---
title: Inference
short_description: "Generate vector embeddings inside Qdrant — server-side inference removes the need to run separate embedding infrastructure."
description: "Use Qdrant's built-in inference to generate text, image, and multimodal embeddings server-side — no separate embedding stack required."
weight: 220
partition: develop
aliases:
  - ../inference
  - /documentation/concepts/inference/
---

# Inference

![Inference generates vectors embeddings from documents or images](/docs/inference.png)

Inference is the process of using a machine learning model to create vector embeddings from text, images, or other data types. While you can create embeddings on the client side, you can also use Qdrant's [Inference API](/documentation/inference/inference-api/) to generate them server-side using a single, unified API across sparse, managed, and externally hosted models.

## Inference Options

Where and how you generate embeddings depends on the model you want to use and whether you prefer to manage your own embedding infrastructure:

- **Client-side inference**: Manage your own inference pipeline locally, for example, using Qdrant's Python [FastEmbed](/documentation/fastembed/) library. This provides full control over the model and its configuration without external network calls and is ideal when you prefer to manage the embedding infrastructure yourself.
- **Qdrant Cluster (BM25)**: Generate sparse embeddings using the [BM25 model](/documentation/inference/inference-bm25/) directly within the Qdrant cluster. This keeps the embedding logic close to the data, eliminating the need for a separate inference service for keyword-based retrieval.
- **Qdrant Cloud Inference**: Managed deployments on Qdrant Cloud have access to [Cloud Inference](/documentation/inference/cloud-inference/). Qdrant Cloud hosts a range of embedding models, some for free, allowing you to generate embeddings without managing the infrastructure.
- **Externally Hosted Models (Qdrant Cloud)**: Access embedding models hosted by [third-party embedding model providers](/documentation/inference/external-inference-providers/) (OpenAI, Cohere, Jina AI, and OpenRouter). Use a wide range of state-of-the-art models through a single, unified Qdrant API, without the need to manage a separate embedding pipeline.

## Choose Your Approach

The right option depends on your deployment and what you need to embed. Use this table to find the best fit for your use case.

| If you… | Use… |
|---|---|
| Need sparse BM25 embeddings | [Qdrant Cluster](/documentation/inference/inference-bm25/) |
| Already manage your own inference service | Client-side inference |
| Self-host Qdrant | Client-side inference, for example using [FastEmbed](/documentation/fastembed/) |
| Use Qdrant Cloud and want to use one of the supported embedding models | [Qdrant Cloud Inference](/documentation/inference/cloud-inference/) |
| Use Qdrant Cloud and want to use a model from OpenAI, Cohere, Jina AI, or OpenRouter | [Qdrant Cloud Inference with an external provider](/documentation/inference/external-inference-providers/) (requires API key) |