Been building small AI tools on top of Kimi K2 and the thing nobody tells you upfront: your bill doesn't scale with users, it scales with context length. A single agentic loop with tool calls and long context can chew through millions of tokens before you've even shipped a feature.

A few things that actually moved the needle for me:

• Cache your system prompts. If you're re-sending the same 2k-token instructions on every call, you're burning money on nothing new.

• Trim conversation history aggressively. Summarize old turns instead of replaying the full thread.

• Batch small tasks instead of firing one request per item — fewer round trips, less overhead per call.

• Buy tokens in bulk when you find a fair rate. Official API pricing adds up fast once you're testing at scale — I started reselling my surplus Kimi K2 tokens (100M for $15) for exactly this reason, so other builders aren't stuck paying full price just to iterate.

If you're building with Kimi K2 and want to compare notes on cutting costs, drop a comment — happy to share what's worked.