> Strategies to cut your LLM bill with Modelux. # Cost optimization LLM bills scale with usage. Modelux gives you three levers to cut costs without code changes: smaller models for simpler traffic, ensembles of cheap models, and budget enforcement. ## Lever 1: Right-size the model Most teams over-provision. GPT-4o isn't needed for a classification task that GPT-4o-mini handles fine. Use a **cost-optimized** routing config: ```json { "strategy": "cost_optimized", "quality_tier": "standard", "allowed_models": [ "gpt-4o-mini", "claude-haiku-4-5", "gemini-2.5-flash" ] } ``` Modelux picks the cheapest allowed model that meets the quality tier. You'll see typical savings of 50-70% vs. using a frontier model for everything. ## Lever 2: Ensembles For quality-critical tasks where you'd otherwise reach for a frontier model, try a 3-model ensemble of cheap models. Voting across multiple cheap models often matches frontier-model quality at 20% of the cost. See [Ensembles](/docs/concepts/ensembles) for full configuration details. ## Lever 3: Budget caps with auto-downgrade Set a monthly budget with auto-downgrade: - Cap: $500/month on your production project - At 80%: email alert - At 100%: automatically downgrade all traffic to a cheaper routing config Your app keeps serving requests — just at a lower cost — until the budget resets next month. ## Lever 4: Caching Enable exact-match caching on routing configs where prompts repeat. Cache hits cost $0. Even 10% cache hit rate on a high-volume endpoint pays for Modelux many times over. ## Measure the savings Go to **Analytics -> Period comparison**. Set the baseline to the month before you enabled cost optimization. You'll see period-over-period cost and volume diffs side-by-side. ## A typical result A team spending $10k/month on GPT-4o: - Adds a cost-optimized config routing 60% of traffic to `gpt-4o-mini` + `claude-haiku-4-5` - Keeps 40% on `gpt-4o` for complex queries - New monthly bill: ~$5,200 - Modelux cost: $199/month (Team tier) - **Net savings: ~$4,600/month**