AI Architecture Comparisons
Source-reviewed architecture decisions tested against the same workload, evidence, access, latency, and cost criteria.
How to use these comparison workbooks
These pages are decision tools, not winner lists. Start by writing down the workload, users, data sensitivity, required integrations, acceptable failure rate, and monthly budget. Compare products or architectures against the same cases and the same scoring rule. A feature should receive credit only when the linked primary source supports it or your own controlled test reproduces it.
Separate hard gates from preferences. Security, consent, retention, accessibility, and legal requirements are pass-or-fail conditions; convenience and interface preferences can be weighted later. Record the product version, plan, region, test date, inputs, outputs, reviewer, and evidence URL so another person can repeat the review. Pricing and product documentation can change, so reopen every cited source before purchasing or deploying.
Each workbook states its review scope and limitations. Documentation review does not prove real-world quality, and a small pilot cannot represent every user or failure mode. Use the downloadable artifacts as a starting point, add cases from your actual environment, and obtain security, privacy, legal, accessibility, and domain review when the decision has material consequences.
Claude vs Gemini API Pricing: 5 Decision Points
Claude's Pro plan ($20/mo) excludes API access; Gemini offers a free API tier with a content-improvement condition. Here's how to evaluate both for your startup.
RAG vs Long Context: A Decision Workbook, Not a Feature Contest
Use a controlled evaluation set and architecture worksheet to choose RAG, long context, or a hybrid for a real document workload.