Analysis Articles
Browse our analysis articles on AI inference, model selection, and GPU planning.
The LLM Cost Paradox: Falling Token Prices, Rising Bills
LLM token prices have fallen 9x to 900x per year, yet inference bills keep growing. Where the tokens go: reasoning, agent loops, and context growth.
One API for All LLM Providers: What Silently Breaks
Why teams want a single API for multiple LLM providers, how OpenAI compatibility became the standard, and where that compatibility quietly breaks.
LLM Gateway vs LLM Router: What Each Layer Actually Decides
LLM gateways and LLM routers sit in the same place in the stack but decide different things. How to tell them apart, and which one your workload needs.
Silent Model Swaps: The -latest Trap in LLM APIs
LLM providers re-point aliases, recycle retired model names, and sunset versions on short notice. What our catalog pipeline observed, and how to keep a model identifier from changing meaning underneath you.
LLM Fallback Chains: Designing for Outages, Rate Limits, and Deprecations
A single model provider is a single point of failure. How fallback chains keep an AI feature serving through 429s, outages, timeouts, and model sunsets, and the design mistakes that quietly make reliability worse.
The Real Cost of Inference at Enterprise Scale: A 2026 Pricing Audit
A cross-provider audit of LLM inference pricing in May 2026, applying the four-factor cost framework to real numbers across frontier models, OSS hosts, and self-hosted GPUs.
How Close Are Roofline Estimates to Real vLLM Benchmarks?
We benchmarked our GPU sizing estimates against real vLLM deployments: where the calculator matches measured throughput and VRAM, and where it diverges.
Why Most GPU Memory Calculators Are Wrong About KV Cache
Public GPU sizing calculators mostly haven't caught up to 2026 inference. Three specific things they get wrong: paged attention, FP8 KV precision, and Mixture-of-Experts memory.
Claude-Class Agent Workloads: When Self-Hosting Beats the Anthropic API
For agentic workloads built on Claude Sonnet or Opus, the self-host vs API decision is rarely about price. It's about cache mechanics, rate limits, and tail latency. Here's the full math.
LLM Benchmarks Explained: What the Scores Actually Mean
LLM benchmark scores dominate model marketing, but most are saturated or contaminated. A practical guide to reading them critically before choosing a model.
The Hidden Costs of LLM APIs: What Token Price Tables Don't Show
The $/M token figure on LLM provider pricing pages represents roughly 60% of what teams actually pay in production. Caching, output ratios, rate limits, and reliability determine the rest.
Claude Opus 4.7: Output Verification, High-Resolution Vision, and Anthropic's Agentic Ambitions
Anthropic's Opus 4.7 verifies its own outputs, adds 3.75 MP vision, and a new xhigh reasoning tier. Benchmarks, pricing, and how it compares to GPT-5.4 and Gemini 3.1 Pro.
Best LLM for Coding in 2026: A Data-Driven Comparison
Which LLM is best for coding? We rank the top models by coding benchmarks, pricing, and context window to help you pick the right one.
Google Gemma 4: Architecture, GPU Requirements, and What It Means for Open-Source AI
Technical breakdown of Google's Gemma 4 model family: the 31B dense, 26B MoE, and on-device E2B/E4B variants. GPU memory requirements, benchmarks, and where each model fits.
Open Source vs Proprietary LLMs: Which Should You Choose?
Compare open-source and proprietary LLMs on cost, performance, privacy, and customization to pick the right approach for your use case.
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