Serving stacks, inference engines, GPU orchestration and cost-control tooling.
A routing layer that keeps your coding agent alive when one provider rate-limits you — worth it for the fallback alone.
The last few percent of GPU throughput, paid for in build complexity and hardware lock-in.
The front end you hand to users when the model layer is already done.
Multi-user, multi-provider chat platform with agents and MCP already wired in.
High-throughput LLM inference engine with PagedAttention — the default choice when you need to squeeze a GPU.
One command to run an LLM on a laptop — the fastest way to get a model in front of a customer.
CPU-first inference in plain C/C++ — runs where a GPU cannot go.
HuggingFace's Rust-based serving stack with tensor parallelism and streaming built in.
Serving engine built for structured outputs and heavy prefix reuse.
One OpenAI-shaped interface for 100+ model providers, plus a proxy that tracks spend per key.
Every multi-tenant RAG eventually discovers the same thing: filtering results after retrieval is not isolation. The filter has to constrain the search, not trim its output.
The risk is not that the model says something wrong. It is that the agent does something wrong — with credentials, in a system that does not have an undo.
"Self-hosting is cheaper" is only true above a certain utilisation. Below it, you are paying for an idle GPU and an engineer's attention — and that second line item is the one nob…
A RAG request feels slow long before it is slow. Knowing which of the five stages is responsible is the difference between a targeted fix and three weeks of replacing the wrong co…