The LLM Cost Optimization Playbook: Cutting Token Spend 60–80% in Production
Model routing, prompt compression, caching, distillation and eval-driven downgrades — the levers we use to bring enterprise LLM bills under control without hurting quality.
Why LLM bills explode after launch
The pattern is familiar: a pilot runs on GPT-class models with generous prompts and no caching. Latency is fine, quality is great, and the bill is small because traffic is small. Launch to production and the same architecture at 100× the volume produces a five- or six-figure monthly invoice — often with output tokens, not input, as the surprise driver. Cost optimization is not premature. Designed in from day one, it usually cuts spend 60–80% with no measurable quality loss.
Lever 1: model routing by task difficulty
Most workloads are a mix of easy and hard requests. Classify each request (heuristic, small classifier, or a cheap LLM call) and route to the smallest model that meets the quality bar. In production we typically route 60–80% of traffic to small / mid-tier models (Haiku, GPT-4o-mini, Gemini Flash, Llama-3.1-8B) and reserve frontier models for the hard tail. This alone often halves the bill.
Lever 2: prompt compression and context discipline
Long system prompts and unfiltered RAG context are the top hidden cost. Trim boilerplate, move stable instructions to prompt caching (Anthropic, OpenAI and Gemini all support it — up to 90% discount on cached input tokens), and cap retrieved chunks aggressively with a re-ranker. Structured output modes (JSON schema, tool calls) shrink output tokens dramatically compared with free-form prose you then parse.
Lever 3: caching at three layers
Response cache for exact or semantically-similar queries (Redis + embedding similarity, typical hit rate 15–40% for support and search workloads). Prompt cache for the stable prefix of every request. Embedding cache so you never re-embed the same chunk twice during ingestion. Combined, these routinely deliver 30–50% cost reduction on top of routing.
Lever 4: distillation and fine-tuning the hot path
Once a workflow is stable and you have logged 10k+ high-quality traces, fine-tune a small open model (Llama-3.1-8B, Mistral, Qwen) on those traces. For narrow tasks — classification, extraction, structured summarisation — a fine-tuned 8B model often matches GPT-4-class quality at 5–20× lower cost and can be self-hosted for predictable pricing. Keep the frontier model as a fallback for low-confidence outputs.
Lever 5: evaluation-gated downgrades
Every cost optimization must ship behind the same eval harness that gates model upgrades. Define an offline eval set with human-labelled ground truth, measure win-rate of the cheaper configuration against the incumbent, and only promote when the delta is within your quality tolerance. Without this discipline, cost work quietly erodes accuracy and nobody notices until customers complain.
What good cost dashboards look like
Instrument cost per request, per feature, per customer segment, and per model — not just total spend. Track input vs output tokens separately (output is 3–5× more expensive on most providers). Alert on cost-per-successful-outcome, not raw spend — a cheaper model that fails more often can be more expensive overall. Teams that ship this dashboard on day one avoid the quarterly panic that drives most of our cost-audit engagements.
Key takeaways
- Route 60–80% of traffic to smaller models; reserve frontier models for the hard tail
- Use prompt caching + aggressive context trimming — output tokens are 3–5× the cost
- Layer response, prompt and embedding caches for 30–50% additional savings
- Fine-tune a small open model on logged traces once a workflow is stable
- Gate every cost change behind the same eval harness you use for upgrades
T7 Research
Enterprise AI Research Group
T7 Research is the research arm of T7 Solution, focused on benchmarking LLMs, evaluating RAG patterns, and compiling implementation playbooks for enterprise technology leaders.