LLM deployment on your infrastructure
Running an LLM on your infrastructure keeps the model endpoint and the application data you choose inside an environment your organization controls. That can be an on-premise server, a private cloud account, a dedicated virtual network or a mix of them.
We choose the model, test it on your hardware and set up inference serving. The work also covers connecting your applications, access control, monitoring, updates and operating documentation. Private RAG systems and AI agents can then use the same deployment without sending their data to a public model API.
What does deploying an LLM on your infrastructure mean?
The model weights and inference software run in an environment your organization controls, and applications send requests to a private model endpoint instead of a public AI service. Network, identity, storage and logging policies stay under your control.
This does not make the whole application private. Prompts can still pass through a gateway, a search system, logs, analytics tools or an external integration. Every component that receives sensitive input or model output has to sit inside the deployment boundary.
What does the deployment cover?
The serving layer around the model controls how applications connect, how requests share the hardware and what happens when the model or a node fails.
Private model endpoint
A documented API that accepts requests, manages queues, streams responses and supports structured outputs, tool calls and request limits.
Inference engine and model
Software such as vLLM, SGLang, llama.cpp or TensorRT-LLM running the approved model weights, tokenizers and any task-specific adapters.
Quantization and batching
Lower-precision weights, continuous batching and prefix caching can cut memory use or raise throughput. How much they help depends on the traffic, and they can change output quality. We test each one on the target task.
Parallelism
A model can be split across GPUs, or copied onto more of them to serve more requests. Designs that span several nodes also depend on network bandwidth and latency.
Routing
A gateway can direct tasks to different local models, route around an unavailable replica or send approved requests to an external model.
Application connections
Chat interfaces, document workflows, private RAG, AI agents, internal APIs and existing products connect through authenticated interfaces. Retrieval, tool use and output validation stay in the application layer.
Where can the system run?
The right location depends on data rules, available hardware, internal operating capacity and how much demand changes over time.
| Option | Location | Usually suitable for | Main trade-off |
|---|---|---|---|
| On-premise | Company data center, server room or workstation | Strict network isolation, existing GPU capacity, fixed workloads or sites with limited connectivity | You own capacity planning, hardware availability, power, cooling and replacement |
| Private cloud | Your cloud account, VPC, private subnet or dedicated tenant | Private networking with faster provisioning, managed storage, monitoring and adjustable compute | The cloud remains an external dependency, and idle GPUs can be expensive |
| Hybrid | Sensitive components run privately while approved tasks use another environment | Workloads with different data classes, model requirements or temporary demand peaks | Routing rules and data classification must be explicit and testable |
When it's a good fit
A private deployment gives you more control, but someone has to operate it.
Running it yourself makes sense
Prompts, documents or outputs cannot leave a controlled network, the system must work offline, or logs and model updates must follow your own approval and retention rules. It also suits a stable workload on existing hardware, or a model or adapter that approved hosted services don't offer.
A hosted API may be simpler
The provider's data handling terms already meet the requirement, demand is small or irregular, or the task needs a model you can't deploy under an acceptable license. It also suits teams that don't want to run GPU servers, or that value fast access to new models over infrastructure control.
How the model is selected
The largest model that fits on the available hardware is rarely the right starting point. We start from examples of the task and measurable requirements, then compare candidates under the serving configuration planned for production.
Task quality
We measure accuracy on the languages, documents, output formats and failure cases of your application. Instruction following, tool use, structured output, context length and multimodal input are checked only where the task needs them.
License
Open-weight does not always mean unrestricted use. Commercial use, distribution, modification, usage restrictions, limits on derived models and required notices are reviewed before deployment.
Operational fit
Startup time, supported inference engines, update path, monitoring and whether your team can maintain the model.
Source and security
Before a model sees company data, we look at where it comes from, how its weights were published, its update history and any known security issues.
Hardware sizing and capacity planning
Parameter count alone does not determine the hardware. Memory use and response speed depend on model format, precision, input and output length, concurrency, inference engine and GPU architecture. We benchmark candidate configurations on a workload like yours and report on the points below.
Memory
GPU and system memory for the model weights, the runtime workspace and the KV cache of active requests, at idle and under load.
Speed and throughput
Time to first token and output tokens per second, measured separately, plus throughput and queue time at the expected concurrency.
Availability
Failure rate, timeout behavior and recovery time tell you whether one server is enough or you need redundancy.
Cost and growth
Estimated infrastructure cost at normal and peak demand, and the capacity that more users, documents, agents and applications will need.
Power and cooling
Rack space, power and cooling must support the chosen accelerators.
Security and data handling
A private model endpoint removes one external data path. Application vulnerabilities, prompt injection, excessive permissions, unsafe output handling and compromised model or container dependencies are still possible. Controls cover every place a request touches, including source documents, vector stores, logs, backups and administration interfaces.
Network boundary
Private subnets, firewall rules, ingress controls, service-to-service policy and optional offline operation define which systems can reach the endpoint.
Identity and authorization
Users and services authenticate before inference. Application permissions still apply when RAG or agents retrieve data and use tools.
Encryption and secrets
Connections are encrypted in transit. Tokens, certificates, registry credentials and storage keys stay in an approved secret-management system.
Logs and retention
Prompt and output logs are disabled, redacted or retained according to the data policy. Operational metrics can be collected without copying full content.
Model supply chain
Model files, containers and packages come from approved sources and are pinned, scanned and recorded before release.
Output controls
Applications validate structured output and do not execute model-generated code, queries or commands without separate checks and permissions.
What you receive
A deployment that can be rebuilt the same way for the agreed environment and workload, with the documentation your team needs to run, update and recover it.
- Model comparison, hardware benchmark results and the documented model and license choice
- Container images or installation packages with pinned versions
- Inference endpoint, authentication, request limits and health checks
- Network, storage, secrets and logging configuration
- Application, RAG or agent integrations included in the scope
- Load tests, quality evaluations and acceptance results
- Monitoring dashboards, alerts, backup, rollback and recovery procedures
Frequently asked questions
Can a self-hosted LLM run without internet access?
It can, as long as everything the model needs is installed inside your environment. Updates are then brought in through a controlled process, and outside services can't be used while it is offline.
Can we run an LLM on the servers we already have?
Often, yes. We check what your current servers can handle, then tell you what can run on them now and whether you would need more hardware or a different model.
How do we know whether a self-hosted model is good enough for our work?
We try the candidate models on examples from your own work and compare the results. General benchmark scores don't tell you how a model will do on your task.
How much does a private LLM deployment cost?
It depends mainly on whether you already have suitable hardware, which model you choose and how much it will be used. A good estimate shows setup, infrastructure and running costs separately.
Do we need an in-house team to run a self-hosted LLM?
Not necessarily, but once the system is live someone has to monitor it, apply updates and check that the model still performs well. That can be your IT team, us, or both.
Can we switch to a newer model later?
Yes. As long as your applications aren't tied to one particular model, it can be swapped later. The new model is tested on your tasks before it replaces the old one.
Will a private LLM learn from our prompts or documents?
No. A model running on your own servers answers requests but doesn't learn from them, unless a separate training process is set up for that.
Can several applications share one private LLM deployment?
They can. Each application gets its own access and usage limits, and capacity is planned for their combined use.
Can we start with a small LLM deployment and scale up later?
Yes. You can start with one model serving one application, then add users, applications or hardware once you have seen how it is used.
How long does an on-premise LLM deployment take?
Installing one model on hardware that is already in place is quick. A full service with new hardware, security reviews and connections to your applications takes longer. We can estimate once we know what you need.
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