7+ SEC
680MS
11× faster ttft
680ms vs 7+ seconds
Time to first token
Your token bill is out of control. We fix that.
A new model drops. Generic endpoints are still optimized for everyone. SwarmOptimizer simulates your real agentic workloads and tunes the stack to yours.
Measured on real agentic workloads. Not synthetic loads.
3.5× faster decode
106 vs 47-50 tok/s/user
Per-user decode speed
OPTIMIZED COST
80% lower cost
On existing GPU hardware
Existing GPU hardware
Published speed numbers are often 1k/1k or about 20k context. Real agentic traffic is 100-150k, multi-turn, with tool calls. On that load, public endpoints often drop to 20-30 tok/s per user. SwarmOne reached 140 tok/s per user on real agentic workloads.
Infrastructure should adapt to your workload.Not the other way around.
Providers cannot subsidize inference anymore. Your workloads are consuming tokens faster than anyone predicted. SwarmOne finds the configuration that makes every token count.
100-150
tok/s per user
Commodity hardware typically lands at 30-40 tokens per second per user on real agentic traffic. After optimization: 100-150. Specialized hardware: 300-600.
Your stack at its best in hours, not weeks.
Deploy, simulate real agentic traffic at fleet scale, optimize across software and hardware, redeploy. The simulator is the ground truth, so the AI cannot cheat. Repeats every 24 hours.
INPUT
Real agent traces
SWARMOPTIMIZER
Search the full stack
OUTPUT
Winning config
Model, cache, kernel, batch, hardware
Production feedback returns to the next optimization cycle
Simulate
Understand real behavior
01
- 1-3 real recordings become tens of thousands of conversations
- Same reasoning trajectory. New to the KV cache every time
- Fleet-scale replay with contention, not a hot-cache illusion
Optimize
Find the best config
02
- Knobs-only: typically 5-8× vs an untuned generic stack
- KV cache policy, routing, serving stack, and optional source changes
- Scarce expertise, applied by hand, once. Now the AI searches. An engineer steers.
Deploy
Ship optimized config
03
- Push the winning config to production
- Keep looping nightly. New models reuse prior experiments
- A 3-day research cycle can compress to 4-5 hours
Continuous loop. Runs every 24h. More time, more lift. New models reuse prior experiments.
The Simulator informs. The Optimizer optimizes.
A few real traces in. A stack that matches production out. SwarmSimulator is the ground truth. SwarmOptimizer tunes the serving stack against it.
Honest workload simulation
SwarmSimulator
Stop guessing. Start simulating. Replay of the same traces keeps the cache hot and lies. SwarmSimulator takes 1-3 real recordings, expands them to tens of thousands of conversations, and replays at fleet scale so every number matches production.
Record: 1-3 real agent traces and tool calls
Perturb: New to the KV cache, same reasoning path
Simulate: Tens of thousands of fleet-scale conversations
Match: Ground truth the optimizer cannot fake
The full solution
SwarmOptimizer
Recursively optimizes your inference stack: serving knobs, env vars, KV cache policy, routing, kernels, and optionally source. Uses SwarmSimulator as unbiased ground truth. Plug in SwarmOne's optimizer or yours.
Knobs-only: typically 5-8× vs generic untuned stacks
KV cache analysis across hundreds of policies
24h continuous re-optimization
Works with your optimizer or ours
A living map of every inference decision.
Workload drift changes the terrain. SwarmOptimizer keeps searching for the lowest-cost path through it.
Hardware is not the bottleneck. The stack is.
SwarmOptimizer works across any silicon, any cloud, any framework. Live cluster for highest fidelity. Vendor simulator (like NVIDIA DynoSim) for speed. No rewrites. No lock-in.
SILICON
- NVIDIA
- AMD
- Intel
- Tenstorrent
- Groq
- Cerebras
CLOUD
- AWS
- Azure
- Google Cloud
- DigitalOcean
- Nebius
- Scaleway
SwarmOne
FRAMEWORKS
- PyTorch
- Hugging Face
- W&B
- NVIDIA
- MLflow
- ClearML
MORE TARGETS
- Crusoe
- Voltage Park
- Hyperstack
- DataCrunch
- Massed Compute
- Applied Digital
The providers will not subsidize your inference anymore. Are you ready?
The price war between OpenAI and Anthropic will not save you. Your workloads will just consume more. SwarmOptimizer continuously simulates, optimizes, and deploys. Automatically.
Talk to Us