Speed
Latency and uptime audit
Review p95, p99, retry behavior, health checks, failover, and where shared capacity stops being enough.
Enterprise inference advisory
Production AI APIs without guesswork.
Get a practical route plan for model choice, GPU fit, fallback, monitoring, and cost controls before traffic depends on the API.
p95/p99Latency budget
A100 80GBCurrent shared route
FallbackPrimary plus backup plan
MonthlyCost and model review
Advisory system
Advisory built around production constraints.
Benchmarks only matter when they map to latency, reliability, model fit, privacy boundaries, and spend.
Quality
Model fit benchmark
Test real customer prompts before moving production to a model because of headline scores.
Budget
AI spend efficiency audit
Find repeated prefill, oversized context, cache misses, expensive retries, and routes that are overbuilt.
Continuity
Fallback and migration plan
Prepare a second route so model changes, provider issues, or capacity events do not become product incidents.
Private capacity when generic APIs are the bottleneck.
A private route can improve privacy boundaries, runtime control, and predictable throughput for lawful internal workloads. It is not a shortcut around abuse rules; it is an operating model for serious teams.
Privacy boundaryFewer third-party prompt and response hops.
Policy controlFewer blanket refusals for lawful internal workflows.
Runtime controlModel, context, quantization, cache, and fallback tuning.
Thunder Compute fitPer-minute GPU, snapshots, CUDA/PyTorch images, port forwarding, and CLI workflows.
Request review
Send the workload. I'll send the path.
Rough answers are enough. Do not paste secrets, private data, or customer records.
Model and GPU direction
Latency and uptime risks to check first
Cost-control opportunities
Fallback route recommendation