Batch inference APIs sell you the same models at half price in exchange for latency you can budget for: results arrive in minutes or hours instead of milliseconds. Anthropic charges all batch usage at 50% of its standard synchronous API prices, and Amazon Bedrock bills batch inference at a 50% …
Cloudi AI Decoded: The Typo Triage Worksheet for GSC
Cloudi AI is not a product, a vendor, or an acronym. It is a recurring mistyped search for cloud AI platforms — one dropped character away from “cloud AI” — and in our own Search Console property the “cloudi ai” cluster logged 386 impressions and 1 click in a 28-day …
TTFT vs ITL: Splitting LLM Latency Metrics That Matter
A slow LLM feature is almost never uniformly slow, and treating it as one number guarantees a bad diagnosis. Time to first token (TTFT) is how long the user waits before the first word appears; inter-token latency (ITL) is the cadence of every word after that. They have different causes, …
MCP Server Security: An Engineer’s Audit Checklist
Before an MCP server touches production data, the review that matters covers four questions: which tokens the server accepts, whether tool definitions can change after approval, how consent is enforced when the server proxies a third-party API, and what every invocation writes to the audit log. The Model Context Protocol’s …
Update Pass: Fixing GSC Pages With CTR Below Intent
Run the CTR update pass on pages that already earn impressions but lose the click. Google Search Console defines click-through rate as the click count divided by the impression count, so a CTR below what a page’s intent would justify points at the snippet, not the ranking. The pass has …
Embeddings on CPU: When Self-Hosting Is Enough
For most production semantic search, classification and RAG indexing workloads, a modern server CPU is enough to serve embedding models — no GPU required. Small encoder checkpoints such as MiniLM-class models run comfortably in the tens to hundreds of embeddings per second per core on CPU once exported to a …
Latency Budgets for LLM Features: The SLO Worksheet
Every LLM feature ships with a hidden number: the total milliseconds a user will wait before the feature feels broken. A latency budget makes that number explicit, splits it across pipeline stages, and attaches an SLO with an error budget to keep it honest. Latency budgets are arithmetic, not opinions: …
LLM Observability Logging: What to Keep, What to Redact
Log the envelope, not the letter. For LLM observability, the telemetry that answers most operational questions — latency, token counts, model version, caller identity, error type — never needs the raw prompt, and on managed platforms the provider already writes the full request and response for you. Amazon Bedrock, for …
AI Gateway Rate Limiting and Quota Design That Holds
AI gateway rate limiting works when the gateway meters tokens, not just requests. Every major provider enforces a different mix of tokens-per-minute, requests-per-minute, daily and spend ceilings, and each one scopes those ceilings differently, so a proxy that only counts calls will pass its own limits and still fail upstream. …
RAG on Managed Vector Databases: Architecture Guide
Retrieval-augmented generation on a managed vector database gives you one clear division of labor: the provider runs storage, indexing and retrieval operations while your team keeps the decisions that determine answer quality. In practice, a managed vector database moves indexing, storage and retrieval operations to the provider while your team …
REA: An Agent Toolkit for Reverse Engineering
Reverse engineering usually means an operator picks a tool, learns its API, and moves evidence between programs by hand. REA, an open-source toolkit released on GitHub in April 2026, gives that job to an AI agent instead: hand it an application — source code optional — and it investigates a …
GPU Capacity Planning for LLM Inference: Sizing Steps
GPU capacity planning for LLM inference is one equation followed by three checks: multiply the memory each replica needs (model weights plus KV cache per concurrent request) by the number of replicas your token-throughput target demands, then verify the result against your latency SLO, your provider quota, and a failover …