Infron Tutorials

Learn what LLM tracing is, the difference between application-level and gateway-level tracing, and how to debug AI apps that fail silently.

How to detect LLM hallucinations in production systems. Compare 5 detection methods: semantic similarity, LLM-as-a-Judge, entailment checking, token confidence, and self-consistency sampling

Build a Seedance 2.0 real human pipeline on Infron: turn a reference image into identity-locked, lip-synced video, with reference image and video on Infron

Generate Seedance 2.0 real human video through one Infron API. Verified real-person clips, enterprise access, and 400+ other models on a single key.

Learn why OpenClaw teams use Infron as their model provider layer — one OpenAI-compatible API, 300+ models, BYOK support, and built-in fallback routing.

Prompt caching can cut LLM input costs by up to 90%. Here is why most teams get near-zero cache hit rates and how Infron fixes it across every provider.

A practical guide to Infron with real-world examples, showing how to route AI models, handle fallbacks, and simplify multi-provider APIs.

Learn how Kimi K2 Thinking maintains stability on long tasks with Claude Code using chaining, memory management, and adaptive context strategies.

Step-by-step guide to using Kimi K2 Thinking in Codex for AI-assisted coding, from setup and API key to generating and debugging code efficiently

A practical guide to Infron usage-accounting, covering real-time cost tracking, transparent billing, and accurate usage data for AI infrastructure teams.