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* feat(spend): track prompt compression saved tokens in daily spend aggregates Native compression interception now records tokens_before/after/saved into the request litellm_metadata so savings land in the SpendLog metadata JSON under a typed compression_savings key. A single normalizer (extract_compression_saved_tokens) sums that key with Headroom guardrail tokens_saved; the two writers are disjoint and run at different stages, so summing never double-counts. The spend-log redactor now preserves purely numeric compression stats inside guardrail_response so Headroom savings survive the store_prompts_in_spend_logs=false default. compression_saved_tokens is threaded through BaseDailySpendTransaction, queue aggregation, the daily upsert blocks, a new BigInt column on all six daily spend tables, and the daily activity read path (SpendMetrics, DailySpendMetadata, raw-SQL rollups) * fix(spend): normalize legacy guardrail shapes and float token stats in compression savings reader * feat(spend): aggregate compression and prompt caching dollar savings in daily rollups Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(spend): update daily spend aggregation fixtures for savings columns Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * feat(ui): add Cost Optimization dashboard page New left-nav Cost Optimization page under Observability that surfaces money saved by prompt compression and prompt caching. It reads the daily activity rollup (userDailyActivityCall / get_daily_activity) and never scans SpendLogs, so it stays fast at 1M+ rows. Renders a Total saved card, per-driver Compression and Prompt caching cards, a savings-over-time area chart, and a savings-by-driver donut, all aggregated in memory from the per-day metrics.compression_savings_spend and metrics.prompt_caching_savings_spend fields. Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --------- Co-authored-by: Krrish Dholakia <krrishdholakia@berri.ai> Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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| CLAUDE.md | ||
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| README.md | ||
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This is a Next.js project bootstrapped with create-next-app.
Getting Started
First, run the development server:
npm run dev
# or
yarn dev
# or
pnpm dev
# or
bun dev
Open http://localhost:3000 with your browser to see the result.
You can start editing the page by modifying app/page.tsx. The page auto-updates as you edit the file.
This project uses next/font to automatically optimize and load Inter, a custom Google Font.
Learn More
To learn more about Next.js, take a look at the following resources:
- Next.js Documentation - learn about Next.js features and API.
- Learn Next.js - an interactive Next.js tutorial.
You can check out the Next.js GitHub repository - your feedback and contributions are welcome!
Deploy on Vercel
The easiest way to deploy your Next.js app is to use the Vercel Platform from the creators of Next.js.
Check out our Next.js deployment documentation for more details.