FangCode: A Coding Agent You Can Budget
FangCode is live at fangcode.ai — agentic coding in the terminal from a single static Go binary, on one $10 plan with the same rate at every hour. Every turn writes down the tokens, cache hit rate and dollars it spent.
An Agent You Cannot Budget Is an Agent You Cannot Deploy
Today FangCode is live at fangcode.ai.
FangCode brings agentic coding to the terminal as one static Go binary. It is a $10 a month subscription with reliable access to DeepSeek V4 Flash, and a receipt under every turn.
The reason it exists is a budgeting problem rather than a capability one. Teams do not fail to adopt coding agents because the models cannot code. They fail because nobody can say in advance what a month of running one will cost, and a cost you cannot forecast is a cost you cannot approve.
One Rate at Every Hour
FangCode publishes its limits rather than describing them. The plan is a rolling five-hour window worth $18 of usage, and the rate does not change with the clock.
Measured on the agent turn OpenCode Go publishes — 410 fresh input, 71,300 cache read, 310 output tokens — the same model and the same window shape gives FangCode 20,000 requests against OpenCode Go's 7,600. That is 2.6 times the volume.
The pricing difference is structural, not promotional. Competing plans bill peak hours at double between 01:00 and 04:00 and again between 06:00 and 10:00 UTC. FangCode charges one card at every hour, which is what makes a monthly figure predictable instead of a range.
A Receipt Under Every Turn
Every turn reports what it spent: tokens consumed, cache hit rate, and dollars. Not a monthly invoice you reconcile after the fact, and not an estimate — the actual cost of the turn you just ran, printed under it.
This is the mechanism behind the budgeting claim. A team can watch cost per task accumulate in real time, attribute it to the work that caused it, and decide where an agent is worth running before the bill arrives rather than after.
What Ships
- One static Go binary for macOS and Linux, installed with a single line
- DeepSeek V4 Flash with a 1M-token context and a pinned upstream route
- Usable from any agent that speaks the OpenAI or Anthropic protocol, not only from FangCode's own terminal
- The live model list served at /api/v1/public/models, so the route is inspectable rather than asserted
- Credit top-ups when a month runs long, and cancellation at any time
Getting Started
Installation is one line, and fang setup probes the endpoint before you spend anything on it.
Full pricing, the published limits and the comparison methodology are at fangcode.ai.