Semantic caching
Return known answers before a GPU is touched.
Cache hitsGPU occupancyTokens per dollarLatency budgets
Cost model
Shift inference spend from brute force to policy.
Modeled reduction61%
Projected monthly savings$109,017
Optimized run-rate$70,983
Tokens per dollar12,679
Optimization stack
Six levers, one serving control plane.
Prompt caching
Reuse long system and context prefixes across requests.
Routing
Send each query to the least expensive model that can answer.
Batching
Fill GPU memory with compatible requests instead of idle gaps.
Quantization
Serve smaller weights without losing task-level quality.
GPU scheduling
Place work by latency budget, queue depth, and hardware fit.
Model coverage
Built for the open model families teams actually deploy.
Reasoning-heavy enterprise and agent workloads.
Multilingual, coding, and high-throughput assistant traffic.
Private deployment paths for coding.
Enterprise feature
Security
Keep model serving inside your enterprise boundary with private routing, policy-aware access, and deployment telemetry that stays under your control.
- Private VPC and on-prem deployment paths
- Role-aware model and data access policies
- Audit-ready request, cache, and routing telemetry
Production posture
Security and economics should reinforce each other.
Private deployment stays private: models, prompts, and telemetry remain in your controlled environment.
Optimization is workload-aware: latency-sensitive chat, batch enrichment, and agentic workflows get different serving policies.
Savings are measured at the serving layer: cache hit rate, tokens per dollar, queue time, and GPU occupancy move together.