Insights & Tutorials
Insights on AI routing, cost control, and getting the most from your API keys with StepBlend.
LLM Cost Control: How to Cap and Reduce AI API Spend
Once you have real LLM usage, cost and visibility keep you up at night. Here's how to cap spend per request, see what actually ran, and route between models without the bill shock.
Multi-Model Routing: One API for OpenAI, Anthropic, and Google
Why use a single routing endpoint instead of calling each provider directly? Vendor neutrality, cost control, and one integration for GPT-4, Claude, Gemini, and more.
Choosing the Right LLM Routing Strategy: Lowest Cost, Balanced, Fastest
When to use lowest cost vs balanced vs fastest vs max reliability. A practical guide to matching routing strategy to your use case.
How to Set Max Cost per LLM Request
Step-by-step: cap spend per request so no single LLM call exceeds your limit. With examples and links to pricing and API docs.
OpenAI vs Anthropic vs Google: When to Use Which (And Why Routing Helps)
Compare GPT-4, Claude, and Gemini on cost, speed, and strengths. Plus why a single routing layer beats locking into one provider.
What Is an LLM Routing Layer?
Definition: a routing layer sits between your app and LLM providers, picks the best model per request, and keeps your keys and cost under control.
Multi-Tenant LLM Routing: Model Override and Per-Tenant Control
Use case: let some tenants force a specific model (e.g. Claude only) while others use automatic routing. One endpoint, per-tenant rules.
OpenAI-Compatible LLM Routing: Add Cost Control Without Rewriting Your Code
If you already use the OpenAI SDK or LangChain, you can get multi-model routing and per-request cost caps by changing one setting: base_url. Same API shape, your keys, no new wrappers.