AI Engineering Blog
Guides and analysis on AI inference, model selection, and GPU infrastructure.
The LLM Cost Paradox: Falling Token Prices, Rising Bills
LLM token prices have fallen 9x to 900x per year, yet inference bills keep growing. Where the tokens go: reasoning, agent loops, and context growth.
Analysis
In-depth pieces on inference economics, model evaluation, and infrastructure decisions.
One API for All LLM Providers: What Silently Breaks
Why teams want a single API for multiple LLM providers, how OpenAI compatibility became the standard, and where that compatibility quietly breaks.
LLM Gateway vs LLM Router: What Each Layer Actually Decides
LLM gateways and LLM routers sit in the same place in the stack but decide different things. How to tell them apart, and which one your workload needs.
Silent Model Swaps: The -latest Trap in LLM APIs
LLM providers re-point aliases, recycle retired model names, and sunset versions on short notice. What our catalog pipeline observed, and how to keep a model identifier from changing meaning underneath you.
Foundations
Foundational explainers on the building blocks of modern AI systems.
What Are Embeddings? How Text Becomes Vectors of Meaning
Text embeddings turn words and documents into vectors so meaning can be measured by distance. How they are produced, what they power, and where they fall short.
What Is Tokenization? How Language Models Read Text
Before a model can read or write a single word, text is broken into tokens. How tokenization works, why subword units won, and why tokens decide both cost and context limits.
What Is Model Routing? Matching Every Request to the Right Model
What model routing is and how it works: sending each request to the best LLM on quality, cost, and latency, and what separates real routing from fallbacks.
Guides
Practical playbooks for choosing models, sizing GPUs, and reducing costs.
Model Routing for Agentic RAG: A Practical Guide
Agentic RAG puts the model in the loop before and after retrieval. Where routing fits per call, how the tool-driven loop routes, and how to wire embeddings on the same API.
Model Routing for Coding Agents: A Practical Guide
Where a routing layer fits in a coding agent: why the tool-calling loop usually pins, how tool calls route when it does not, and how to audit every step.
How to Route OpenAI SDK Calls Across Multiple Models
A working migration guide: point your existing OpenAI SDK calls at a routing layer, decide per call site between auto-routing and pinning, and verify the cutover.
Product & Methodology
How Inferbase tools work and the methodology behind them.
How Our AI Model Recommendations Work
How the 70/25/5 scoring model ranks AI models for your use case, with tag matching, capability analysis, and priority weighting.
How Our GPU Capacity Planning Calculator Works
Every formula and assumption behind our GPU capacity planning tool, covering model memory, KV cache, throughput, latency, and parallelism.
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