the ability to swap conversation engines, test new model releases, and optimize costs across a broad catalog — all through one integration.
Access hundreds of LLMs through a single AI/ML API endpoint — no separate API keys, vendor agreements, or rate limit negotiations per model
Competitive per-token pricing across a broad model catalog makes cost-per-call optimization straightforward
Test new model releases in your Fluents stack as soon as they're available through AI/ML API — no integration changes required
The Model Proliferation Problem
The LLM landscape moves fast. New models are released monthly, pricing changes constantly, and the best model for a given task shifts over time. Managing separate API integrations for Gemini, Claude, Llama, Mistral, and others is an engineering overhead that grows with the model landscape. AI/ML API collapses this into one integration — Fluents connects once, and you access the whole catalog.
Staying Current in Fast-Moving AI
When Meta releases Llama 3.2 or Mistral releases a new fine-tuned variant that benchmarks better on intake workflows, AI/ML API makes it available immediately. Teams using Fluents with AI/ML API can evaluate and switch to new models without waiting for new vendor agreements or API integrations — keeping their voice AI stack at the frontier without engineering work.
Cost Optimization Across the Catalog
AI/ML API's unified pricing dashboard makes it easy to compare per-token costs across models and select the most cost-effective option that meets quality thresholds for each call type. High-volume, low-complexity calls get routed to affordable models; complex intake calls get routed to premium ones. The optimization work happens at the AI/ML API layer, not in Fluents.