Managed inference
The implemented deployment workflow uses vLLM and LiteLLM, bounded API keys and private management access. Hardware and model compatibility must pass live checks.
SOLUTIONS / ENGINEERING REVIEW
Your workload. Your deployment boundary.
Request dedicated inference or an assessment of existing cloud, on-premise and hybrid infrastructure. We confirm feasibility, access, scope and costs before provisioning.
Bring your existing GPU
Submit your current provider, hardware and access constraints for an operational takeover assessment. Existing infrastructure is not modified merely by submitting the request.
On-premise and hybrid
Request private connectivity, data-location and responsibility planning. Site readiness, VPN access and a migration/rollback plan need review before a deployment is approved.
Training and fine-tuning assessment
We can collect dataset, compute and storage requirements for engineering review. Training execution and fine-tuning jobs are not automated in the current deployment engine.
Image and batch workloads
Submit image generation, custom runtime and batch requirements for a scoped proposal. These requests do not create a serverless container or promise a supported runtime.
Operations and support coverage
Define monitoring, patch windows, backups and incident escalation in the service scope. Around-the-clock human response is supplied only when explicitly agreed and staffed.