Capability-based routing separates traffic by task complexity and quality sensitivity. Start by cataloging use cases (e.g., short answers, content generation, code editing, translations, multi-turn assistants). For each use case, define a primary low-cost path and a secondary high-quality path. Implement a lightweight classifier to estimate complexity at request time, or use confidence signals from cheaper models to gate escalation. This reduces overall expensive inference while ensuring tricky requests still receive appropriate resources.

Prompt compression is a practical discipline: replace verbose instructions with concise templates, move constant context server-side, and use compact representations like bulletized examples or schema-driven function calls. For conversations, store full history but pass a short summarized state to the model. For RAG workflows, run a relevance filter and pass only top-k passages, or use embedded-based re-ranking to avoid oversized prompts that add both cost and latency.

Implement multi-tier caching: exact-match caches for deterministic inputs, semantic caches for paraphrase detection, and result hashing for structured outputs. Batch similar low-priority jobs to amortize per-request overhead and push non-urgent processing to off-peak batch pipelines. Use cheaper models as deterministic fallbacks for repeated or low-stakes tasks and only invoke premium models when cache misses or classifier signals require higher fidelity.

A single-minded focus on token cost often backfires. Build a cost-control dashboard that correlates input tokens, output tokens, cache hit rate, model route share, latency, task success and quality scores. Monitor user-level quality regressions after routing or prompt compression changes and set guardrails (e.g., minimum success rates per route). Use A/B tests to measure customer-facing impact before rolling out cost-optimizations broadly.

Teams frequently pick the cheapest model for everything, allow unbounded outputs, resend entire histories on each call, and implement poor retry logic that multiplies cost. Avoid these by enforcing max output tokens and stop sequences, summarizing histories, recording exact-match hashes before retries, and building retry budgets that prefer cached or cheaper fallback paths. Documenting and automating these safeguards prevents surprise bill spikes and quality degradation.