Cached Claude API
Cut Claude API costs by 90% via active prompt caching.
What it does
Stop burning your budget on API limits! Our Drop-In Cached API Endpoint cuts your Claude Opus 4.8 and Sonnet costs by up to 90% using Enterprise Active Prompt Caching. You only pay for new data, not redundant context. ✅ Drop-in replacement (just change Base URL) ✅ No KYC & Privacy-first ✅ Pay with Stripe (Cards) or Crypto ✅ Instant delivery Perfect for 24/7 trading bots, AI apps, and heavy developer tasks. Scale your AI infrastructure today!
Does a similar job
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I kept slamming into Claude Code limits mid-session and couldn’t find a quick way to see how close I was getting, so I hacked together a tiny local tracker. Streams your prompt + completion usage in real time Predicts whether you’ll hit the cap before the session ends Runs 100 % locally (no auth, no server) Presets for Pro, Max × 5, Max × 20 — tweak a JSON if your plan’s different GitHub: https://github.com/Maciek-roboblog/Claude-Code-Usage-Monitor It’s already spared me a few “why did my run just stop?” moments, but it’s still rough around the edges. Feedback, bug…
Context GatewayMar 2026 · github.com · ▲220Make Claude Code faster and cheaper without losing context
My Claude quota ran out in 10 minutes, so I made a tool to find out why10d ago · github.com · ▲88Ask Claude Code where your usage went. Token audit, limit diagnosis and usage forensics — built from the session logs already on your machine, nothing leaves it. - kelviq/tare
cache-assembler22d ago · github.com · ▲2Cuts Claude Code and API costs 8.2x by fixing prompt-caching
- CACodeBurn – Analyze Claude Code token usage by taskApr 2026 · github.com · ▲112
Built this after realizing I was spending ~$1400/week on Claude Code with almost no visibility into what was actually consuming tokens. Tools like ccusage give a cost breakdown per model and per day, but I wanted to understand usage at the task level. CodeBurn reads the JSONL session transcripts that Claude Code stores locally (~/.claude/projects/) and classifies each turn into 13 categories based on tool usage patterns (no LLM calls involved). One surprising result: about 56% of my spend was on conversation turns with no tool usage. Actual coding (edits/writes) was…
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