Private model serving and fine-tuning: plan quality and capacity
Enclave runs open or custom models on GPUs in your cloud. Compare model quality, workload utilization and operational responsibility before replacing a hosted endpoint.
Relevant capabilities
- Model serving and fine-tuning on customer GPUs
- pgvector retrieval and governed data access
- Eligible endpoints can scale down when idle
How should you compare the options?
- 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?
What should you bring?
- Model and representative evaluation set
- Traffic, context length and latency profile
- Region, licenses and available GPU capacity
What does a practical first workflow look like?
- 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.
What should you check before expanding?
- Evaluate answer quality on the agreed cases.
- Measure startup, latency and capacity under load.
- Include idle compute, storage and support in cost comparisons.
How should you evaluate the cost?
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.
What needs checking 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.