DeepSeek and other open-weight models let a company download model weights and run them on its own infrastructure or a chosen cloud, instead of sending every request to a closed API. Moving from pilot to production means confirming the licence, choosing where the model runs, testing quality on your real tasks, setting up security and monitoring, and planning who maintains it. This checklist covers each step in order.
Open-weight is a slightly different idea from open source. The weights are published so you can run and often fine-tune the model, but training data and code may not be fully released, and licences vary. Well-known families include DeepSeek’s models, Meta’s Llama, Mistral’s open models, Alibaba’s Qwen and Google’s Gemma. Each has its own licence terms.
Why are teams considering open-weight models?
Three reasons come up most often. Data control: running a model in your own environment means prompts and documents do not leave it, which matters for legal, health or financial content. Cost at volume: for steady, high-volume tasks such as classifying product reviews or tagging content, self-hosting can be cheaper than paying per token, though that depends heavily on usage and hardware. Customisation: you can fine-tune an open model on your own examples.
There are also reasons for caution. Running models well takes engineering skill. The largest open models need serious GPU capacity. And for some providers, there are questions about data handling and jurisdiction when using their hosted apps or APIs, which is separate from running the weights yourself. Several governments and companies have restricted the use of DeepSeek’s hosted services for that reason. Running the weights on your own infrastructure avoids sending data to the provider, but you should still assess the model’s behaviour and licence.
Step 1: Have you checked the licence?
Read the licence for the exact model version you plan to use. Some are permissive, such as MIT or Apache 2.0, while others add usage restrictions or conditions above a certain number of users. Check whether commercial use is allowed, whether outputs can train other models, and whether attribution is required.
- Record the model name, version and licence text in your project files.
- Confirm commercial use for your case.
- Note any acceptable-use policy attached to the licence.
- Ask legal to review if the licence is custom rather than a standard one.
Step 2: Where will the model run?
You have three main options. Managed platforms such as AWS Bedrock, Azure AI Foundry, Google Vertex AI and dedicated inference providers host many open models with business terms and regional data residency. Your own cloud GPUs give more control but require operations work. On-premise servers give the most control and the highest maintenance burden.
For a first production use, managed hosting in a region you trust is usually the quickest route. Distilled or smaller variants can run on modest hardware, and tools such as Ollama, vLLM or llama.cpp make local testing easy, but production traffic needs proper serving, scaling and uptime monitoring.
Step 3: How do you test quality on your own tasks?
Public benchmark scores tell you little about your use case. Build an evaluation set of real examples: fifty to two hundred inputs with expected outputs or scoring criteria. Run the open model and your current closed model on the same set and compare.
What to score
- Accuracy against the expected answer or a human judgment.
- Instruction following, such as output format and length.
- Language quality in every language you need, not only English.
- Refusals or unwanted content on sensitive topics.
- Latency per request at realistic load.
Reasoning models, such as DeepSeek’s R1 family, produce long chains of thought before answering. That can improve accuracy on complex tasks but adds latency and cost, so test whether your task needs it.
Step 4: What security and safety controls are needed?
An open model in your environment is your responsibility. Put it behind authentication, log requests and responses with care for personal data, and limit which systems it can call if it uses tools. Add input and output filters for your use case, such as blocking personal data in outputs or checking that generated product claims match approved facts.
Test for prompt injection if the model reads user-submitted text or web pages. Test behaviour on topics where the model’s training may carry bias or restrictions; some models answer certain political or historical questions in ways that do not match your standards, and you should know that before customers see it.
Step 5: How will you monitor it in production?
Models do not change on their own when self-hosted, which is an advantage, but inputs do. Track error rates, latency, cost per request and a sample of outputs reviewed by people each week. Keep the evaluation set and rerun it before any model update or prompt change. Set a clear rollback plan to the previous version.
Step 6: Who owns it after launch?
Name an owner for the model, the prompts and the infrastructure. Decide how often you will review newer model versions, since the open-weight field moves fast. Budget for GPU costs or hosting fees, and for the engineering time that keeps everything patched. A model nobody owns becomes a security risk within months.
How Alien Road applies open-weight models for clients
In a typical engagement we start with the task, not the model. Discovery identifies which AI tasks are high in volume, sensitive in data, or both, because those are where open-weight models make the most sense. We then build an evaluation set from the client’s real examples and test two or three candidate models against the closed model they use today.
If an open model performs well enough, we deploy it on a managed platform in the client’s preferred region, add logging and output checks, and run it in parallel with the existing setup for a few weeks. Reporting covers quality scores, cost per thousand requests and latency, side by side. The final decision rests on that evidence, and sometimes the answer is to stay with the closed model for one task and switch for another.
Where to start this week
Choose one repetitive AI task your team runs today, such as tagging support tickets or summarising reviews. Collect fifty real examples with the correct result. Run them through a small open-weight model locally with Ollama and through your current tool, and compare the scores. Read the licence of the model you tested. That small experiment tells you whether a full pilot is worth the effort.
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