Private LLM Consulting
Choose a private or VPC-hosted LLM only when data, residency, or vendor terms require it — then serve it in your cloud with evals, not a science project.
- Service
- LLM Platform
- Industry
- Enterprise
- Updated
- 2026-08-25
- Engagement
- 4 wks
Private LLM consulting decides whether a VPC-hosted, dedicated, or on-prem model is justified versus a public API, then implements serving, identity, reconstructable traces, and eval gates in your cloud — typically in four weeks for one workload, with you owning the artifacts and no shared training on your data.
Why teams pick this engagement
LLM Platform × EnterprisePerimeter as a control
Private serving is for data you cannot send to a public API — not a status symbol. We write the residency, retention, and training-term reasons before you buy GPUs.
Quality vs cost on your cases
The API baseline and the private candidate run the same golden set. If quality does not hold, we do not recommend the private path.
Your VPC, your weights path
Serving, adapters, and traces stay in the client cloud. Client owns IP. Model-agnostic: closed APIs, dedicated instances, or open weights.
Security and platform together
Network, secrets, identity, and on-call are designed with your platform team. Financial-services reviews typically take 8–12 weeks.
Four-week standard
Discovery, bake-off, serving skeleton, eval gate, handover. We do not start a six-month GPU program to avoid a contract clause we have not read.
Reconstructable serving traces
Every in-scope call stores model id, version, input class, and output so you can prove what ran inside the perimeter.
Key takeaways
- 01
A private LLM is justified by data class, residency, or vendor training terms — not by preferring open weights as a brand.
- 02
Run the same golden set on the public API and the private candidate; if quality or latency fails, do not migrate for theater.
- 03
Private still needs identity-aware access, reconstructable traces, rubric judges, and CI gates. Isolation is not evaluation.
- 04
Work runs in the client cloud. Client owns IP, adapters, and evals. Zero-retention, SOC 2-aligned, no shared training.
- 05
Four weeks is the standard decision-plus-serving path; financial-services programs with heavier review typically take 8–12 weeks.
What the engagement covers
How we work
- 01
Discover
Data classes, vendor terms, latency, and current API spend. Write the reason a private path would exist at all.
- 02
Design
Target serving pattern, identity, eval plan, and capacity envelope reviewed with security and platform before build.
- 03
Build
Stand up serving in your VPC, wire traces, and run the bake-off against the golden set.
- 04
Validate
Quality, latency, failure modes, and a reconstructable incident walkthrough. Confirm no data path to shared training.
- 05
Enable
Handover of serving configs, evals, and on-call runbooks so your team can pin, scale, and roll back.
Take the playbook with you
The working documents from real engagements — free, in exchange for an email. They’re useful whether or not we ever talk.
Private vs Public LLM Decision Matrix
Score data class, residency, vendor training terms, latency, cost, and eval quality — the same rubric we use before recommending a VPC model.
Get the matrix ·VPC LLM Serving Checklist
Networking, secrets, GPU capacity, observability, and rollback — the controls a private LLM needs before production traffic.
Get the checklist ·