Optimize Costs, Enhance Performance & Strengthen Security

Auto-scale agents and orchestrators across heterogeneous workloads—keeping performance high and budget spending under control.

Dynamically route tasks to optimal models or fallback agents using runtime metadata, enabling faster, more resilient outcomes to keep production costs adhered to budgets.

Optimize every token: dynamically select model and prompt templates based on context, confidence, and compute budgets.

Reduce unnecessary token spend by giving your agents persistent context. FloTorch supports long-term memory (knowledge bases for user profiles, policies, and project history) and short-term memory (session-level context via Mem0 or Agent Core Memory) — so your LLM retrieves what it already knows instead of making redundant external calls. For enterprises processing thousands of requests, this directly translates to lower token costs at scale.

Enforce tenant isolation, secure agent-to-agent calls, and audit everything—by default—without slowing innovation.

Trace failures, policy violations, and cost spikes in real time—before they impact customers or budgets.

Full observability into execution paths, token usage, external API latency, and cost attribution across your entire AI workflow.