Private and Self-Hosted LLM Deployment
Open-weight language models deployed inside your own cloud tenancy or data centre, sized properly, served efficiently, and documented well enough to satisfy the people who asked for it in the first place.
Private LLM deployment is usually requested for one of three reasons: data cannot leave a boundary, a regulator or customer contract requires it, or the unit economics of an API stop working at volume. All three are legitimate. Only the third is usually costed properly before the decision is made.
Private and self-hosted LLM deployment is the engineering of an open-weight language model running inside infrastructure you control: model selection and sizing, a serving stack tuned for your traffic shape, autoscaling and capacity planning, monitoring, and the security and governance evidence that shows where data goes and who can reach it.
When self-hosting a model is the right call
- Data residency and sovereignty. Content cannot cross a jurisdictional or organisational boundary, and a contractual assurance is not sufficient for your risk function.
- Regulated workloads. Healthcare, defence, financial services or public sector obligations that make third-party inference difficult to evidence.
- Volume economics. Sustained, predictable throughput where dedicated capacity is cheaper per token than metered API calls.
- Latency and locality. Inference has to sit close to the data or the user, including on-premise and edge cases.
- Model stability. You need the exact same weights available in three years, with no deprecation notice.
And when it is not: bursty or unpredictable traffic, small volumes, teams without platform engineering capacity, or a use case that genuinely needs frontier-model capability that open weights do not yet match. We will say so, and the answer is often a hybrid where sensitive requests stay in and the rest go out.
What the deployment involves
Model selection against your task
Open-weight models differ far more by task than the general rankings suggest. Candidates are benchmarked on your evaluation set at the quantisation you would actually run, because a model that wins at full precision and loses at four-bit is not the model you are deploying. Where no evaluation set exists we build one, as covered in LLM evaluation and benchmarking.
Honest capacity sizing
Sizing is driven by concurrency, context length and tokens per second at your peak, not by the parameter count. We model memory per request including the key-value cache, measure throughput under realistic load, and give you a capacity plan with headroom and a cost per million tokens that can be compared directly against the API you are leaving.
A serving stack, not a script
Continuous batching, paged attention, quantisation, prefix caching and streaming, deployed with autoscaling, health checks, graceful draining and a queue that degrades predictably under load rather than timing out. GPU utilisation is the number that decides whether the economics work, and most first attempts leave more than half of it on the floor.
Security and governance evidence
Network isolation, key management, tenant separation, audit logging of who asked what, and retention rules for prompts and outputs. This is the material your security review, your auditors and your enterprise customers will ask for, and producing it during the build is far cheaper than reconstructing it afterwards.
Run the hybrid comparison before committing
We routinely model three options side by side: full API, full self-host, and a hybrid that keeps sensitive traffic inside and routes the rest out. The hybrid wins more often than either extreme, and seeing the three costed together tends to change the conversation with finance.
The economics, stated plainly
A self-hosted deployment trades a variable cost for a fixed one. That is a good trade at steady high volume and a poor one otherwise, and the break-even is easy to compute once four numbers are known:
- Sustained tokens per second at peak, and the shape of the daily curve.
- Achievable throughput per accelerator at your context length and quantisation, measured rather than quoted.
- The fully loaded cost of that hardware, including reserved capacity, storage, networking and idle time.
- The engineering time to run it, which is the cost most business cases omit entirely.
We produce that comparison as a working model with editable assumptions, so it can be rerun when prices move. Where a broader financial case is needed, AI ROI analysis and business case covers the wider picture.
How the engagement runs
The go or no-go on economics happens in the first fortnight, before any infrastructure is committed.
Requirements and economics
Residency and compliance constraints documented, traffic shape measured, and the API, self-host and hybrid options costed side by side.
Model selection and benchmarking
Candidate open-weight models scored on your task at realistic quantisation, with throughput and latency measured on target hardware.
Serving stack build
Inference server, batching, caching, autoscaling, health checks and observability deployed as infrastructure as code.
Security, load and failover
Isolation, key management, audit logging, load testing at peak plus headroom, and failover behaviour verified under induced failure.
Handover
Runbooks, capacity model and cost dashboard handed to the platform team, with an on-call walkthrough.
What you receive
A running deployment, the evidence it is safe, and the model that says what it costs.
Deployed inference service
Serving stack as infrastructure as code, with autoscaling, health checks and streaming.
Model selection report
Candidates scored on your task at production quantisation, with throughput and latency measured.
Capacity and cost model
Tokens per second per accelerator, headroom, break-even against API pricing, with editable assumptions.
Security and residency evidence
Network design, key management, audit logging and data retention, written for a security review.
Observability and alerting
GPU utilisation, queue depth, latency percentiles, error budgets and the alerts that matter.
Operations runbook
Scaling, upgrading, rolling back and failing over, with the first ninety days of recommended checks.
Is this the right engagement?
Worth being direct. Private and Self-Hosted LLM Deployment is the wrong spend in some situations, and those are listed rather than buried.
Good fit if
- Data cannot leave your tenancy, jurisdiction or data centre for legal or contractual reasons.
- Sustained volume is high and predictable enough that dedicated capacity beats metered pricing.
- A customer or regulator requires evidence of where inference happens and who can see prompts.
- You have, or are building, a platform team that can run GPU infrastructure.
- Model stability over years matters more than access to the newest frontier capability.
Choose something else if
- Traffic is low or spiky. You will pay for idle accelerators and enjoy none of the savings.
- The task genuinely needs frontier capability that current open weights do not reach.
- No one will own the infrastructure after handover.
- The driver is cost alone and nobody has measured the traffic. Model it first, then decide.
Frequently asked questions
Marked up with FAQPage schema so these answers can surface directly in search results and inside AI assistant responses.
Is private LLM deployment cheaper than using an API?
At sustained high volume it usually is; at low or spiky volume it usually is not, because you pay for capacity whether or not it is used. The break-even depends on your traffic shape, context length and achievable throughput per accelerator, which is why we measure those in the first fortnight rather than assert a general answer.
Which open-weight models do you deploy?
Whichever performs best on your evaluation set at the quantisation you will actually run, within the licence terms your legal team accepts. We benchmark candidates rather than default to a favourite, and we check licence conditions for commercial use before recommending anything.
Can it run in our own data centre rather than a cloud?
Yes. The serving stack is the same; the differences are in procurement, capacity planning and failover, and on-premise sizing needs to be right first time because you cannot add an accelerator with a configuration change.
How do we handle model upgrades once self-hosted?
The same way as any other deployment: the new weights are scored against your evaluation set, deployed alongside the current version, traffic is shifted gradually and rolled back if quality or latency regresses. Because you control the schedule, you upgrade when it suits you rather than when a provider deprecates something.
Do we still need an API provider at all?
Often yes, and that is a feature rather than a failure. Many deployments keep sensitive traffic inside and route everything else to a commercial API for capability or burst capacity, with a routing layer deciding per request. We build that routing where the hybrid comparison shows it wins.
Often paired with this
Most clients combine two or three engagements from the Generative AI & LLM Engineering pillar. These are the ones that most often run immediately before or after.
Model Distillation and Inference Optimisation
Cost and latency reduced through routing, caching, quantisation and distillation, with quality held to an agreed floor.
Read more →Small Language Models and On-Device AI
Task-specific small models that run on a phone, laptop or edge device, with low latency and no data leaving it.
Read more →LLM Evaluation and Benchmarking
Golden sets, calibrated judges and a regression suite in CI, so quality is a number that moves rather than an opinion.
Read more →Is this the right engagement?
Tell us what you are trying to build. If a different service fits better, or if you do not need us at all, we will say so.