Perspectives on PromptOps, governance, observability, and what it takes to ship AI features safely.
What happens when an upstream LLM provider has an outage? The honest answer in three layers: what ships today (same-provider retry on 5xx/429), what's domain-modeled but not wired (RoutingPolicy with primary/fallback), and what's on the roadmap (cross-provider failover with explicit opt-in).
Read more →If your backend calls the OpenAI or Anthropic SDK today, migrating to SchneeAI is deliberately small — one base URL, one auth header, optional model alias swap. The actual diff, language by language, with the pitfalls that show up in real migrations.
Read more →Tracking LLM cost in production is harder than passing through provider invoices. A walk-through of the representation, the two-phase reserve-and-settle pattern, idempotency, and why the credit ledger is append-only.
Read more →Build or buy is the first question every team asks. A frank comparison — what 17-31 engineer-weeks of in-house work actually buys, when building is the right call, and when SchneeAI is cheaper before the first invoice.
Read more →An AI Gateway sits between your application and the LLM providers — handling auth, routing, scanning, logging, and billing. A practical introduction for teams shipping AI features without taking on a year of platform engineering.
Read more →A walk-through of every step a chat completion request takes through the SchneeAI Gateway — from Bearer JWT to Vault write to audit record. The full path, with the design choices behind each hop.
Read more →o1, o3-mini, and DeepSeek R1 emit chain-of-thought before answering. That's powerful — and 6× the cost of GPT-4o. A practical framework for when reasoning models earn their keep, and when they're the wrong tool.
Read more →LiteLLM is the de-facto open-source LLM proxy. SchneeAI builds on the same idea but adds the layers production teams eventually need — billing, budgets, governance, prompt operations, and Vault. A practical comparison for teams choosing between them.
Read more →The Vault is where SchneeAI stores encrypted raw prompts and outputs — separate from operational metadata, with its own retention and access controls. A design walk-through.
Read more →Pre-call PII scanning is harder than post-hoc logging. A walk-through of categories, severity, verification, and what to do when the scanner is wrong — the design behind SchneeAI's 17 categories.
Read more →PromptOps is the discipline of treating prompts as production artifacts — versioned, observable, governed. A practical introduction for teams shipping AI features.
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