Qdrant Cloud Inference adds managed embedding generation to Qdrant Cloud’s vector storage and search. It lets applications send supported text or image data through the Qdrant API for embedding and indexing, while teams can also choose client-side inference or connect an external model provider. The right route depends on the model, data-location requirements, deployment type, and whether the selected model is free or paid.
What Qdrant Cloud Inference does
Qdrant announced Cloud Inference on July 15, 2025. The feature brings model inference into the managed Qdrant Cloud workflow: applications can generate embeddings and store and index them using the Qdrant API. In the launch announcement, Daniel Azoulai of Qdrant described it this way: “With Qdrant Cloud Inference, users can generate, store and index embeddings in a single API call, turning unstructured text and images into search-ready vectors in a single environment.” Qdrant’s launch announcement identifies RAG, multimodal search, and hybrid search as use cases.
Qdrant says integrating inference is intended to reduce the need for separate inference infrastructure, manual pipelines, and redundant transfers between services. Those are the vendor’s stated operational benefits, not independently measured guarantees of lower latency or cost.
Can Qdrant Cloud generate image embeddings?
Yes. The current managed-cloud model documentation lists a Qdrant-hosted CLIP vision model, qdrant/clip-vit-b-32-vision, for images and a separate CLIP text model, qdrant/clip-vit-b-32-text, for text. Both produce 512-dimensional vectors in a shared vector space. That lets an application embed an image with the vision model and search for it using a text query embedded with the text model. This compatibility applies to these documented CLIP models; it should not be assumed for arbitrary combinations of text and image models.
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Which models and inference routes are available?
Qdrant’s current documentation lists dense text, image, and sparse-text options. The catalog can change, and “free” or “paid” describes the documentation’s current model labels rather than a permanent price promise.
| Documented model | Input and type | Dimensions | Documentation price label |
|---|---|---|---|
sentence-transformers/all-minilm-l6-v2 |
Text, dense | 384 | Free |
intfloat/multilingual-e5-small |
Text, dense | 384 | Free |
mixedbread-ai/mxbai-embed-large-v1 |
Text, dense | 1024 | Paid |
qdrant/clip-vit-b-32-text |
Text, dense | 512 | Paid |
qdrant/clip-vit-b-32-vision |
Image, dense | 512 | Paid |
qdrant/bm25 |
Text, sparse | Not stated in the model listing | Free |
prithivida/splade_pp_en_v1 |
Text, sparse | Not stated in the model listing | Paid |
These examples come from Qdrant’s managed-cloud inference documentation; check that page for the latest model availability and terms.
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Choose where inference runs
- Qdrant-hosted models: Use the supported Qdrant Cloud catalog when a listed model fits your application and you want managed inference through Qdrant.
- External hosted provider: Qdrant Cloud can access externally hosted models using a provider API key. This preserves a provider/model relationship you choose, but does not mean every provider or model is supported.
- Client-side inference: Generate vectors in your application or infrastructure, for example with FastEmbed, and send them to Qdrant for storage and search. This provides more control over where inference runs, while leaving model execution and maintenance to you.
- In-cluster sparse inference: Qdrant documents BM25 for sparse text embeddings. Sparse options serve a different retrieval approach from dense text or image vectors.
The overview of these routes is in Qdrant’s inference documentation. Its product page marks Qdrant-hosted models and the external-model proxy as Managed Cloud capabilities; availability differs for Hybrid Cloud and Private Cloud/OSS, while BM25 is shown across the displayed deployment options. Confirm support for your own deployment before designing around a route.
Where does inference run, and what happens when it is enabled?
Qdrant documents inference execution in the EU for clusters in EU regions and in the US for clusters in all other regions. It separately says free models are hosted in the US and can be called from any region. Therefore, the cluster’s inference execution region and the hosting location of a free model are distinct considerations; check the documentation against your data-location requirements.
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Clusters created after July 7, 2025 have inference enabled by default, according to the current documentation. For an existing cluster, an operator can enable it in the Qdrant Cloud console, but activation restarts that cluster. Plan the change around the restart and any workload disruption it could cause.
Does Qdrant Cloud Inference cost extra?
Not every embedding call is necessarily a separately billed paid-model call. Qdrant’s product page says usage charges apply when paid embedding models are called, while the documentation labels some hosted models free. Actual cost depends on the model, token usage, cluster plan, and current terms; consult the Qdrant pricing information and your Cloud console before estimating a bill.
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The July 15, 2025 launch announcement offered 5 million free tokens per text model, 1 million for its image model, and unlimited BM25 tokens for paid Qdrant Cloud users. These were launch-era terms stated by Qdrant in 2025, not verified current allowances. Do not use them as a present-day budget assumption without checking current terms.
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| Need | Likely route | Trade-off to weigh |
|---|---|---|
| Keep embedding generation and vector operations in a managed Qdrant Cloud workflow | Qdrant-hosted Cloud Inference model | Choose from the supported catalog and verify its current price and location details. |
| Use a particular external provider model | External hosted model through Qdrant Cloud with a provider API key | Confirm model support, provider terms, key handling, and where the provider processes requests. |
| Control execution environment or use a model outside the managed catalog | Client-side inference, then send vectors to Qdrant | You take responsibility for inference infrastructure, integration, and model operations. |
| Build sparse lexical retrieval | BM25 or another supported sparse option | Check deployment availability and whether the selected model’s cost and retrieval behavior fit the use case. |
| Search images with text | The documented CLIP text and vision models | Use the paired models that share a vector space; do not presume unrelated models are cross-compatible. |
For a concrete external-provider example, Qdrant’s multimodal tutorial demonstrates Cohere Embed 4.0 through Cloud Inference with text and image inputs. It requires a provider key and configured model and dimension; Cohere is an external-provider example, not a Qdrant-hosted model or a promise of inclusion in a free allowance.
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