The short answer
Use an AI service when its business terms satisfy your policies, usage is light or uneven, or you need the most capable model. Host an open-weight model on GPU servers you control, possibly in Nigeria, when data must not leave your environment or the country, usage is steady and high, or you need control over the model and updates. Many organisations use both, routing by sensitivity.
Side by side
| AI service (business terms) | Self-hosted open-weight model | |
|---|---|---|
| What it is | OpenAI, Anthropic, Google or Microsoft, directly or through your cloud account | Llama, Qwen, DeepSeek, GLM, Mistral, gpt-oss or similar, in your cloud account or on your hardware |
| Where prompts go | To the provider, under its business terms; from Nigeria, almost always processed abroad | Nowhere outside your environment; on servers in Nigeria, nowhere outside the country |
| Training on your data | Excluded by business terms | Not possible: the model runs only for you |
| Retention | Often a limited period for abuse monitoring; zero data retention for eligible customers, usually by approval | Your choice, including none |
| Answer quality | The most capable models | Strong for many tasks; behind leading commercial models on the hardest |
| How you pay | Per use, usually in US dollars: cost follows volume and the exchange rate | Per server hour, or hardware and power you own, busy or idle |
| Effort to run | Low: the provider runs it | Yours or your IT partner's: patching, monitoring, capacity, updates |
More differences
| AI service | Self-hosted | |
|---|---|---|
| Time to start | Hours | Days to weeks, including sizing and security |
| Model changes | The provider updates and retires models on its schedule | You decide when to change, and test first |
| Evidence for auditors | The provider's terms and reports, plus your transfer basis | Your own architecture, network rules and logs |
Through your cloud account, the models run in the cloud provider's environment. Microsoft, for example, states that prompts sent to the models it sells through Azure are not available to OpenAI or other model providers (Microsoft Learn).
How to compare the cost
Compare both on one measure: cost per month at your expected volume, in one currency (both are usually priced in US dollars), allowing for exchange-rate movement.
- AI service: requests per month × tokens per request × the rate. Starts near zero, grows with use.
- Self-hosted: GPU server hours × the hourly rate (or owned hardware spread over its life), plus storage, networking, power, monitoring and people time. Roughly flat, for ten questions or ten thousand.
Whether self-hosting is cheaper depends on how busy the server stays: one running around the clock for a business-hours team pays for many idle hours.
Four ways to tilt the numbers
- Right-size the model: a smaller model that meets your accuracy bar needs less GPU memory and costs less to run.
- Schedule capacity: start and stop servers with working hours, or run batch work overnight on the same hardware.
- Share the platform: one private endpoint can serve several applications, spreading the fixed cost.
- Count the people time: patching, monitoring and testing new models are real costs, whether your team or a partner does them.
Uncontrolled use costs money either way: OWASP lists "unbounded consumption" among top AI application risks, so set limits and alerts on both (OWASP). The rest of the budget: what an AI project costs.
Rather talk it through? If you are not sure whether your volumes justify a private server, we can model both options on your expected use. Talk to a Promatics specialist
Quality: test it, do not assume it
Benchmarks change monthly and rarely match your work: test answers to your questions, from your documents, in your languages.
- Evaluation setReal questions with the answers you expect.
- Run candidatesTwo or three open-weight; one commercial, if policy allows.
- CompareAccuracy, response time and cost per answer.
- Decide, re-testUse the numbers; re-run when new models are released.
A well-chosen open-weight model often meets the bar for answering from documents, summarising, drafting and extracting data. Independent trackers report the best open-weight models trailed leading closed models by about four months on average in 2026 (Epoch AI), more on the hardest reasoning and coding tasks: one reason many organisations use both.
Using both: route by sensitivity
Confidential to private
Client files, health records, financial data, internal plans: never leave your environment.
The rest to a service
Public information, marketing drafts and general research, under business terms.
One gateway decides
Routes by data classification, user or application, and logs each decision.
Self-hosted inference servers such as vLLM offer an OpenAI-compatible API (vLLM), so the same code can usually call either, keeping you free to switch. Test each model anyway: chat formats and tool calling differ.
Decide in ten questions
- Does any policy, contract or regulator forbid sending this data outside?
- Would business terms, zero retention and a transfer basis satisfy your DPO and clients?
- Must data stay in Nigeria, on your premises or a Nigerian data centre?
- How many requests per month now, and in a year?
- Is usage steady through the day or in bursts?
- What accuracy is good enough, measured on your own questions?
- How fast must answers be at peak use?
- Who will patch, monitor and update a self-hosted platform?
- How will you show auditors and clients where AI data goes?
- Could a hybrid design give most of the benefit for less effort?
When to bring in help
Data not sensitive and usage modest? Start with a business AI service and good settings; you can move later. Bring in help when:
- Confidential or personal information is involved
- Clients or regulators ask where AI data goes
- Volumes are growing fast
- You are considering buying GPU servers
Our private AI and self-hosted models service tests both options on your questions, designs the hosting, and deploys and runs the platform if self-hosting wins. Where a service with strong terms fits, we help set it up through AI strategy and readiness.
Sources and further reading
Product capabilities and guidance change. These are the primary sources this article relies on, checked on the review date above.
- Online serving (OpenAI-compatible server), vLLM documentation
- Open models lag state-of-the-art closed models by 4 months, Epoch AI
- Data, privacy and security for Microsoft Foundry models, Microsoft Learn
- LLM10:2025 Unbounded Consumption, OWASP Gen AI Security Project
- General Application and Implementation Directive (GAID) 2025, Nigeria Data Protection Commission
This article is general information, not legal, accounting or security advice for your specific situation. Examples are hypothetical unless stated otherwise.