Why self-host an LLM?
- Data sovereignty. Prompts, documents and outputs never leave your network — important for legal, healthcare, finance and government data.
- Compliance. Easier to meet client contracts, data-residency rules and internal security policies.
- Predictable cost. At high volumes, a tuned self-hosted model can cost less than paying per token.
- Control. You choose the model version, it never changes without warning, and you can fine-tune it on your own data.
What's included
- Model selection — we benchmark open models such as Llama, Mistral and Qwen on your tasks and pick the smallest one that does the job well.
- GPU infrastructure on AWS EC2 or SageMaker, sized to your traffic, with autoscaling.
- Inference optimisation — quantisation and serving frameworks (such as vLLM) to cut latency and GPU cost.
- Private networking — VPC isolation, PrivateLink, no public endpoints, encryption at rest and in transit.
- Access control & audit logs — role-based access and a record of who asked what.
- Guardrails & monitoring — content filters, prompt-injection defences, uptime and quality dashboards.
- RAG on top — connect the model to your documents and databases. See RAG chatbot development.
Self-hosted vs. API: which is right for you?
| Public API (OpenAI, etc.) | Private endpoint (e.g. AWS Bedrock) | Self-hosted in your VPC | |
|---|---|---|---|
| Data leaves your cloud | Yes | Stays in AWS, no training on your data | Never |
| Setup effort | Low | Medium | Higher |
| Cost at low volume | Lowest | Low | Higher (GPU running cost) |
| Cost at high volume | Can be high | Medium | Often lowest |
| Fine-tuning & full control | Limited | Some | Full |
Not sure? That's what the free audit is for — we'll recommend the cheapest option that meets your security needs, even if that isn't self-hosting.
Why IRT Solutions
We're led by an AWS Certified Solutions Architect with 14+ years of database and cloud engineering. We don't just wrap a public API — we design the network, the data layer and the operations around the model, so it's still running reliably a year from now.
Frequently asked questions
Which open-source models can you deploy?
Popular choices include Llama, Mistral and Qwen families, plus embedding and re-ranking models. We choose based on benchmarks on your own tasks, licence terms and GPU budget.
Do we need our own GPUs?
No. We deploy on AWS GPU instances (EC2 or SageMaker) in your account, so there's no hardware to buy. On-premise Kubernetes is also possible for strict requirements.
Is a self-hosted model as good as GPT-4-class models?
For focused business tasks — answering from documents, extraction, classification, summarising — well-chosen open models combined with RAG are often good enough. We test this on your data before you commit.
Can you support ISO 27001 or SOC 2 requirements?
We design the architecture to align with ISO 27001 and SOC 2 controls (isolation, encryption, access control, logging), which supports your certification and audits.