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Closed vs. open-weight models: When to migrate

Not every call to a closed API needs a frontier model. This guide covers when to move routine work to open-weight models, based on your costs, your task mix, and the control you need over the model.

How to increase LLM inference throughput without breaking your latency budget

Learn how to measure goodput, tune batching and KV cache in vLLM, and decide when another GPU will help, all without breaking your latency SLOs.

How to reduce LLM latency in production: Diagnose before you tune

Diagnose LLM latency before you tune it. Split TTFT, TPOT and end-to-end latency, map them to eight stages, and find which one owns your slowdown.

10 best open-weight AI inference providers for startups

How to pick an open-weight inference provider before you can forecast your traffic: criteria to test, and how 10 providers compare on billing, commitments and risk.

Kimi K3 vs. GPT-5.6 Sol: Performance, cost, and tradeoffs

Compare Kimi K3 and GPT-5.6 Sol on performance, pricing, context, weights, architecture, and deployment tradeoffs.

Prefill vs. decode in LLM inference

Understand how prefill and decode shape time to first token, streaming performance, KV-cache pressure, and LLM serving decisions.

Designing multi-region LLM deployments with gateway architecture

Learn how multi-provider LLM gateways solve routing, failover, and latency across regions. Compare self-hosted vs. managed options for production inference.

Parasail vs. DeepInfra: Cost, latency, model selection, and engineering support

A side-by-side comparison of Parasail and DeepInfra on serverless and dedicated pricing, engineering support, model catalog, cold starts, and compliance.

Guide to optimize batching and throughput for offline LLM inference in 2026

Tune vLLM, TensorRT-LLM, and SGLang for offline LLM batch inference. Config knobs, KV cache math, prefix caching, and speculative decoding tradeoffs for 2026.