Knowledge / Software & AI

Software & AI · Deployment & cost · Written and maintained by Haink’s AI adoption team · Updated August 2026 · 8 min read

On-Premises vs Cloud LLM Deployment for Enterprises

The choice between cloud LLM APIs and on-premises deployment comes down to four factors: data sensitivity, cost at your volume, latency requirements, and control. Cloud APIs are the fastest way to start and give access to the strongest models; on-premises keeps data in your network and gives predictable cost at steady high volume. Many enterprises end up hybrid — sensitive or high-volume workloads private, everything else on managed APIs.

Cloud LLM APIs

Managed APIs from frontier model providers are the fastest way to start, give access to the strongest models, and require no infrastructure. The trade-offs: your data leaves your network (subject to the provider's policies and region), cost scales with every token, and you depend on the provider's availability, rate limits and roadmap. For low or variable volume and rapid prototyping, this is usually the right starting point.

On-premises / private deployment

Running open-weight models such as Llama or Qwen on your own GPUs keeps data inside your network, gives predictable cost at steady volume, and removes external rate limits. The trade-offs: you need the right GPU hardware, MLOps to operate it, and open-weight models — while excellent — may trail the very best proprietary models on the hardest tasks. For regulated data, air-gapped environments, strict latency, or high steady volume, on-premises wins.

Cloud vs on-premises at a glance

The break-even, computed

Every comparison of this kind says the break-even “depends on your volume” and stops there. It is computable, and the number surprises most teams, so here it is with the assumptions on the table.

Take a single inference node at $78,000, amortised over three years, with a 50% uplift for power, cooling, hosting and a share of someone’s time to run it. That is $39,000 a year, about $4.45 an hour — charged whether the node is busy or idle. The uplift is where honest estimates differ most; treat it as a placeholder for your own figure rather than a quote.

Your cost per token is that hourly figure divided by the tokens you actually push through it. Not peak capability — sustained average throughput, which is the number almost nobody measures before buying:

Now set that against per-token pricing. Most production workloads are input-heavy — retrieval stuffs a lot of context in and a short answer comes out — so blend at roughly ten input tokens per output token. As of August 2026 a frontier-tier API around $2.50 in / $15 out blends to about $3.64 per million; a cheap capable model around $0.14 / $0.28 blends to about $0.153.

Ten billion tokens a year is a large enterprise workload running continuously. Two hundred and fifty billion is not a volume most single organisations have. On inference cost alone, self-hosting loses more often than this market admits — and it loses worst against exactly the open-weight-class models you would otherwise have self-hosted, because someone else is serving them at scale on hardware that never idles.

Three options, not two

Framing this as cloud versus on-premises hides a third option, and the three do not fall into a single ranking:

Rows two and three are both self-hosting, and choosing between them is a different calculation — GPU-hour against GPU-hour rather than token against token. That one is worked through in cloud versus private AI infrastructure, which puts the crossover at roughly 8–12 hours of genuine GPU usage a day. Reference deployments, sizing anchors and observed payback periods are in private LLM for pharma.

So when does self-hosting actually make sense

Almost always for a reason that is not the token bill. Four that hold up:

• Residency and confidentiality. The data cannot leave the network, or cannot leave the country. That is a constraint rather than a preference, and it settles the question before any arithmetic starts.
• Change control. A hosted model is updated on the provider’s schedule. If you have to demonstrate that the system running today is the system you tested, a version that can move underneath you is disqualifying — the argument in full is in document extraction under validation.
• Rate limits and availability. When a queue in front of someone else’s capacity sits on your critical path, owning the capacity is worth more than it costs.
• Air-gap. No network path out at all, which no API arrangement satisfies by construction.

And one case that does hold on cost, narrowly: a continuous batch workload — document processing running unattended overnight, throughput-bound and latency-tolerant — is the one shape that genuinely keeps a node saturated. Interactive assistants rarely can, because usage follows the working day and the node idles through two thirds of the week.

Which is why most estates end up hybrid, and that is a conclusion rather than a hedge: workloads with a residency or change-control constraint run on owned hardware, everything else runs on an API, and the split is drawn by constraint rather than preference. A single deployment mode for the whole estate usually means one of the two groups is being paid for twice.

Open-weight vs proprietary models

Open-weight models like Llama and Qwen have closed much of the gap and are sufficient for the majority of production tasks, especially with good retrieval and prompting. The strongest proprietary models can still lead on the hardest reasoning problems. The pragmatic approach is to choose per use case — and on-premises deployment specifically requires open-weight models, which is part of why private and cost-controlled workloads run on them.

Related Resources

• LLM Applications & RAG
• Software & AI Development Services
• How to Build a Production RAG System
• Cloud vs Private AI Infrastructure — the GPU-hour comparison: renting versus owning
• Private LLM for Pharma — sizing anchors and observed payback periods
• Document Extraction Under Validation — why change control, not cost, forces self-hosting

Frequently Asked Questions

Should we run our LLM on-premises or in the cloud?

Use cloud APIs for low or bursty volume and fast access to the strongest models. Choose on-premises when data must stay private, volume is steady and high, latency is strict, or the environment is air-gapped. Hybrid setups — sensitive workloads private, the rest on cloud — are common.

Is on-premises LLM cheaper than cloud?

Less often than the market implies. A $78,000 inference node amortised over three years, with a 50% uplift for power, cooling and operations, costs about $4.45 an hour whether it is busy or not. Against a frontier-tier API blended at roughly $3.64 per million tokens, you need to sustain about 340 tokens per second around the clock — roughly 10.7 billion tokens a year — before owning is cheaper. Against a cheap capable model at about $0.153 per million, the break-even is near 8,100 tokens per second, which is not a volume most single organisations have. On inference cost alone, cloud usually wins.

If cost is not the reason to self-host, what is?

Four reasons hold up: data residency or confidentiality that forbids the data leaving the network or the country; change control, because a hosted model version can be updated on the provider's schedule and you may have to show that the system running today is the system you tested; rate limits or availability, when someone else's queue sits on your critical path; and air-gapped operation, which no API arrangement satisfies. One cost case does hold narrowly — a continuous batch workload that genuinely saturates the hardware, such as unattended overnight document processing.

Can open-weight models match proprietary ones?

Open-weight models like Llama and Qwen are strong and sufficient for most production tasks, though the best proprietary models can still lead on the hardest problems. Choose per use case.

How do we keep AI data private?

Run open-weight models on your own GPUs with data inside your network, including air-gapped deployments for the most sensitive workloads.

What is a hybrid LLM deployment?

Running sensitive or high-volume workloads on private/on-premises infrastructure while using managed cloud APIs for everything else — combining privacy and cost control with fast access to the strongest models.