---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: f4208945290d1959e95e57ac3ad115c8cfadf5b09e8256343af940f19102abe5
  pageDirectory: concepts
  sources:
    - databricks-hosted-foundation-models-available-in-foundation-model-apis-databricks-on-aws.md
  confidence: 0.9
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - pay-per-token-pricing-mode
    - PPM
  citations:
    - file: databricks-hosted-foundation-models-available-in-foundation-model-apis-databricks-on-aws.md
title: Pay-per-token Pricing Mode
description: A consumption-based pricing model for Foundation Model APIs where users are charged per token processed, suitable for experimentation and variable workloads.
tags:
  - pricing
  - llm-serving
  - databricks
timestamp: "2026-06-18T11:38:54.546Z"
---

# Pay-per-token Pricing Mode

**Pay-per-token Pricing Mode** is a consumption-based billing model for [Foundation Model APIs](/concepts/foundation-model-apis.md) in Databricks where you are charged based on the number of tokens processed — both input (prompt) and output (generated) — rather than reserving dedicated compute capacity. This mode is ideal for experimentation, development, and low-to-moderate throughput workloads. ^[databricks-hosted-foundation-models-available-in-foundation-model-apis-databricks-on-aws.md]

## Overview

In pay-per-token mode, you send query requests to model endpoints available in your Databricks workspace and pay only for the tokens consumed. This model eliminates the need to provision or manage underlying infrastructure, and you can scale usage up or down instantly based on demand. ^[databricks-hosted-foundation-models-available-in-foundation-model-apis-databricks-on-aws.md]

Foundation Model APIs offer both pay-per-token and [Provisioned Throughput Foundation Model APIs](/concepts/provisioned-throughput-foundation-model-apis.md) modes. Pay-per-token is suitable for development, prototyping, and variable workloads, while Databricks recommends [Provisioned Throughput](/concepts/provisioned-throughput.md) for production workloads. ^[databricks-hosted-foundation-models-available-in-foundation-model-apis-databricks-on-aws.md]

## Supported Models

Pay-per-token endpoints support a range of state-of-the-art open models, including models from OpenAI (GPT-5 series, GPT-5.4 series), Google (Gemini 3 series, Gemini 2.5 series), Anthropic (Claude Opus 4 series, Claude Sonnet 4 series), Meta (Llama 3, 3.1, 4), Alibaba Cloud (Qwen3.5, Qwen3), and embedding models like GTE Large and BGE Large. For the complete list of endpoint names, see the pay-per-token supported models table in the Foundation Model APIs documentation. ^[databricks-hosted-foundation-models-available-in-foundation-model-apis-databricks-on-aws.md]

You can interact with supported models using the [AI Playground](/concepts/ai-playground.md) in your Databricks workspace. ^[databricks-hosted-foundation-models-available-in-foundation-model-apis-databricks-on-aws.md]

## Token Pricing Details

Pricing varies by model and is based on the number of tokens processed. Tokens are counted for both the input prompt and the output generated by the model. Each model's pricing tier reflects its computational cost — larger, more capable models typically have higher per-token rates than smaller or more efficient models. ^[databricks-hosted-foundation-models-available-in-foundation-model-apis-databricks-on-aws.md]

### Context Windows and Output Limits

Pay-per-token models have varying context windows and maximum output token limits. For example:

- OpenAI GPT-5.5 Pro: 400K total token context window, 128K maximum output tokens
- Google Gemini 3.5 Flash: unspecified context window but supports multimodal inputs (text, image, video, audio)
- Anthropic Claude Haiku 4.5: optimized for real-time, low-latency applications

These limits affect the total number of tokens you are billed for per request. ^[databricks-hosted-foundation-models-available-in-foundation-model-apis-databricks-on-aws.md]

## When to Use Pay-per-Token

Pay-per-token mode is best suited for:

- Development and prototyping of AI applications
- Intermittent or variable workloads where usage is unpredictable
- Evaluating different models before committing to a specific architecture
- Low-to-moderate throughput production workloads where cost predictability is less critical

For high-volume or latency-sensitive production deployments, Databricks recommends [Provisioned Throughput Foundation Model APIs](/concepts/provisioned-throughput-foundation-model-apis.md), which supports all model architectures available in pay-per-token mode, including fine-tuned and custom pre-trained models. ^[databricks-hosted-foundation-models-available-in-foundation-model-apis-databricks-on-aws.md]

## Using Pay-per-Token Endpoints

Send query requests to pay-per-token endpoints using the Foundation Model REST API or the MLflow Deployments SDK. See [Use Foundation Models](/concepts/foundation-model-apis.md) for detailed guidance on scoring foundation models. ^[databricks-hosted-foundation-models-available-in-foundation-model-apis-databricks-on-aws.md]

## Related Concepts

- [Provisioned Throughput Foundation Model APIs](/concepts/provisioned-throughput-foundation-model-apis.md) — Reserved-capacity mode recommended for production workloads
- [Foundation Model REST API Reference](/concepts/foundation-model-apis.md) — API details for interacting with model endpoints
- [AI Playground](/concepts/ai-playground.md) — Interactive interface for testing supported models
- [Token Pricing and Billing](/concepts/pay-per-token-pricing-databricks.md) — General Databricks billing concepts (suggested)
- [Model Serving](/concepts/model-serving.md) — Broader model deployment options in Databricks (suggested)

## Sources

- databricks-hosted-foundation-models-available-in-foundation-model-apis-databricks-on-aws.md

# Citations

1. [databricks-hosted-foundation-models-available-in-foundation-model-apis-databricks-on-aws.md](/references/databricks-hosted-foundation-models-available-in-foundation-model-apis-databricks-on-aws-8de78726.md)
