AI API routing

Insights Sep 10, 2026 · 8 min read

Token Compression for LLMs: Cut Context Cost Before Routing

Token compression helps teams remove noisy context before model calls, lowering cost and improving routing decisions without hiding quality trade-offs.

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Developers Sep 9, 2026 · 5 min read

LLM Tracing at the AI Gateway: See Every Model Call

LLM tracing helps teams see model calls, latency, token usage, errors, and evaluation data from one gateway layer.

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Developers Sep 9, 2026 · 6 min read

Reduce LLM API Costs With Smart Routing: A Practical Guide

Learn how smart routing, prompt caching, provider fallbacks, and batch processing can reduce LLM API costs without flattening quality.

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Insights Sep 8, 2026 · 4 min read

LLM Vendor Lock-In: 5 Ways to Build a Flexible AI Stack

LLM vendor lock-in shows up in drift, outages, and brittle integrations. Here are five practical ways to keep your AI stack portable and resilient.

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Developers Sep 8, 2026 · 8 min read

AI Risk Management: Put Controls on Every Model Call

AI risk management moves from policy to practice when teams control model routes, access, budgets, logs, and failover at request time.

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Developers Sep 8, 2026 · 8 min read

AI Safety vs AI Security: Control Risk at the Model Call

AI safety and AI security solve different production risks. Use this checklist to separate behavior risk, exploitation risk, and model-call controls.

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Developers Sep 8, 2026 · 7 min read

SLM vs LLM: Route Production Tasks to the Right Model

A practical SLM vs LLM guide for routing production AI by task complexity, latency, cost, and quality instead of choosing one model size for every request.

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Developers Sep 8, 2026 · 7 min read

Open-Weight Model Routing: Add Fast Inference Without Rewriting Apps

Open-weight model routing lets teams test faster providers, control cost and fallback, and keep one stable AI API integration as models change.

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Developers Sep 8, 2026 · 8 min read

DeerFlow AI Agent Framework: Route Usage With Control

DeerFlow AI agent framework gives teams an open-source way to run long-horizon agents. Learn how to route model usage through ShareAI with cost visibility, failover, and Builder monetization.

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