Available solutions:
On-prem / in-house (maximum control and privacy):
Ollama – quick start with popular models (e.g., Llama, Mistral).
LM Studio – a convenient panel for working with local models.
vLLM / TGI – when you need steady, reliable model operation at work.
Cloud solutions (fastest start and easy scaling):
OpenAI / Azure OpenAI
Anthropic (Claude)
Google Vertex AI / Gemini
Mistral Cloud
Amazon Bedrock
…and many more.
Most providers offer ready-made integrations and company-grade modes tailored to business requirements.
Hosting your own AI model: pros and cons
Pros
Privacy & compliance – data stays inside your company; easier to meet GDPR.
Full control – you decide which model runs and how it behaves.
Independence – fewer worries about provider outages; lower internal-network latency.
Cost at scale – with many requests, your own infrastructure often pays off.
Cons
Upkeep – you need basic know-how to set up and maintain your own server.
Updates – regular updates and testing are on your side.
Relatively higher unit cost at low usage – compared to a single cloud license from a popular provider.
When to choose cloud
fast rollout, MVPs, testing, non-sensitive data, day-to-day work, “small” solutions.
When to choose in-house (on-prem)
sensitive data (finance, legal, healthcare), strict compliance, predictable costs and steady load.
Costs & quality — quick cheat sheet
Few queries / variable traffic: cloud is usually cheaper to start.
High, regular volume: self-hosting often wins over the long run.
Answer quality: top commercial models shine at creativity; for many back-office tasks, smaller models plus your company knowledge base are enough.
Data, privacy, regulations (GDPR, AI Act)
What data can I send to chat? In the cloud — stick to public or anonymised data. Sensitive data only in a secure company environment.
What happens to data in the cloud? Business modes typically promise no training on your data and clear retention rules. Always check the contract and processing region.
Does AI train on my data? In enterprise plans — generally no. In-house — you decide.
GDPR & AI Act: emphasise transparency, data minimisation and human oversight. A well-designed process meets these without excess paperwork.
How we do it at Bitecode
We take a hybrid approach. We combine proven cloud services (e.g., OpenAI/ChatGPT, Anthropic Claude, Google Gemini, Mistral) with private deployments running at the client.
Right model for the job – e.g., GPT-4/5 for content, Llama 3 or Mistral for internal document analysis.
Answers based on your materials – not on the model’s memory.
Security & compliance – roles, encryption and clear logs; a design that’s GDPR-ready and aligned with the spirit of the AI Act.
Clear metrics – you see costs, quality and outcomes in real time, making scale-up decisions easy.
5-step deployment process:
Needs analysis – pick the top 1–2 processes to optimise.
Choose the approach – cloud, in-house or hybrid.
Fast iterations – test and refine until the result is solid.
Final tests & launch – short training, go-live and baseline measurement.
Run & evolve – regular reviews, updates and continuous development.
Working with AI in your company — practical tips
Start with one measurable process (e.g., invoices, customer FAQ, contract review).
Match the model to the task — check public benchmarks; one model may be better for coding, another for data lookup.
Use your own knowledge base for consistent, verifiable answers.
Set simple data-sensitivity rules: anonymise sensitive info, clear permissions, access to query history.
Measure weekly: time, cost and quality.
Summary
If privacy, peace of mind and predictable costs matter, hosting your own AI model is a strong direction. With a hybrid setup, you take the best of the cloud and combine it with the safety and full control of in-house.
