Guide · Open-Source AI
How to Deploy DeepSeek or Llama in Your Company: Hardware, License and Stack
At a Glance: DeepSeek and Llama are the two open-weight families most companies shortlist first — DeepSeek for strong reasoning at efficient cost, Llama for the most mature ecosystem and every size. Both are deployable on your own infrastructure and both permit commercial use: DeepSeek under a permissive licence, Llama under the Llama Community License with a signed acceptable-use policy. Most business workloads are well served by a mid-size member of either family on a single GPU server. This guide covers picking, licensing, hardware, the serving stack (vLLM or Ollama), and the step-by-step method we use as an integrator.
DeepSeek or Llama: Which to Deploy
These two families cover most of the "we want an open model we can self-host" conversations we have, and they complement each other:
| Family | Standout strength | Style | Good defaults |
|---|---|---|---|
| DeepSeek | Reasoning and analysis at excellent efficiency | Reasoning style | Analytical tasks, extraction, RAG answers where reasoning matters |
| Llama | Most mature ecosystem, every size, huge tooling support | General-purpose | Drafting, classification, Q&A, agent tooling, hybrid stacks |
The right one is the one that wins on your tasks — benchmark a shortlist (including other families like Mistral or Qwen for multilingual work) on your actual documents before deciding, exactly as we lay out in our model selection guide. For many companies the answer is not "either/or" but "both": a mid-size model for the bulk, a larger one for harder cases, routed by the gateway.
The Licenses, Decoded for Business
DeepSeek — including the latest DeepSeek V4 release (MIT-licensed, August 2026, with a million-token context) — publishes its models under a permissive licence that permits commercial use, self-hosting and modification — one of the reasons it became a default choice so quickly. As always, read the exact licence text for the specific model version you deploy and archive it.
Llama ships under the Llama Community License, which permits commercial use, self-hosting and fine-tuning within defined limits, and — unlike a permissive open-source licence — requires agreeing to a separate acceptable-use policy (a short, routine step when you download from the official repository). The conditions that exist target scale and specific uses, not ordinary internal self-hosted deployment. Our license guide walks through how to review any of these in minutes.
For internal company use, neither family is a licensing blocker. Archive the text, pin the weights, and have legal/business confirm your particular case if you plan to embed the model in a product you distribute.
The Hardware Reality: Size the Model to the Job
The honest hardware answer is the same as for every open model: it depends on the model size you choose, and most business workloads do not need the biggest member of the family.
- Mid-size models — the natural default for document processing, internal Q&A, drafting and classification — fit comfortably on a single GPU server. This is the sweet spot for most companies, and where the cost case for self-hosting is strongest.
- Larger models — needed for frontier-grade reasoning or very heavy workloads — start to need multiple GPUs or a small cluster. This is where you should weigh a private cloud tenancy against on-premise hardware, and where per-token APIs may still win at low volume.
- Quantised variants do the same work with less fast memory, which is why a modest server often goes further than a headline parameter count suggests — benchmark on your tasks, not on the number.
We size the deployment target during the audit against your actual volumes, and we have never once regretted choosing a smaller, well-served model. More on sizing in our enterprise deployment guide and the cost math in our self-hosted vs API guide.
The Serving Stack: vLLM for Scale, Ollama for Simplicity
Two tools cover most deployments:
- vLLM is the production-grade choice for meaningful volume — efficient batching, streaming, an OpenAI-compatible API, and strong support across models. This is what we use for real workloads behind a gateway.
- Ollama is ideal for a quick pilot, developer adoption and edge cases — a single command pulls and serves a model with OpenAI-compatible endpoints, making it the fastest way to get a hands-on benchmark. For company-wide production, we typically graduate the same model to vLLM behind the gateway.
Whichever serves, the model is only a platform once surrounded by the usual layers: a gateway (SSO, per-team access, rate limits, audit logs), RAG on your internal knowledge with cited sources, agents and integrations that execute real workflows (where the ROI concentrates), and monitoring and evaluation re-run before every upgrade.
How to Deploy DeepSeek or Llama in Your Company, Step by Step
Pick the model and clear the licence — Benchmark a shortlist on your tasks, then download the chosen weights from the official repository, pin the version, record checksums, and archive the licence (add the Llama acceptable-use sign-off if you go with Llama). Governance first.
Size the deployment target — Match the model-size to your workload: mid-size on a single GPU server, larger in a small cluster or private cloud tenancy. Compute the break-even against per-token APIs on your numbers.
Serve behind a production inference server — vLLM (or Ollama for a pilot), behind your gateway with SSO, audit logs and rate limits, exposing the OpenAI-compatible endpoint your tools already speak. Switch a base URL, not your stack.
Add RAG, agents and enablement — Connect internal knowledge with cited sources, build the agents and integrations that execute real workflows, monitor and evaluate — then train the teams who will use and operate the platform before handover. As certified trainers we have trained 450+ professionals, including 140+ people across 12 departments at the Luxembourg Stock Exchange; you own the platform, we stay as backup.
What It Costs
The same two budgets as any self-hosted deployment:
Infrastructure. A single GPU server runs most mid-size members of these families comfortably; series of larger models may mean several GPUs or a private cloud tenancy. This is sized during the audit against your volumes and quoted case by case rather than invented here.
Integration. The work that makes the model useful follows our published bands: targeted AI automations typically run 1 500–10 000 €, and custom autonomous agent systems 10 000–30 000 €, always quoted after the audit. Serving the model is a modest slice; RAG, agents, integrations and enablement are where the value concentrates.
And the benchmark we hold ourselves to is operational: at Créabim Architectes the agent system we deployed made regulatory feasibility studies 10× faster and saves more than one full-time equivalent per year. If a self-hosted model cannot plausibly reach that kind of impact for you, the audit should say so — before you buy hardware.
Common Pitfalls
- Buying a cluster for the headline model. The biggest member of the family is rarely the one your tasks need. Benchmark, then size.
- Skipping the licence/acceptable-use step. Llama specifically requires the acceptable-use agreement — routine, but don't skip it in a production pipeline.
- Treating serving as the finish line. A model behind vLLM with no gateway, no RAG, no agents and no trained users is an expensive demo. Adoption is the product.
- Confusing "open weights" with "no data leaves". Only self-hosted and private-cloud deployments keep data in your perimeter; a hosted provider on the same model does not.
Frequently Asked Questions
Can we deploy DeepSeek or Llama on our own servers?
Yes. Both families are open weight and permit self-hosting. Mid-size members run well on a single GPU server — the natural default for most business workloads — while larger members may need several GPUs or a private cloud tenancy. DeepSeek ships under a permissive licence; Llama under the Llama Community License with a signed acceptable-use policy. We size the target to your actual workload during the audit.
Which is better for a business, DeepSeek or Llama?
It depends on your tasks. DeepSeek is known for strong reasoning at efficient cost and a reasoning style; Llama has the most mature ecosystem, every size, and huge tooling support, making it a strong general-purpose default. The honest answer comes from a benchmark on your own data — and many companies deploy both, using a mid-size model for the bulk and a larger one for harder cases behind a routing gateway.
What hardware do we need to self-host DeepSeek or Llama?
For most business workloads, a mid-size model on a single GPU server is enough — this is the sweet spot for both cost and reliability. Larger models need multiple GPUs or a small cluster, which is where a private cloud tenancy is often the pragmatic option. Quantised variants can also run on more modest hardware. Benchmark on your tasks to decide; we size it during the audit.
Is DeepSeek or Llama free to use commercially?
Both permit commercial use. DeepSeek's models are under a permissive licence that allows commercial use, self-hosting and modification. Llama is under the Llama Community License, which permits commercial use and self-hosting within defined limits and requires agreeing to a short acceptable-use policy when you download. For internal self-hosted deployment, neither is a licensing blocker — archive the licence and pin the weights.
Should we use vLLM or Ollama to serve the model?
For production-scale workloads, vLLM is the robust choice — efficient batching, streaming and OpenAI-compatible APIs behind your gateway. Ollama is ideal for a fast pilot, developer adoption and edge cases: one command pulls and serves a model. A common path is to pilot with Ollama, then graduate the same model to vLLM behind the gateway for company-wide use.
How long does a DeepSeek or Llama deployment take?
A benchmarked pilot typically takes a few weeks — model choice and licence review first, then one workflow served on rented or existing capacity. Production hardening depends on your security requirements, but with mature tooling like vLLM, think in weeks, not quarters. The audit at the start gives you a realistic timeline for your context.