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Hosted Models

Zilliz Cloud can host embedding and reranking models on Zilliz-managed infrastructure. You can deploy dedicated, fully managed model instances and use them directly from Zilliz Cloud for stable and high-performance inference.

With a managed model instance, you can insert raw data into a collection. Zilliz Cloud automatically generates vector embeddings with the deployed model during ingestion. For semantic search, you only provide the raw query text. Zilliz Cloud uses the same model to create a query vector, compares it with stored vectors, and returns the most relevant results.

The following diagram shows the procedures for using hosted models.

Deploy a model

Currently, Zilliz Cloud supports the following regions, instance types, and models.

📘Notes

If you have specific requirements for hosted models, please contact us.

Supported regions

The model deployment region should be consistent with your cluster region. Available options include:

Region	Location
aws-us-east-1	N. Virginia, USA
aws-us-east-2	Ohio, USA
aws-us-west-2	Oregon, USA
aws-ca-central-1	Canada (Central)
aws-eu-central-1	Frankfurt, Germany
aws-ap-northeast-1	Tokyo, Japan
aws-ap-southeast-2	Sydney, Australia

Supported instance type

The instance type determines the available compute resources. Available options include:

Instance Type	Resources
g6.xlarge	1 Nvidia L4 GPU 8 vCPU 32GB RAM

Supported models

Available options include:

Type	Model
Embedding	Qwen/Qwen3-Embedding-0.6B
	Qwen/Qwen3-Embedding-4B
	Qwen/Qwen3-Embedding-8B
	BAAI/bge-small-en-v1.5
	BAAI/bge-small-zh-v1.5
	BAAI/bge-base-en-v1.5
	BAAI/bge-base-zh-v1.5
	BAAI/bge-large-en-v1.5
	BAAI/bge-large-zh-v1.5
Reranking	BAAI/bge-reranker-base
	BAAI/bge-reranker-large
	Qwen/Qwen3-Reranker-0.6B
	Qwen/Qwen3-Reranker-4B
	Qwen/Qwen3-Reranker-8B

Obtain a deployment ID

Using the information you provide, Zilliz will deploy the model for you which takes about 15 minutes. When the deployment is ready, Zilliz Cloud Support will return a deployment ID, which you will use when creating embedding or reranking functions.

"deploymentId": "68f8889be4b01215a275972a"

Use the deployed model in a function

Once you have the deployment ID, you can create collections that use the deployed model through embedding or reranking functions.

Use an embedding function

1. Create a collection with embedding function.

   • Define at least one VARCHAR field for the raw text.

   • Define at least one vector field for the embedding vectors generated by the model.

   • Set the vector field dimension to match the model’s output dimension.

   schema = milvus_client.create_schema()
   schema.add_field("id", DataType.INT64, is_primary=True, auto_id=False)
   schema.add_field("document", DataType.VARCHAR, max_length=9000)
   schema.add_field("dense", DataType.FLOAT_VECTOR, dim=384) # important, the dimension must be supported by the deployed model.
   
   # define embedding function
   text_embedding_function = Function(
       name="zilliz-bge-small-en-v1.5",
       function_type=FunctionType.TEXTEMBEDDING,
       input_field_names=["document"], # Scalar field(s) containing text data to embed
       output_field_names="dense", # Vector field(s) for storing embeddings
       params={
           "provider": "zilliz",
           "model_deployment_id": "...", # Use the model deployment ID we provide you
           "truncation": True, # Optional: if true, inputs greater than the max supported input length of the model will be truncated
           "dimension": "384",                # Optional: Shorten the output vector dimension, only if supported by the model
       }
   )
   
   schema.add_function(text_embedding_function)
   
   index_params = milvus_client.prepare_index_params()
   index_params.add_index(
       field_name="dense",
       index_name="dense_index",
       index_type="AUTOINDEX",
       metric_type="IP",
   )
   
   ret = milvus_client.create_collection(collection_name, schema=schema, index_params=index_params, consistency_level="Strong")

2. Insert raw text data.

   Insert only the raw text into the collection. Zilliz Cloud automatically calls the embedding function and populates the vector field.

   rows = [
           {"id": 1, "document": "Artificial intelligence was founded as an academic discipline in 1956."},
           {"id": 2, "document": "Alan Turing was the first person to conduct substantial research in AI."},
           {"id": 3, "document": "Born in Maida Vale, London, Turing was raised in southern England."},
   ]
   
   insert_result = milvus_client.insert(collection_name, rows, progress_bar=True)
   

3. Conduct a similarity search with raw text data.

   Provide the query as raw text. Zilliz Cloud generates the query vector using the same model and performs the similarity search.

   search_params = {
       "params": {"nprobe": 10},
   }
   queries = ["When was artificial intelligence founded", 
              "Where was Alan Turing born?"]
   
   result = milvus_client.search(collection_name, data=queries, anns_field="dense", search_params=search_params, limit=3, output_fields=["document"], consistency_level="Strong")

Use a reranking function

You can also configure a reranking function that uses the deployed model to rerank search results.

import numpy as np
rng = np.random.default_rng(seed=19530)
vectors_to_search = rng.random((1, dim))

# define reranking function
ranker = Function(
    name="model_rerank_fn",
    input_field_names=["document"],
    function_type=FunctionType.RERANK,
    params={
        "reranker": "model", 
        "provider": "zilliz",
        "model_deployment_id": "...", # Use the model deployment ID we provide you,
        "queries": ["machine learning for time series"] * len(vectors_to_search),  # Query text, the number of query strings must match exactly the number of queries in your search operation
    }
)

# Use it during search
result = milvus_client.search(collection_name, vectors_to_search, limit=3, output_fields=["*"], ranker=ranker)

Billing

Using hosted models only incurs function and model services charges. Because inference runs within Zilliz Cloud, your data does not traverse the public internet—so you will not incur data transfer charges.

For model unit prices by region, please contact sales.

Cost calculation

Function and Model Services Cost = Model Unit Price x Usage Time

• Model Unit Price: For details, contact sales.

• Usage Time: The total time the model deployment is running, measured in hours, regardless of whether the model is actively used.