“Llama 3 is free! Why are we paying OpenAI?” This is a common refrain in bank boardrooms. While open weights models are democratizing AI, “free to download” does not mean “free to run”.
The GPU Tax
Running a 70B parameter model with acceptable latency (sub-2 seconds) for a RAG application requires significant VRAM. You are looking at multiple A100 or H100 GPUs. In an on-premise data center, procuring, cooling, and powering these beasts is a massive capital expenditure.
Cloud-hosted GPUs are Opex, but they aren’t cheap either. If your utilization isn’t 100%, you are burning money on idle silicon.
The MLOps Overhead
When you use an API like GPT-4, you don’t worry about batching, quantization, KV caching, or model serving throughput. When you host Llama 3, that is YOUR problem.
You need a dedicated MLOps team to manage the inference servers, handle auto-scaling, ensure high availability, and update the model versions. For a bank, finding and retaining this talent is harder than finding good credit officers.
Security Patching
Open source models and the container stacks they run on have vulnerabilities. Who patches them? In a managed service, the provider does it. In self-hosted, your InfoSec team needs to constantly scan your AI containers for CVEs.
When DOES Self-Hosting Make Sense?
Self-hosting makes sense when:
- Data Gravity: Your data is so sensitive (e.g., core banking transaction logs) that it absolutely cannot leave your physical VPC.
- Volume: You are processing millions of tokens per day, where the unit cost of API calls exceeds the fixed cost of GPU ownership.
- Fine-Tuning: You have a highly specific task (e.g., parsing a proprietary legacy file format) where a small, fine-tuned 8B model outperforms a general 70B model.