Inference engineering services
Private AI deployment
We deploy suitable LLM and speech models on infrastructure your team manages, including endpoints for private coding assistants. Compare quality, capacity and total operating cost with your current provider before deciding which workloads to move.
Discuss your workloadWhen this helps
For teams considering private or customer-managed inference due to data handling, cost or control. Useful when GPU availability is uncertain, capacity is unverified, or you need to clarify monitoring, ownership and rollback before changing production.
Check model fit against the workload
Start with the tasks, request patterns and quality requirements. Compare suitable models using representative inputs, assessing output quality, errors and response behaviour.
A model that starts is not automatically production-ready. Consider request lengths, concurrency, dependencies and the work needed to keep the service available under expected conditions.
Assess capacity and total operating cost
Review available GPU resources, memory needs and capacity for representative traffic. If hardware is shared or availability is uncertain, include those limits in the assessment rather than assuming resources are always free.
Compare operating costs with your current approach using relevant workload and operating assumptions. Include infrastructure and ongoing work; do not treat hardware expense alone as the full cost.
Define privacy boundaries and operating needs
Map where requests and outputs travel, which systems handle them, and what the deployment logs for operation and troubleshooting. Agree which data may be processed or retained and who can access each part.
Clarify ownership for configuration, model updates, monitoring and incident response. Record which components stay in your environment, which external dependencies remain, and which tasks your team takes over after handover.
Plan rollout, monitoring and rollback
Choose a rollout approach that lets the team observe quality, latency, errors and resource use as the workload moves. Define signals that would pause or reverse the change and how the existing route remains available during that decision.
Implement the agreed configuration and hand over monitoring guidance and a rollback plan where included in scope. Scope, price and timeline are agreed for the workload before implementation starts.
What you receive
- Workload assessment covering model fit, quality requirements and representative request patterns.
- Capacity and operating-cost comparison based on stated hardware and deployment assumptions.
- Review of privacy boundaries, logging needs and operational ownership, agreed with the team.
- Deployment configuration with monitoring guidance and a rollback plan, where agreed.
Common questions
Does private deployment always reduce total cost?
No. It depends on workload, available hardware, capacity needs and ongoing operating effort. Review these assumptions alongside model quality before deciding what to move.
Can every model run on our available GPUs?
Fit depends on the model, memory requirements, request patterns and available hardware. The assessment checks these constraints rather than assuming a deployment will fit.
Which data needs to stay inside our infrastructure?
That depends on your requirements. We map prompts, outputs, logs and external dependencies with your team, then document where each is processed and retained. This gives security and compliance reviewers a concrete design to assess.
Start with one workload
Tell us what runs today, where cost or latency causes problems, and which quality requirements must hold. We agree scope, price and timeline before implementation.
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