Comparison - deployment
Enterprise LLM deployment options compared
The model matters less than most procurement processes assume. How you deploy it decides your compliance posture, your running cost and how painful the next model change will be.
There are four realistic ways to run a large language model inside an enterprise. Each trades control against effort, and the right answer usually differs per workload rather than per company. Running two of these side by side is normal and often cheaper than forcing everything into one.
| Option | Control and residency | Effort to run | Cost shape | Best for |
|---|---|---|---|---|
| Direct vendor API | Lowest. Region choice only, sub-processor is the vendor | Lowest. Days to first production call | Per token, no floor | Early builds, low volume, non-regulated data |
| Cloud platform (Bedrock, Vertex, Azure AI) | Moderate. Runs inside your existing cloud agreement and region | Low. Reuses IAM, logging and networking you already have | Per token, plus platform overhead | Most regulated enterprises, the common default |
| Private or dedicated capacity | High. Isolated capacity, contractual isolation guarantees | Moderate. Capacity planning becomes your job | Committed spend, high floor | Steady high volume, strict isolation requirements |
| Open weights, self hosted | Highest. Nothing leaves your perimeter | Highest. GPUs, serving, upgrades, evaluation, on-call | Fixed infrastructure, low marginal cost | Very high volume, air-gapped or sovereignty bound work |
How to choose without a six month evaluation
Answer three questions. What is the most sensitive data class this workload touches, and does anything prohibit it leaving your cloud account. What is the steady state volume, since below a few million tokens a day self hosting rarely beats per token pricing once engineering time is counted. And how fast do you need to adopt new models, because self hosting means you adopt on your own schedule, which is both the advantage and the cost.
The mistake worth avoiding
Choosing self hosted for control and then running it without evaluation sets, load testing or an upgrade path. Control that nobody operates is worse than a managed service with a good contract. The teams who succeed with open weights treat the model as one component of a serving stack they own, with the same on-call discipline as any other critical service.
Keep the choice reversible
Whichever option is picked, build behind an abstraction with a regression suite attached. Prompts, evaluation sets and retrieval indexes stay yours. A model or deployment change should then be a configuration change plus an evaluation run, measured in days. Digiton builds and operates systems this way across 8 countries. A short AI audit settles the deployment question per workload rather than as a single company-wide decision.
Frequently asked questions
What are the enterprise LLM deployment options?
Four: calling a vendor API directly, running models through your existing cloud platform such as Bedrock, Vertex or Azure AI, buying private or dedicated capacity, or self hosting open weight models. They differ mainly in control and residency, operating effort, and whether cost is per token or fixed infrastructure.
Is self hosting an LLM cheaper than an API?
Only at sustained high volume, and only after counting engineering time. Below roughly a few million tokens a day, per token pricing usually wins because GPU capacity sits idle and someone still has to handle serving, upgrades, evaluation and on-call. Compare total cost of ownership, not the price per million tokens.
Can we change deployment model later?
Yes, if you designed for it. Build behind an abstraction, keep the prompts, evaluation sets and retrieval index in your own repositories, and maintain a regression suite. Then a change is a configuration update plus an evaluation run. Without those, switching means rebuilding and revalidating from scratch.
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