Qdrant Cloud Inference
Qdrant Cloud Inference is a managed service that integrates embedding generation directly into the vector database. Instead of generating vectors using an external API (like OpenAI) and then sending them to Qdrant, you simply send raw text or images to Qdrant Cloud. The platform handles the vectorization internally using hosted models, significantly simplifying the AI technology stack and reducing latency.
Traditionally, Vector Search requires a three-step process:
1. Send data to an Embedding Provider (e.g., OpenAI, Cohere).
2. Receive the vector back.
3. Send the vector to the Database.
Qdrant Cloud Inference collapses this into a single step. You send the raw data object to Qdrant, and the cluster performs the inference (vector creation) and indexing locally within the same network. This “In-Database Embedding” approach removes the need for separate ETL pipelines or external inference servers, effectively turning Qdrant into an all-in-one search backend.
Key Features
-
In-Cluster Processing: Embeddings are generated inside the same cloud cluster where your data is stored. This eliminates the network latency (ping) associated with calling external APIs.
-
Multimodal Support: It natively supports both Text and Image embeddings. You can search for images using text descriptions (and vice versa) without setting up complex multimodal pipelines.
-
Hybrid Search Ready: It supports Dense Vectors (semantic meaning) and Sparse Vectors (BM25/SPLADE keywords) simultaneously. The inference engine can generate both vector types from a single text input automatically.
-
Unified API: Developers use a single SDK method (
client.add()) to upload raw text. The database handles the complexity of chunking and vectorizing. -
Zero-Setup Models: It comes with pre-tuned, high-performance open-source models (like BERT-based transformers and CLIP) ready to use instantly.
Ideal For & Use Cases
-
Real-Time Search: Applications where milliseconds matter (e.g., e-commerce search bars, customer support bots) benefit from removing the external API round-trip.
-
Multimodal Apps: Developers building “Search by Image” features for retail or digital asset management systems who don’t want to manage heavy CLIP models themselves.
-
Simplified RAG Pipelines: Teams looking to reduce code complexity. You don’t need to manage API keys for OpenAI or write retry logic for embedding failures; the database handles it all.
-
Privacy-Conscious AI: Since the inference happens inside your dedicated Qdrant instance (on AWS/GCP/Azure), your data doesn’t leave the cluster to go to a third-party API like OpenAI.
Deployment & Technical Specs (Supported Models)
| Category | Model Name | Description | Dimensions |
| Fast Text (Dense) | all-MiniLM-L6-v2 |
Extremely fast, lightweight model good for general English tasks. | 384 |
| High Quality (Dense) | mxbai-embed-large-v1 |
State-of-the-art open-source model, rivaling commercial APIs in quality. | 1024 |
| Multilingual (Dense) | multilingual-e5-large |
Supports 100+ languages, ideal for global applications. | 1024 |
| Sparse (Keywords) | Pruned BERT (SPLADE) |
Generates sparse vectors for keyword matching (better than BM25). | Dynamic |
| Image/Multimodal | CLIP-ViT-B-32 |
Embeds images and text into the same space for cross-modal search. | 512 |
Pricing & Plans
Qdrant Cloud Inference uses a Usage-Based pricing model (per 1 million tokens), similar to OpenAI, but generally more cost-effective for high-volume internal traffic.
| Tier | Cost | Allowance / Notes |
| Included Free | $0 / month |
• 5 Million text tokens free per month. • 1 Million image tokens free per month. • Unlimited BM25 (Sparse) generation. |
| Overage (Text) | ~$0.10 | Per 1 Million tokens (approx. 700k words). |
| Overage (Image) | ~$1.00 | Per 1,000 images processed. |
| Enablement | Standard/Enterprise | Available only on Paid Qdrant Cloud clusters (not available on the Sandbox Free Tier). |
Pros & Cons
| Pros (Advantages) | Cons (Limitations) |
| Lower Latency: Eliminates the HTTP round-trip time to external embedding providers. | Model Selection: You are limited to the curated list of models Qdrant supports (approx 6-10 models), unlike HuggingFace which has thousands. |
Simplified Stack: No need to maintain a separate Python/Docker service just to run sentence-transformers. |
Paid Clusters Only: You cannot use this feature on the “Free Forever” sandbox tier; you need a standard paid cluster. |
| Cost Transparency: Embedding costs appear on the same bill as your database storage, making budgeting easier. | Hardware Limits: extremely high throughput (thousands of docs/sec) might require scaling your cluster size up to handle the CPU load of inference. |
| Data Privacy: Data stays within your Qdrant VPC; it is not sent to third-party API aggregators. |
Final Verdict: Qdrant Cloud Inference
Qdrant Cloud Inference is a feature that transforms Qdrant from a “passive storage bucket” into an “active search engine.” It is one of the most compelling reasons to choose Qdrant Cloud over self-hosting.
For 80% of use cases, the pre-selected models (like MiniLM and CLIP) are more than sufficient. The ability to simply “upload text” and have the database handle the vectorization magic is a massive productivity booster for developers. While advanced teams needing proprietary or niche models might still need external inference, for the vast majority of RAG and Search applications, this feature simplifies the architecture and lowers costs significantly.