AI Infrastructure
Run Your Own AI – Private, Fast, and Cheaper Than You Think
The Problem with Managed AI APIs
OpenAI, Anthropic, and Google charge per token. At low volumes that’s fine. But as your usage grows, costs scale linearly – and every prompt you send passes through a third-party server. For companies handling sensitive data, that’s a compliance risk. For companies at scale, it’s an unnecessary expense.
There is an alternative.
What I Offer
I deploy open-source AI models on your own AWS infrastructure — giving you a private, cost-controlled inference endpoint that your applications talk to exactly like they talk to OpenAI. Same API format. No per-token fees. Your data stays in your environment.
LLM Deployment on AWS GPU Instances
Get a running Llama, Mistral, or other open-source model on an AWS GPU instance (g4dn.xlarge or larger), served via a REST endpoint. OpenAI-compatible API included – point your existing code at a new URL and you’re done.
Inference Cost Optimization
Choose the right instance type, quantization level, and spot vs on-demand strategy for your workload. A typical 8B parameter model runs for under $1/hour and generates 34+ tokens per second – enough for most production use cases.
RAG Pipelines
Connect your LLM to your own documents, knowledge base, or database using Retrieval-Augmented Generation. Your model answers questions grounded in your data, not just its training.
Ongoing Management
Auto-shutdown when idle, CloudWatch monitoring, EBS snapshots for fast restarts. Set it up once, pay only when you use it.
Why This Makes Sense Financially
OpenAI API (GPT-4o) | Self-Hosted (Llama 3.1 8B) | |
|---|---|---|
Output tokens | $15 / 1M tokens | ~$0 (infrastructure only) |
Data privacy | Third-party servers | Your AWS account |
Control | None | Full |
Setup cost | None | One-time deployment fee |
Monthly cost at scale | Hundreds to thousands | $50–150/month |
Self-hosting makes sense once you’re past the early experimentation stage and have a predictable, recurring AI workload.
Proof of Work
I recently deployed Llama 3.1 8B on an AWS g4dn.xlarge instance using llama.cpp with full CUDA acceleration. The result: 34 tokens/second text generation, 1,093 tokens/second prompt processing, running on a $0.53/hour instance.
Who This Is For
- SaaS companies with growing OpenAI API bills
- Companies handling sensitive or regulated data that cannot leave their environment
- Development teams that need a private LLM for internal toolingStartups that want AI capabilities without long-term API vendor lock-in
How It Works
- Free 30-minute call – you describe your use case, I assess whether self-hosting makes sense for you
- Proposal – I send a fixed-fee quote for deployment, or an hourly estimate for more complex setups
- Deployment – I set up the infrastructure on your AWS account, you keep full ownership and access
- Handover – I document everything and hand over a running system you or your team can manage
Get Started
Describe your situation and I’ll reply within 24 hours with an honest assessment of whether self-hosted AI makes sense for you – and what it would cost.