Why Enterprises Deploy Private LLMs
Most enterprise discussions about LLMs eventually run into the same wall: the data that would make AI most useful is the data the organisation is least willing to send to a third-party API. Customer records, financial data, legal documents, proprietary research, employee information — the content that lives in enterprise systems is exactly the content that generates real value from AI, and exactly the content that compliance, legal, and security teams cannot allow to leave the corporate perimeter.
A private LLM deployment keeps inference entirely within your infrastructure. Prompts and completions never leave your network. You control the model, the serving infrastructure, the access controls, and the audit logs. You are not subject to a third-party provider’s terms of service changes, price increases, or API deprecations. For organisations where data sovereignty is a hard requirement, private deployment is not optional — it is the only viable path to using LLMs on sensitive workloads.
Three Deployment Models
Private LLM deployment sits on a spectrum from fully on-premises to fully cloud-managed, with hybrid options in between. The right choice depends on your data requirements, technical capacity, and cost tolerance.
On-premises deployment runs the LLM on hardware you own and operate in your own data centres. Maximum data control — data never leaves your physical infrastructure. Highest upfront cost (GPU servers are expensive) and highest operational burden (you manage everything). The right choice for organisations with strict data locality requirements, classified workloads, or existing data centre infrastructure they want to leverage.
Private cloud deployment runs the LLM on dedicated cloud infrastructure — a cloud provider’s GPU instances in a dedicated VPC, with no shared tenancy and private networking. Your data stays within your cloud account and never traverses the public internet. Lower upfront cost than on-premises, less operational burden, but still requires your team to manage the serving infrastructure. Azure OpenAI with private endpoints, AWS Bedrock with VPC endpoints, and dedicated GPU instances on any cloud provider all fit this pattern.
Managed private deployment uses a vendor (Anthropic, OpenAI, or others) who deploys dedicated capacity for your organisation in an isolated environment. You get the managed service experience with stronger isolation guarantees than the standard API. The least operational burden, but the most expensive option and still involves trusting a third party with your data to some degree.
Choosing the Right Open-Source Model
Private deployment means choosing and operating your own model, which requires understanding what the open-source landscape offers. In 2026, the best openly available models are competitive with frontier proprietary models on many tasks, though not all.
Llama 3.1 / 3.3 (Meta): The most widely deployed family. Available at 8B, 70B, and 405B. The 70B variant is the most popular enterprise choice — strong on reasoning, code, and instruction following, with a commercially permissive licence. The 8B model is appropriate for lower-stakes tasks where cost and latency matter more than quality.
Mistral and Mixtral (Mistral AI): Strong performance per parameter, particularly Mixtral 8x7B (mixture-of-experts architecture that gives 70B-quality outputs with 47B active parameters). Very permissive Apache 2.0 licence.
Qwen 2.5 (Alibaba): Particularly strong on code and multilingual tasks. The 72B variant rivals Llama 70B on many benchmarks. Apache 2.0 licence.
Gemma 3 (Google): Strong performance at the 27B scale, multimodal capability, permissive licence.
For most enterprise private deployments, Llama 3.3 70B or Qwen 2.5 72B at Q4/Q8 quantisation on one or two A100 80GB GPUs covers the majority of use cases. Fine-tune on proprietary data for domain-specific tasks.
Reference Architecture: Private LLM on Kubernetes
A production-grade private LLM deployment typically has several components working together: a serving layer (vLLM or TGI), an API gateway for authentication and rate limiting, an observability stack, and optionally a RAG pipeline for document retrieval. Here is a reference architecture for a Kubernetes-based deployment:
┌─────────────────────────────────────────────────────────┐
│ Private VPC/VNet │
│ │
│ ┌──────────────┐ ┌─────────────────┐ │
│ │ API Gateway │ │ Auth Service │ │
│ │ (Kong/Nginx) │◄──►│ (Entra ID / │ │
│ │ Rate limits │ │ OIDC) │ │
│ └──────┬───────┘ └─────────────────┘ │
│ │ │
│ ┌──────▼───────────────────────────────────────┐ │
│ │ vLLM Deployment (K8s) │ │
│ │ Pod 1: GPU Node (A100 80GB) │ │
│ │ Pod 2: GPU Node (A100 80GB) ← Load balance│ │
│ │ HPA: scale on queue depth │ │
│ └──────┬───────────────────────────────────────┘ │
│ │ │
│ ┌──────▼───────────┐ ┌──────────────────────┐ │
│ │ Vector Store │ │ Observability Stack │ │
│ │ (pgvector/Qdrant)│ │ Prometheus + Grafana │ │
│ │ Document chunks │ │ + Loki (logs) │ │
│ └──────────────────┘ └──────────────────────┘ │
└─────────────────────────────────────────────────────────┘Serving Layer: vLLM Configuration
vLLM is the recommended serving layer for enterprise deployments due to its throughput efficiency and OpenAI-compatible API. A production Kubernetes deployment for Llama 3.3 70B on dual A100 80GB:
apiVersion: apps/v1
kind: Deployment
metadata:
name: llm-server
namespace: ai-platform
spec:
replicas: 1
template:
spec:
nodeSelector:
nvidia.com/gpu.product: "A100-SXM4-80GB"
containers:
- name: vllm
image: vllm/vllm-openai:v0.6.0
args:
- --model
- /models/llama-3.3-70b-instruct
- --tensor-parallel-size
- "2"
- --gpu-memory-utilization
- "0.90"
- --max-model-len
- "8192"
- --enable-prefix-caching
- --served-model-name
- llama-3.3-70b
resources:
limits:
nvidia.com/gpu: "2"
memory: "200Gi"
volumeMounts:
- name: model-storage
mountPath: /models
readOnly: true
volumes:
- name: model-storage
persistentVolumeClaim:
claimName: model-weights-pvcStore model weights on a persistent volume pre-populated with the model files. This eliminates the cold-start download delay and ensures consistent startup times. Use a ReadOnlyMany PVC if you need the same weights accessible across multiple pods.
API Gateway and Authentication
Never expose the vLLM server directly to your internal network without an authentication layer. vLLM has no built-in auth. A lightweight approach using FastAPI as a proxy:
from fastapi import FastAPI, HTTPException, Depends, Header
from fastapi.security import HTTPBearer
import httpx
import os
app = FastAPI()
security = HTTPBearer()
VLLM_URL = os.environ["VLLM_SERVICE_URL"]
VALID_TOKENS = set(os.environ["API_TOKENS"].split(","))
async def verify_token(authorization: str = Header(...)):
if not authorization.startswith("Bearer "):
raise HTTPException(status_code=401, detail="Invalid auth")
token = authorization[7:]
if token not in VALID_TOKENS:
raise HTTPException(status_code=403, detail="Forbidden")
return token
@app.post("/v1/chat/completions")
async def chat(request: dict, token: str = Depends(verify_token)):
async with httpx.AsyncClient(timeout=120) as client:
response = await client.post(
f"{VLLM_URL}/v1/chat/completions",
json=request
)
return response.json()For production, replace the static token check with your organisation’s identity provider — Microsoft Entra ID, Okta, or a service mesh mutual TLS setup. Log every request with the authenticated identity so you have a complete audit trail of which user or service called the LLM with what prompt.
RAG Pipeline for Enterprise Documents
Most enterprise use cases combine the private LLM with a retrieval pipeline over internal documents. The standard stack: a document ingestion pipeline that chunks and embeds documents, a vector store for semantic retrieval, and a query pipeline that retrieves relevant chunks and injects them into the LLM context.
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import PGVector
from langchain_openai import ChatOpenAI
from langchain.chains import RetrievalQA
# Embeddings run locally too — no external API calls
embeddings = HuggingFaceEmbeddings(model_name="BAAI/bge-m3")
# pgvector in your private Postgres instance
vectorstore = PGVector(
connection_string="postgresql://user:pass@internal-postgres:5432/vectordb",
embedding_function=embeddings,
collection_name="enterprise_docs"
)
# Point the LLM client at your private vLLM deployment
llm = ChatOpenAI(
base_url="http://llm-service.ai-platform.svc.cluster.local/v1",
api_key="your-internal-token",
model="llama-3.3-70b"
)
qa_chain = RetrievalQA.from_chain_type(
llm=llm,
retriever=vectorstore.as_retriever(search_kwargs={"k": 5})
)
result = qa_chain.invoke("What is our refund policy for enterprise contracts?")Both the embedding model and the LLM run privately. No data leaves the cluster at any point in the query pipeline.
Hardware Sizing for Enterprise Deployments
Hardware selection is the most consequential decision in a private LLM deployment. The calculus depends on your model choice, expected throughput, and latency requirements.
For a team of 50–200 users with moderate usage (a few hundred requests per day), a single server with two A100 80GB GPUs running Llama 70B at Q4 handles the load comfortably. Total server cost is roughly $30,000–$50,000 for a new build, or $8,000–$15,000/year if running on cloud dedicated instances. At moderate usage, this frequently compares favourably to equivalent API costs within 12–18 months.
For higher-traffic applications — internal chatbots serving thousands of employees, document processing pipelines — horizontal scaling with multiple inference pods behind a load balancer is more cost-effective than scaling individual server size. Two A100 servers at $40K each handle twice the throughput of one at $60K, and add redundancy.
For organisations not ready to commit to GPU hardware, cloud GPU instances are a reasonable starting point. AWS p4d.24xlarge (8× A100 40GB), Azure NC A100 v4, and GCP A2 instances all provide dedicated A100 access. Run for 3–6 months to understand your actual usage pattern before deciding whether to purchase hardware or continue on cloud instances.
Compliance and Governance
Private deployment addresses data residency concerns but does not automatically satisfy compliance requirements. A few additional controls are typically needed for regulated workloads. Audit logging — every prompt and completion logged to an immutable store with timestamp, user identity, and model version. Necessary for GDPR data subject access requests, SOC 2 evidence, and internal policy enforcement. Access controls — role-based access to different model capabilities, with finance users accessing one deployment and engineering users another, each with different system prompts and guardrails. Data retention — define and implement a retention policy for prompt logs. Some regulations require retention; others require deletion. Know which applies to your use case before logging everything indefinitely. Model governance — version control for model weights and system prompts, with change management processes for updates. A production LLM deployment should be treated like production software: tested in staging before changes reach production, with rollback capability.
Cost Comparison: Private vs. API
The break-even analysis between private deployment and commercial API depends heavily on usage volume. At low volume, the API is cheaper — you pay only for what you use. As volume grows, the fixed cost of private infrastructure is amortised over more requests, and the per-request cost falls below the API price.
A rough break-even estimate: a private deployment capable of serving 70B quality responses costs roughly $3,000–$5,000 per month in cloud GPU costs (or ~$1,500/month amortised if on-premises hardware). At GPT-4o pricing of approximately $10 per million output tokens, break-even occurs at roughly 300–500 million output tokens per month. For a team generating 1M tokens of output per day (about 500 detailed responses), break-even is around 10 months on cloud infrastructure, or less than 6 months with on-premises hardware. Beyond that volume, private deployment is significantly cheaper — often 70–90% less per token at scale.
Getting Started: A Practical Path
For organisations beginning their private LLM journey, a staged approach reduces risk. Start with a pilot deployment — a single GPU server or cloud instance running Ollama or vLLM with a 7B or 13B model, accessible to a small internal team. Validate that your use cases work, understand the operational requirements, and gather usage data. Then make the hardware and architecture decisions informed by real data rather than projections. Expand to a production-grade deployment with proper auth, monitoring, and high availability only after you have validated the value. This staged approach avoids committing to expensive infrastructure before you understand your actual requirements, and it builds the internal expertise to run the production system well.
Security Hardening for Private LLM Infrastructure
A private LLM deployment that handles sensitive enterprise data requires the same security rigour as any other piece of critical internal infrastructure. Several controls are worth implementing from the start rather than retrofitting later.
Network isolation. Run the inference cluster in a dedicated subnet with no inbound internet access. All access should come through internal load balancers from within the VPC/VNet. Outbound internet access should be restricted to specific endpoints needed for model downloads during setup, and removed entirely once the deployment is stable.
Secrets management. API tokens, database credentials, and model access tokens should be stored in a secrets manager (HashiCorp Vault, AWS Secrets Manager, Azure Key Vault) and injected into containers at runtime. Never bake secrets into container images or Kubernetes manifests.
Image scanning and supply chain security. vLLM and its dependencies are open source with an active community, but container images should be scanned for vulnerabilities and pinned to specific digests rather than floating tags. A compromised inference container is a privileged position from which an attacker could access every prompt processed by the system.
Prompt and output logging with PII controls. Log prompts for audit purposes, but scrub PII before writing to log storage. The same Presidio-based detection pipeline that guards your input guardrails can run on logs before they are written, ensuring that sensitive information in user prompts does not end up in log files with broader access than the application itself.
The investment in these controls is modest compared to the cost of a data breach involving the sensitive internal data your private LLM is processing. Treat the LLM infrastructure with the same security standard as the data systems it is accessing.
Private LLM deployment is not a simple lift-and-shift from API to self-hosted. It adds real operational complexity. But for organisations where data residency matters, where usage volume makes the economics compelling, or where fine-tuning on proprietary data is central to the use case, it is the only architecture that delivers both full capability and full data control — and in 2026, with mature open-source models and proven serving infrastructure, it has never been more accessible to execute well.