Open Claude Code. Open Cursor. Open Windsurf. Open Copilot.
Now tell me: how much did your last coding session cost?
You can't. None of them show you.
The Visibility Gap
AI coding tools have a strange relationship with money. They charge you — sometimes per-seat, sometimes per-token, sometimes through your API key — but they never show you what individual sessions, requests, or tasks actually cost.
Here's what the pricing pages tell you:
| Model | Input (per 1M tokens) | Output (per 1M tokens) |
|---|---|---|
| Claude Sonnet 4 | $3.00 | $15.00 |
| GPT-4.1 | $2.00 | $8.00 |
| Gemini 2.5 Pro | $1.25 | $10.00 |
Here's what they don't tell you: how many tokens a typical coding session actually uses. A file read is ~1K tokens. A grep across a codebase is ~5K. A 10-round debug loop with full context replay can hit 200K+. You're multiplying these prices by numbers you can't see.
The result: monthly invoices that are either a relief or a surprise, with no way to understand what drove them.
Why This Matters
For solo developers, it's an annoyance. For teams, it's a budget problem.
Consider:
- No per-session attribution. You can't tell which task or feature drove a cost spike.
- No per-user visibility. Engineering managers see a total bill, not per-engineer spend.
- No budget enforcement. There's no way to set a daily limit and have the tool respect it.
- No model efficiency data. Are you using Opus for tasks where Haiku would suffice? You'll never know.
Cloud computing went through this exact phase. Early AWS bills were a mystery. Then tools like CloudWatch, Cost Explorer, and third-party observability platforms matured. Teams got visibility, set budgets, and optimized.
LLM spend is where cloud spend was in 2010. The tools don't exist yet.
Building the Missing Layer
That's why I'm building Candela — an open-source LLM observability proxy.
The idea is simple: Candela sits between your AI tool and your LLM provider. Every request passes through, gets logged with cost/latency/token data, and continues to its destination. No code changes, no SDK integration, no vendor lock-in.
AI Tool → Candela Proxy → LLM Provider
↓
Cost per request
Token counts
Cache hit rates
Model breakdown
Budget enforcement
It works with anything that speaks the OpenAI-compatible API format — which is most AI coding tools today.
What You Get
Per-request cost tracking. Every API call is logged with input tokens, output tokens, model, latency, and computed cost. Not estimated — computed from the actual token counts × the model's pricing.
Budget enforcement. Set a daily spending limit. When you hit it, Candela can warn you or block requests. No more surprise invoices.
Model analytics. See which models you're using, how often, and what they cost. Spot the $15/day Claude Opus habit when Sonnet would work.
Cache monitoring. Prompt caching can cut costs 50-90% — but only if you know your hit rate. Candela tracks cache read/write tokens per request.
The OpenCode Plugin
I use OpenCode for daily coding, so I built a plugin that surfaces Candela data directly in the terminal:
opencode plugin @candelahq/opencode
It gives you 7 AI tools (so you can ask "how much have I spent today?" in natural language) and 13 slash commands (/cost, /budget, /export, etc.).
The plugin is also open source: @candelahq/opencode on npm.
The Pitch
If you're using AI coding tools professionally, you should know what they cost. Not approximately — exactly. Per session, per model, per day.
Candela is early, open source, and free. If this resonates:
- ⭐ Star the repo
- 📖 Read the docs
- 💬 Open an issue if you want support for your tool/workflow
I'm looking for design partners — teams or individuals willing to try Candela and give honest feedback. If that's you, reach out on GitHub.
What's your monthly AI coding spend? Do you know? I'm curious — drop a number in the comments.
Top comments (0)