The AI inference crisis is here. We built the fix.
The industry is 3× over budget. Providers are racing to IPO and cannot subsidize inference anymore. Agentic workloads break every assumption about batched infrastructure. SwarmOne built SwarmOptimizer for exactly this moment.
Talk to UsThe token bill just came due.
Companies exceeded their entire 2026 AI budgets by April. Uber, Microsoft, Meta: they are all scrambling. OpenAI posted a -122% operating margin. The providers cannot subsidize your inference anymore. Agentic workloads (long context, high KV-cache pressure, interactive reasoning) break batched infrastructure. Performance lives at a specific point in a three-axis space: model, workload, and serving stack. Generic endpoints are optimized for everyone. SwarmOne simulates, optimizes, and deploys for your point.
What we build
SwarmOptimizer
Uses simulator ground truth to recursively search serving knobs, KV cache policy, routing, and optionally source.
SwarmSimulator
Turns 1-3 real recordings into tens of thousands of conversations that look new to the KV cache and still preserve reasoning.
Why teams work with SwarmOne
- High-fidelity simulation of real agentic traffic, used as ground truth for continuous optimization
- Production-grade inference across heterogeneous GPU fleets
- Deployed by enterprises, neo-clouds, and chip manufacturers