Controlling LLM costs with Databricks' own features

Controlling LLM costs on Databricks means picking the right serving option and model size for the workload, then applying platform controls to keep spend predictable. Model Serving lets you scale compute to zero when idle and choose smaller or self-hosted open models instead of always calling expensive external APIs. Cost control is an ongoing monitoring activity, not a one-time setup decision.

1 · Learn the must-know

• 1Databricks Model Serving endpoints for provisioned throughput or classic CPU/GPU serving can autoscale down to zero when there is no traffic, avoiding charges for idle capacity.
• 2Choosing a smaller or open-source foundation model (or a fine-tuned smaller model) for a task instead of a larger general-purpose model reduces per-token inference cost while often meeting quality requirements.
• 3Databricks-hosted Foundation Model APIs (pay-per-token) avoid paying for reserved capacity when usage is low or unpredictable, while provisioned throughput is more cost-effective for steady, high-volume traffic.
• 4System Tables (e.g., billing/usage tables) can be queried to track and attribute Model Serving and workspace costs over time, enabling budget monitoring and alerting.
• 5Batching requests and controlling prompt/response length (e.g., limiting max tokens, trimming context, avoiding unnecessary retrieval context) directly reduces token-based billing costs.
• 6Caching frequent or repeated queries/responses avoids redundant LLM calls and lowers cost for high-traffic applications with repetitive inputs.

2 · Check your understanding