Skip to content
#

llm-cost-tracking

Here are 17 public repositories matching this topic...

Seven patterns for adding AI features to existing products: sidecar vs in-process, streaming, caching tiers, eval-gated deploys, fallback UX, cost guardrails, vendor-lock hedging. Each says when to skip it. CI parses the doc and lints out fabricated numbers.

  • Updated Jul 26, 2026
  • Python

AI copilot retrofitted into an existing multi-tenant Django SaaS without touching its schema or auth: semantic search, ask-your-data behind a SQL guardrail, summarization, each with a per-tenant cost cap, PII redaction and an eval gate in CI. Tests run offline, no API key.

  • Updated Jul 26, 2026
  • Python

Improve this page

Add a description, image, and links to the llm-cost-tracking topic page so that developers can more easily learn about it.

Curate this topic

Add this topic to your repo

To associate your repository with the llm-cost-tracking topic, visit your repo's landing page and select "manage topics."

Learn more