Good AI recommendations should explain capability, cost, latency, data risk, and the work needed after the demo.
We compare model capability, real cost, latency, tool support, data controls, and the failure modes hidden by demos.
A source-checked model selection guide for small teams comparing AI model costs, context windows, reasoning, tool calling, latency, privacy, vendor lock-in, and evaluation method.
A practical cost ledger for small teams estimating AI product spend across API tokens, context windows, prompt caching, batch processing, RAG, vector databases, evaluation, human review, logging, privacy, and infrastructure.
A practical RackNerd VPS review with pricing context, support limits, SLA boundaries, and a checklist before ordering.
A practical backup checklist for small servers, based on CISA guidance, NIST references, and Backblaze Drive Stats.
A domain-owner guide to enabling DNSSEC without confusing it with privacy, uptime, or registrar account security.
A comparison mindset for Plausible, GoatCounter, Matomo, GA4, and server logs, with privacy and performance trade-offs.
A small-site operations guide for ACME, Certbot, HTTP-01, DNS-01, rate limits, and renewal monitoring.
We compare model capability, real cost, latency, tool support, data controls, and the failure modes hidden by demos.
A model is judged by the job: reasoning, coding, retrieval, vision, tools, latency, and reliability.
Input, output, cached tokens, batch modes, grounding, and hosting costs are separated before ranking.
Prices, model names, context windows, and preview status are marked with the date checked.
Preference goes to AI stacks that can be measured, rolled back, logged, and switched when the frontier moves.
Infrastructure cost guide
RackNerd can be a good buy for small services and side projects, if you are comfortable running an unmanaged Linux box and testing backups yourself.
Read the full guideShort notes with primary sources kept close to the claim.