HyperLake — Private model serving and fine-tuning: plan quality and capacity
FREE PLANNING CHECKLIST

Enclave runs open or custom models on GPUs in your cloud. Compare model quality, workload utilization and operational responsibility before replacing a hosted endpoint.

Prepare these inputs
[ ] Model and representative evaluation set
[ ] Traffic, context length and latency profile
[ ] Region, licenses and available GPU capacity

Your first workflow
[ ] Define the quality and latency acceptance bar.
[ ] Pilot one endpoint with representative traffic and retrieval needs.
[ ] Compare utilization and total cost, then agree the production operating model.

Acceptance checks
[ ] Evaluate answer quality on the agreed cases.
[ ] Measure startup, latency and capacity under load.
[ ] Include idle compute, storage and support in cost comparisons.

Decision criteria
[ ] Does the chosen model meet the actual quality and safety requirements?
[ ] What capacity and cold-start trade-offs suit the traffic?
[ ] Which data, models and licenses can run in the selected region?

Cost and scope
Compare the cloud bill, platform charges, operating effort and migration work for the same workload. Cloud capacity is not free. Charges and scope need agreement before paid deployment.

Before switching
AWS and Google Cloud are available today; other deployment environments are roadmap. Preserve the existing workload and access path until compatibility, recovery and cutover checks pass.

Sources
https://hyperlake.cloud/stacks
https://hyperlake.cloud/faq
https://hyperlake.cloud/get-started

Next step
No free cloud capacity is promised. Deployment scope and charges are agreed separately; AWS and Google Cloud are available today.
https://hyperlake.cloud/solutions/private-model-serving

This is a planning template, not a completed assessment or a quote. No personal data is required to download it.