AI Inference, Explained
Practical guides on GPU economics, serverless AI infrastructure, and cutting your inference bill — from the Lexora team.
Kimi vs Claude vs DeepSeek: Which Frontier Model in 2025?
Head-to-head on coding, reasoning, cost, and API access. Kimi K2, Claude Sonnet, and DeepSeek V4 Pro — and when to use each.
Kimi API via OpenAI SDK: Complete Integration Guide
Kimi K2.7 Code is OpenAI-compatible. Working Python and TypeScript examples, model IDs, streaming, and production tips.
DeepSeek API in 2025: Models, Pricing & Integration
DeepSeek V4 Pro and Flash — what makes them different, how to call them with the OpenAI SDK, and reliable access without a waitlist.
Why GPUs Stay Idle 80% of the Time
The hidden economics of GPU utilization — why even well-run AI teams waste most of their compute budget on idle hardware.
What Is Serverless AI Inference?
A plain-English explanation of how serverless inference works, why it matters, and when you should use it instead of dedicated GPU pools.
Pay-per-Token vs Renting a GPU: A Cost Breakdown
Side-by-side numbers for a typical AI startup running 10M tokens/day. The difference is bigger than you think.
How Startups Waste Money on Idle GPUs
Four patterns that cause AI startups to hemorrhage compute budgets — and how usage-based inference eliminates all of them.
How Much Does AI Inference Actually Cost?
Real numbers: what you pay per token on OpenAI, RunPod, Together AI, and Lexora — and what drives those costs.
GPU Rentals vs Inference APIs: Which Should You Choose?
A decision framework for choosing between renting dedicated GPUs and using inference APIs at every stage of your startup's growth.