AI & LLM Networking Guides
LLM APIs are a new category of networked service with their own performance language, cost levers, and operational patterns. Bandwidth matters less than token counts; total latency breaks down into two very different numbers (time-to-first-token and time-per-output-token); a single bad prompt-cache decision can 10x your bill; streaming is the default but the transport choice is non-trivial. These guides explain the networking layer of LLM integration in plain English, with the concrete numbers and engineering trade-offs that actually drive system behavior.
Where to start
If you are integrating an LLM into a user-facing product, start with LLM API latency explained — the two-number latency model (TTFT + TPOT) is the foundation for every other engineering decision, from streaming choice to model selection.
If you are building a high-volume backend, jump to prompt caching and cost optimization — those two together can reduce LLM spend 5-10x for repeating workloads.
If you are operating a self-hosted inference service, see self-hosted LLM networking for batching, KV cache, and the network considerations that determine throughput.
Latency & transport
How LLM responses get from the server to the user, and where the time goes.
Latency & Transport
LLM API Latency Explained
TTFT, TPOT, end-to-end latency math, and what makes each component slow.
Streaming LLM Responses: SSE vs WebSocket
How streaming works, why SSE wins for unidirectional output, and when WebSocket is appropriate.
LLM Tokens, Bytes, and Bandwidth
Tokens vs bytes math, bandwidth implications, and what shows up on your network bill.
Cost & Optimization
Prompt Caching: How It Works
Anthropic and OpenAI prompt caching mechanics, what gets cached, and the savings.
LLM Rate Limits and 429 Handling
TPM, RPM, exponential backoff, queueing, and how to size for bursty workloads.
LLM API Cost Optimization
Caching, batching, model selection, and the operational moves that reduce spend 5-10x.
Inference Mechanics
Inference Latency Components
Prefill, decode, and the KV cache — where inference time actually goes.
Batching vs Streaming
Server-side batching vs wire-side streaming — and why they are independent.
Context Windows and Token Budgets
How to allocate tokens across system prompt, retrieval, and history.
Inference Server Architecture
Inside a modern LLM serving system: scheduler, paged attention, multi-GPU layouts.
Application Patterns
Embedding API Networking
Vector dimensions, batching, bandwidth, and quantization for embedding endpoints.
RAG Architecture Network Patterns
Retrieval-augmented generation on the wire — hops, latency, and failure modes.
Function Calling Network Patterns
Multi-round tool-use workflows and their round-trip cost.
AI Inference: Edge vs Cloud
Where models should run — on device, at the edge, or in the cloud.