How I Cut 80% of Token Costs Without Losing Quality
Token costs were eating our runway. Here's the playbook I used to slash them by 80% while actually improving output quality.
When we first launched our multi-agent platform, our token bill was growing 40% month over month. At that rate, we'd be dead in six months. Something had to change.
The waste problem
Most AI applications waste tokens the same way: they dump entire conversation histories into every request, stuff system prompts with rarely-used instructions, and use the biggest model available for every single task.including ones that a smaller model handles perfectly.
Strategy 1: Intelligent context routing
Not every request needs GPT-4 or Claude Opus. We built a lightweight classifier that routes incoming tasks to the appropriate model tier. Simple extraction? Haiku. Complex reasoning? Opus. Code generation? Sonnet. This alone cut costs by 35%.
Strategy 2: Shared memory, not shared context
Instead of passing the full conversation history to every agent, we built a shared memory layer. Agents write structured summaries of their findings, and downstream agents only read what's relevant to their task. Context windows went from 100K tokens to under 8K per request.
Strategy 3: Aggressive caching
We implemented semantic caching.if a request is sufficiently similar to a previous one, we serve the cached response. Combined with prompt caching on the API level, this eliminated roughly 20% of all API calls entirely.
The cheapest token is the one you never send.
Results
After implementing all three strategies, our per-user token cost dropped from $0.12 to $0.02 per session. Quality scores actually improved because each agent was getting cleaner, more focused context. The lesson: efficiency and quality aren't trade-offs.they're the same thing.
Written by
Dayssam Bakaar