Private LLM Adoption Playbook

Short answer: recommended approach and timeline

Build a vendor-agnostic private LLM PoC in 6-8 weeks. Start small. Prove security, accuracy, and cost.

Use a VPC or on-prem host, a vector database for RAG, and clear audit logs. If you need a quick primer, see what a private large language model is.

What is a private LLM?

A private LLM is a language model you host under your control. That can be on-premises, in a dedicated cloud account, or inside a VPC. The goal is simple: keep inputs and outputs inside your boundary so sensitive data never leaks to public APIs. For background reading, see private vs public LLMs and running LLMs privately.

Why teams choose private LLMs

• Data privacy and compliance (HIPAA, GDPR, SOC2).
• Better domain accuracy by grounding on internal docs.
• Control over model updates and training data.
• Predictable performance and reduced vendor training use of your data.

When to build vs buy

Use this quick checklist. Score each item 0 or 1 and add up the total. If your score is 3+ out of 5, lean build.

• We handle regulated data or IP you can t share externally.
• We need consistent, low-latency inference inside our cloud or network.
• We must prove audit trails and data lineage for compliance.
• We have engineering resources for infra and model ops.
• We expect long-term cost advantages or want to avoid vendor lock-in.

6-8 week PoC runbook

Goal: safe, measurable RAG assistant that answers internal questions. Success criteria: private deployment, sample dataset indexed, F1-like accuracy target met for 10 queries, audit logs enabled, basic RBAC working.

1. Week 0: Prep - Pick scope and dataset (one team, one doc type). Secure budget and a cross-functional team: engineering, security, product, legal.
2. Week 1: Environment - Provision VPC or on-prem VM. Harden network rules. Create service accounts and MFA for operators. See VPC recommendations.
3. Week 2: Base model - Choose a base model (open or licensed). Deploy model container or managed instance. Run a smoke test inference.
4. Week 3: Ingest & vector DB - Extract corpus, split text, embed, and store vectors. Use a vector DB like Weaviate or your vendor of choice. For a local demo, read privateGPT and vector approaches.
5. Week 4: RAG pipeline - Wire retrieval to the model. Implement prompt templates and a safety layer that strips PII. Run 50 test queries and log results.
6. Week 5: Security & governance - Turn on encryption at rest and in transit. Enable audit logging, integrate with SIEM, and run access reviews.
7. Week 6: UX & integrations - Add a simple chat UI or API. Integrate auth (OIDC), and limit data export paths.
8. Week 7: Validation - Run acceptance tests, measure latency and accuracy. Tune retrieval and prompt templates.
9. Week 8: Review & next steps - Present results, cost estimates, and a recommended production plan.

Quick run commands

docker run --rm --name llm-demo -p 8080:8080 my-llm-image:latest

How to secure a private LLM

Security controls must map to your compliance needs. Keep controls simple and testable.

Control	Why it matters	How to check
Network isolation	Stops data exfiltration	VPC rules, no public egress
Encryption	Protects data at rest and in transit	TLS for transport, KMS for storage
Auth & RBAC	Limits who can query or update	OIDC, scoped roles
Audit logging	Proof for audits	Immutable logs to SIEM
Data minimization	Reduces leak risk	Mask PII, redact before indexing

For examples mapping to regulations, read the enterprise security guide.

RAG with a private LLM: simple example

RAG means you retrieve documents, embed them, and send top hits with a prompt to the model. Keep the prompt short and include sources.

• Vector schema: id, source, chunk_text, embedding, timestamp.
• Retrieval: top-k by cosine similarity, then filter by recency or clearance level.
• Prompt template: include a one-line instruction, a short context block from retrieved docs, and the user question. Example: Use only the facts below to answer. Cite sources.

See a practical note about slow local inference and hybrid setups at private LLM tradeoffs and grounding strategies at grounding on internal data.

Cost model and performance tips

Think CAPEX vs OPEX. Small checklist:

• Estimate GPU hours for training or fine-tuning.
• Estimate vCPU/RAM for inference instances.
• Plan storage for vectors and raw docs.
• Add monitoring and SRE time.

Performance tips:

• Use quantized models for cheaper inference.
• Cache common responses.
• Separate cold batch jobs from hot inference paths.
• Limit prompt length to control cost.

Bloomberg and others show tight domain models can beat general models on accuracy and cost for frequent queries. For enterprise patterns, see industry examples.

Acceptance tests and metrics

• Accuracy: answer correctness on 20 golden queries.
• Latency: P95 under target (e.g., 500ms for internal tools).
• Cost: monthly infra under approved budget.
• Security: no external calls for indexed data, audit logs present.

Deliverables at PoC end

• Running private LLM in your VPC or on-prem host.
• Vector DB with sample corpus and retrieval code.
• Prompt templates and safety filters.
• Security control matrix and logs integrated to SIEM.
• Cost estimate and production recommendation.

Next steps and resources

If you want a short checklist and a cost worksheet, start with these anchors: Private LLM basics, RAG with private LLM, LLM security checklist. Read vendor-neutral pieces at Zilliz and enterprise security notes at Matillion.

Final note from the ops desk

Keep the first PoC small. Prove safety and value. If the PoC passes, scale in stages. Roll forward with clear gates.

I m Morgan, and this is a practical path you can run in weeks, not months.