Fast, affordable inference for Llama, Mixtral, and other open-weight models — as the conversation engine behind your Fluents agents.
Run Open-Weight Models via Together AI in Your Fluents Stack
Together AI specializes in high-performance, cost-efficient inference for open-weight LLMs — making Llama 3.1, Mixtral, Qwen, and other leading open-source models accessible at scale without the cost of proprietary frontier models or the infrastructure overhead of self-hosting.
For Fluents deployments where open-weight model quality meets the bar and cost-per-call economics are important, Together AI is the inference layer that makes it practical at volume.
Access Llama 3.1, Mixtral, and other leading open-weight models as the conversation engine for your Fluents agents at competitive inference pricing
Faster inference than self-hosting — Together AI's optimized infrastructure delivers lower latency than typical cloud GPU setups
No GPU infrastructure to manage — Together AI handles capacity, scaling, and uptime while Fluents handles the calls
The Open-Weight Inference Problem
Open-weight models like Llama are free to use — but running them at production scale requires serious GPU infrastructure, 24/7 uptime management, and ongoing optimization. Together AI solves this by providing managed inference for these models at prices well below proprietary frontier models. For Fluents customers running tens of thousands of calls per month, the cost difference between Together AI-hosted Llama and Gemini can be significant.
High-Volume Outbound: Cost Efficiency Without Quality Sacrifice
An insurance carrier running 200,000 outbound renewal reminder calls per month doesn't need the most powerful LLM available for every call. Llama 3.1 70B via Together AI delivers strong instruction-following and natural conversation quality — more than sufficient for structured reminder workflows — at a fraction of the cost of frontier models. The savings compound at scale.
Healthcare: Privacy-Forward Open-Weight Deployment
Some healthcare organizations prefer open-weight models because the model architecture is auditable and the weights are inspectable — properties that closed proprietary models don't offer. Together AI provides managed inference for these models with enterprise data processing agreements, giving healthcare teams the privacy-forward model choice without the infrastructure burden.