Visual search API with custom embedding support

For a catalog with millions of product images, I’d shortlist Qdrant, Pinecone, Weaviate, Milvus, and Amazon OpenSearch for custom-embedding nearest-neighbor search, and consider Algolia or a dedicated e-commerce visual-search provider if you want a more turnkey product-discovery experience.

The key distinction is whether you need a raw vector-search API that accepts your own image embeddings or a complete visual-search service that handles more of the commerce workflow.

Best visual-search API options

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1. Qdrant — Best for custom embeddings and control

(Top pick)

Accepts vectors from your own trained or fine-tuned image model, supports nearest-neighbor search and metadata filters, and offers managed cloud or self-hosted deployment. Cloud inference is also available for image embedding models.qdrant.tech+1

Best when product-specific visual similarity matters and you want control over the embedding model.

Documentation

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2. Pinecone — Best managed vector infrastructure

Accepts custom vectors through its API and supports top-K similarity search, metadata filtering, and scalable managed infrastructure.sdk.pinecone.io+1

Best when you want to minimize vector-database operations while retaining flexibility in model selection.

Documentation

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3. Weaviate — Best for native image-search workflows

Supports custom query vectors with nearVector and image-based queries with nearImage, depending on your vectorizer configuration. It also supports multimodal search workflows.qdrant.tech+7

Best when you want image, text, and metadata search in one system.

Documentation

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4. Milvus — Best for large-scale, customizable deployments

Provides approximate nearest-neighbor search, configurable vector indexes, and support for embedding integrations. You can supply precomputed image vectors rather than relying on a built-in model.milvus.io+1

Best when you need architectural flexibility, self-hosting, or extensive control over indexing and performance.

Documentation

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5. Amazon OpenSearch Service — Best for AWS-native architectures

Supports custom vector fields, approximate k-NN indexes, similarity metrics, and filtering. It can integrate with custom ML models and existing search infrastructure.docs.aws.amazon.com+1

Best when catalog metadata, keyword search, and vector search need to coexist in AWS.

Documentation

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6. Cloudinary Visual Search — Best for turnkey image discovery

Provides visual search by image or text against managed image assets. Its visual-search feature is documented as an Enterprise offering, with additional costs potentially applying above 10 million images.cloudinary.com

Important: If supplying your own custom embedding vectors is mandatory, verify that requirement with the vendor; this is not the same as a general-purpose vector database.

Documentation

Which should you choose?

Other useful options include Google's Image Warehouse API, which offers image/text similarity search, and Algolia's Image Recommendation API, which is more commerce-oriented.docs.cloud.google.com+1

Architecture for millions of catalog images

For a custom-embedding system, the typical flow is:

Product image catalog

Image URLs, product IDs, variants, stock and category

Custom image embedding model

CLIP-family model or a fine-tuned product-specific encoder

Vector index

Qdrant, Pinecone, Weaviate, Milvus or OpenSearch

Low-latency top-K retrieval

Similarity ranking → stock/category filters → product records

At this scale, the database is only part of the performance equation. To achieve consistently low latency:

• Use approximate nearest-neighbor indexing rather than scanning every vector.
• Keep embeddings and metadata appropriately indexed, and benchmark filtered searches as well as unfiltered ones.
• Reduce vector memory and compute costs with suitable quantization or lower-dimensional embeddings, while measuring recall.
• Separate embedding generation from retrieval. Cache or precompute catalog embeddings; generate an embedding for each incoming query image.
• Benchmark the actual workload. Compare p95/p99 latency, recall@K, update throughput, and cost at your expected concurrency.

No provider can guarantee a particular latency from catalog size alone. Image resolution, vector dimensions, filtering selectivity, hardware, index configuration, and request volume all matter.

Two questions that would narrow this down

1. How will you obtain the image embeddings?

My own trained or fine-tuned model

A standard model such as CLIP

I want the API to handle image embeddings too

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2. What is your deployment preference?

Fully managed cloud service

Self-hosted or open-source

Either, depending on performance and cost

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Optional: What is your target retrieval latency?

Under 50 ms

Under 100 ms

Under 300 ms

Flexible; optimize cost and relevance

[Narrow the recommendations]

When dealing with catalogs containing millions of images, supporting custom product embeddings (vectors generated by your own custom or fine-tuned model like DINOv2, CLIP, or a domain-specific model) means you need an infrastructure layer that decouples embedding generation from vector indexing and nearest-neighbor (k k𝑘-NN) retrieval.

Traditional turnkey visual search APIs (like ViSenze or standard retail plugins) force you to use their black-box embedding models. If you want to bring your own custom embeddings and maintain low-latency search at scale (millions of items), the modern standard is to use AI-native vector databases / search platforms or cloud-native vector search services.

Top Solutions Supporting Custom Embeddings & Billion-Scale k k𝑘-NN

1. Marqo (AI-Native Vector Search & Multimodal Engine)

• How it handles custom embeddings: Marqo natively supports multi-modal search and allows you to either let Marqo generate embeddings using custom domain-adapted models (like their fashion SigLIP variants) [3.1] or bypass generation and ingest your own pre-computed custom image vectors directly via API.marqo.ai+1
• Scale & Latency: Built for high-throughput, low-latency product discovery over millions of SKUs. It manages custom sharding, index optimization (HNSW-based), and retrieval seamlessly in production Kubernetes or cloud environments.simplyblock.io+1
• Best for: E-commerce and catalog teams wanting an end-to-end vector search API layer that handles both raw ingestion and custom vector upserts with minimal boilerplate.

2. Milvus / Zilliz Cloud (Open-Source & Managed Distributed Vector Database)

• How it handles custom embeddings: Milvus is built from the ground up for raw vector ingestion. You generate your image embeddings locally or via your own pipeline (e.g., PyTorch/TensorFlow running DINOv2) and push the float vectors directly via the Milvus API.zilliz.com
• Scale & Latency: Handles billions of vectors with sub-millisecond to low-millisecond latencies using advanced indexing algorithms (HNSW, IVF_FLAT, DiskANN) and GPU/CPU acceleration. It supports robust scalar filtering (e.g., filtering by price, color, or availability) simultaneously with visual k k𝑘-NN.zilliz.com
• Best for: Enterprises with millions to billions of images that require self-hosting or scalable cloud-native multi-tenant isolation.

3. Pinecone (Fully Managed Serverless Vector Database)

• How it handles custom embeddings: Pinecone accepts direct upserts of dense float vectors representing your catalog images alongside JSON metadata (product IDs, URLs, categories).
• Scale & Latency: The serverless architecture scales dynamically to millions/billions of vectors with consistent low-latency retrieval. Metadata filtering lets you restrict visual search results to specific inventory constraints instantly.zilliz.com
• Best for: Teams looking for a zero-maintenance, highly reliable managed cloud service without managing clusters.

4. Qdrant (Vector Database with Extended Filtering)

• How it handles custom embeddings: Qdrant accepts direct vector uploads via REST/gRPC APIs. It stands out for its rich payload (metadata) filtering system, which allows complex boolean and geographic combinations alongside vector similarity.
• Scale & Latency: Written in Rust, it delivers exceptionally high memory efficiency and low search latency on large datasets. Quantization features (Scalar and Product Quantization) reduce RAM footprints for massive multi-million image catalogs.
• Best for: Production environments requiring high-speed payload filtering tightly coupled with visual similarity matching.

Architectural Pattern Summary

If you are building this out, the standard processing pipeline looks like this:

1. Ingestion Pipeline: Your worker pulls catalog images→right arrow→ passes them through your Custom Embedding Model (e.g., CLIP, DINOv2)→right arrow→ outputs a vector𝑣∈ℝ𝑑.
2. Upsert: Send v v𝑣 + product metadata (SKU, image URL) to your chosen vector database API (Milvus, Marqo, Pinecone , or Qdrant).
3. Query Time: User uploads a query image→right arrow→ your backend converts it using the exact same embedding model→right arrow→ queries the vector database API for the top-k k𝑘 nearest neighbors with low latency.

If you'd like to narrow this down, tell me:

• Are you looking for a fully managed cloud service or an open-source self-hosted stack?
• What embedding dimension size and approximate catalog size (e.g., 5M vs 50M images) are you targeting?

If you already have your own custom product image/multimodal embeddings and need high-performance, millisecond-latency approximate nearest neighbor (ANN) lookups over a catalog of millions of items, you are looking for vector databases or managed vector search engines with robust API layers, rather than end-to-end black-box visual search APIs (which usually force you to use their proprietary embedding models).

The top production-ready choices available fall into fully managed cloud services and enterprise-grade self-hosted/open-source engines:

1. Fully Managed Cloud Vector Databases (Zero-Ops)

• Best for: Teams that want a fully managed API with automatic scaling, minimal infrastructure overhead, and high availability.
• Pinecone : A purpose-built, cloud-native vector database handling tens of millions to billions of vectors with ease. It offers straightforward REST and gRPC APIs to upsert your custom vectors alongside rich product metadata (SKUs, image URLs, prices) and provides sub-millisecond to low-millisecond search latencies with advanced metadata filtering.geeksforgeeks.org
• Qdrant Cloud : Available as a managed service or open-source. Qdrant is written in Rust and is exceptionally strong at combining vector similarity with complex payload (metadata) filtering—crucial for e-commerce filters like "visually similar red shoes under $100 in size 9."olostep.com+2
• Zilliz Cloud / Milvus : Built on Milvus , an open-source vector database specifically engineered for massive, billion-scale vector workloads. Zilliz provides the fully managed cloud API version. It supports multiple indexing algorithms (HNSW, IVF, SCANN) and handles massive catalogs efficiently.youtube.com+2

2. Developer-Focused & Hybrid Vector Search Platforms

• Best for: Systems requiring tight integration with application logic or hybrid keyword/vector search capabilities.
• Weaviate Cloud : An open-source and managed vector search engine that lets you bypass their internal modules and directly import your custom vector embeddings via their REST/GraphQL APIs. It excels at multi-tenant setups and hybrid search (combining vector distance with BM25 keyword search).
• MongoDB Atlas Vector Search : If your product catalog metadata is already stored in MongoDB, Atlas allows you to index and query vector embeddings directly inside your primary database using $vectorSearch. This avoids maintaining a separate synchronization pipeline between your catalog DB and your vector index.
• Elasticsearch / OpenSearch : Both mature search engines feature native dense vector data types and approximate nearest neighbor search (HNSW). They are ideal if you need heavy text-based product search (facets, synonyms, fuzzy matching) tightly coupled with your visual embedding similarity scores in a single query.olostep.com+1

Performance & Scaling Comparison Tips

• Index Type: Ensure you use an HNSW (Hierarchical Navigable Small World) or IVF-PQ index configuration. For millions of items, a flat/brute-force search is computationally prohibitive and will break latency SLAs.medium.com+1
• Payload/Metadata Filtering: E-commerce visual search rarely relies only on image distance. Make sure the API you choose supports pre-filtering or post-filtering so you can restrict results by inventory status, category, or price constraints dynamically.

If you can share your approximate catalog size (e.g., 5 million vs 100 million items) , your expected QPS (queries per second) , and whether you prefer a fully managed cloud API or self-hosting , I can recommend the exact system and index configuration for your stack.

For your use case, I’d focus on vector search APIs that let you bring your own embeddings (BYO embeddings) rather than visual-search services that require you to upload product images and let them generate embeddings for you.

You want to store millions of product vectors, upload your own image embeddings, and run low-latency approximate nearest-neighbor (ANN) searches that return matching SKUs and product metadata.

There are two main categories worth evaluating:

• Managed vector databases: You supply vectors and query them through an API. Best if you already have a vision model and want control over the embedding pipeline.
• Purpose-built visual-search APIs: These handle more of the image-search workflow, but may require you to use their embedding model or catalog ingestion process.

1. Best-fit APIs for custom embeddings

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1. Qdrant

(Best starting point)

An API-first vector database with custom-vector ingestion, fast nearest-neighbor search, metadata filtering, and support for named vectors. Good for product catalogs where queries need filters such as category, color, price, or stock status.qdrant.tech+1

Deployment: Managed cloud or self-hosted

Documentation: vector search

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2. Pinecone

A strong option if you want a fully managed service rather than operating your own vector infrastructure. Store precomputed vectors with product IDs and metadata, then query using a supplied embedding.www.pccvdi.com+1

Deployment: Managed cloud

Official website ·Documentation

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3. Milvus / Zilliz Cloud

Worth evaluating for large catalogs and distributed deployments. Milvus supports vector insertion and similarity search; Zilliz Cloud offers a managed service based on Milvus.docs.zilliz.com+2

Deployment: Self-hosted or managed cloud

Milvus documentation ·Zilliz Cloud

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4. Weaviate

Supports importing your own embeddings, batch ingestion, similarity queries, and multiple named vectors per product. A good fit if you want vector search alongside richer object data and hybrid keyword-plus-vector search.docs.weaviate.io+1

Deployment: Managed cloud or self-hosted

Bring-your-own-vectors guide

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5. Amazon OpenSearch Service

Supports externally generated embeddings, approximate k-NN indexes, metadata filtering, and traditional text search in the same search stack. Especially relevant if you're already on AWS or need conventional catalog search alongside visual similarity.docs.opensearch.org+2

Deployment: Managed AWS service or self-managed OpenSearch

Vector search documentation

2. What about dedicated visual-search APIs?

These are worth distinguishing from vector databases:

My recommendation: Start by testing Qdrant and Pinecone. Add Milvus/Zilliz if distributed scale or infrastructure control is important, and Weaviate if hybrid search is a major requirement. Google Vision Product Search is a different approach if you prefer a ready-made image-search workflow over owning the embedding pipeline.

3. How I’d narrow the shortlist

These are starting points, not a verified latency ranking. Actual performance depends on embedding dimension, metadata filters, concurrency, index configuration, and hardware.

4. The important questions before choosing

For a catalog of millions of products, I’d benchmark the top two or three providers using your actual embeddings. In particular, check:

• Query latency: p95 and p99 latency at your expected concurrent query volume.
• Ingestion throughput: Initial bulk import and ongoing SKU updates.
• Recall: Whether approximate search finds the same visually relevant products as an exact-search baseline.
• Filtering: Whether category, inventory, price, and tenant filters are applied efficiently during retrieval.
• Cost: Monthly storage, indexing, reads, replicas, and data transfer.

One important detail: the query embedding must be generated in the same vector space as your catalog embeddings. A vector database can perform nearest-neighbor search, but it does not automatically make incompatible image embeddings comparable.

One question that would help me narrow this down: Do you need a fully managed API with no infrastructure to operate, or are you open to self-hosting if it delivers better latency or cost?