Which visual search APIs support custom product embeddings and low-latency nearest-neighbor results for catalogs with millions of images?

Several specialized multimodal vector search platforms and managed vector databases support custom product embeddings and low-latency approximate nearest-neighbor (ANN) search for catalogs scaling into millions of items .

Traditional turnkey retail visual search APIs (such as the legacy Google Vision API Product Search) have largely shifted into maintenance mode or require modern component architectures. Today's architectures rely on pairing custom embedding models (like CLIP or DINOv2) with high-performance vector engines.

Top Visual Search and Vector Platforms

Architectural Best Practices for Million-Scale Catalogs

• Embedding Generation: Extract dense features offline or via real-time inference using vision transformers (e.g., CLIP, DINOv2) optimized for visual attributes like color, shape, and texture.
• Index Configuration: Use Approximate Nearest Neighbor algorithms like HNSW (Hierarchical Navigable Small World) for ultra-low latency or IVF (Inverted File) variants to balance memory footprint and recall rate for millions of items.
• Metadata Filtering: Store critical filters (price, inventory status, category IDs) alongside vectors to ensure out-of-stock or irrelevant items are pruned during the nearest-neighbor search phase.

If you can share your preferred cloud infrastructure (AWS, GCP, self-hosted) and approximate catalog size , I can help recommend the most cost-effective deployment pattern.

For a catalog with millions of product images, the key distinction is whether you need a managed visual-product-search API or a vector search engine where you control the embeddings.

The strongest fits for your requirement

Azure AI Search is particularly interesting if you want a managed search API rather than operating a vector database. You can supply your own vectors, create vector fields, and perform nearest-neighbor queries; it supports HNSW and filtering, and it can combine vector and keyword search. Microsoft also explicitly documents multimodal/image embeddings and CLIP-style image/text retrieval.

Pinecone, Qdrant, Weaviate, and Milvus/Zilliz are generally the more natural choices if the architecture is:

product image → your embedding model → vector DB → top-K nearest products → metadata/reranking

That gives you control over the embedding model—e.g. a fine-tuned CLIP/SigLIP-style model trained specifically on your catalog—and lets the vector index handle ANN retrieval. For millions of images, this architecture is usually preferable when embedding quality specific to your catalog matters more than having a turnkey "product search" model.

Google Cloud Vision API Product Search is different. It is explicitly designed for retail product matching: you create products and reference images, put them into product sets, and query with an image to get visually/semantically similar products. Google Cloud Documentation Google Cloud Documentation However, it doesn't give you the same arbitrary-vector interface as a vector database—you aren't uploading your own embedding vectors and asking for cosine/inner-product nearest neighbors. Its product index is also updated approximately daily, which matters if your catalog changes frequently.

What I'd choose architecturally

If "custom product embeddings" is a hard requirement, I'd narrow the shortlist to:

1. Pinecone — managed, straightforward production vector retrieval.
2. Qdrant Cloud — strong filtering and vector-search control.
3. Weaviate Cloud — useful if you want vector + richer search/data-model capabilities.
4. Milvus/Zilliz — particularly attractive for very large-scale vector infrastructure.
5. Azure AI Search — compelling when you're already heavily invested in Azure and want conventional search + vector + multimodal retrieval together.

For millions rather than merely thousands of images, I'd benchmark P95/P99 query latency, recall@K, filtered-search latency, index-build/update time, and cost per million vectors rather than relying on advertised "low latency." The embedding dimensionality (512 vs. 768 vs. 1536, etc.) and whether you need metadata filtering can make a substantial difference.

If you tell me your approximate catalog size (e.g. 5M/50M), embedding dimension, target P99 latency, and whether embeddings are CLIP/SigLIP/custom, I can narrow this to 2–3 architectures and compare their expected tradeoffs.

Pinecone, Qdrant, Weaviate, and Milvus/Zilliz are generally the more natural choices if the architecture is:

product image → your embedding model → vector DB → top-K nearest products → metadata/reranking

That gives you control over the embedding model—e.g. a fine-tuned CLIP/SigLIP-style model trained specifically on your catalog—and lets the vector index handle ANN retrieval. For millions of images, this architecture is usually preferable when embedding quality specific to your catalog matters more than having a turnkey "product search" model.

Google Cloud Vision API Product Search is different. It is explicitly designed for retail product matching: you create products and reference images, put them into product sets, and query with an image to get visually/semantically similar products. docs.cloud.google.com docs.cloud.google.com However, it doesn't give you the same arbitrary-vector interface as a vector database—you aren't uploading your own embedding vectors and asking for cosine/inner-product nearest neighbors. Its product index is also updated approximately daily, which matters if your catalog changes frequently.

When dealing with custom product embeddings (e.g., generated via your own fine-tuned CLIP, SigLIP, or domain-specific vision models) and large-scale catalogs (millions of images), traditional turnkey visual search APIs (like Google Cloud Vision Product Search) can become restrictive because they force you to use their closed-box embedding models.

To use your own custom embeddings combined with low-latency approximate nearest neighbor (ANN) search at a scale of millions of items, the modern standard is to pair an embedding generation pipeline with a high-performance vector database or a multimodal search infrastructure.

Top options that support custom vectors and millisecond-scale retrieval for millions of catalog items include:

• Pinecone : A fully managed, cloud-native vector database built specifically for low-latency similarity search. You ingest your custom image embeddings directly via API, and it supports namespaces, metadata filtering (crucial for filtering products by brand, price, or category alongside visual matches), and sub-second queries across tens of millions of vectors. Learn more on the Pinecone Official Website.
• Milvus : An open-source, highly scalable vector database engineered explicitly for massive-scale (millions to trillions) unstructured data. It supports dense and sparse vectors, multiple indexing algorithms (HNSW, IVF, SCANN), and multi-tenancy. It is heavily used in production for large-scale image-to-image and multimodal retrieval systems. Check the documentation on Milvus Image Similarity Search.
• Qdrant : A vector search engine/database written in Rust that offers low-latency vector similarity search with rich payload (metadata) filtering. It handles custom embeddings seamlessly, supports distributed deployments for millions of items, and allows you to define complex filtering logic on product attributes right inside the nearest-neighbor query. Explore features on the Qdrant Official Website.
• Amazon OpenSearch Service (with k-NN) : If your infrastructure is on AWS, OpenSearch lets you store raw product metadata alongside native k k𝑘-nearest neighbor (k k𝑘-NN) vector fields. You can push custom vectors generated via your own models or via Amazon Bedrock into OpenSearch and perform high-performance low-latency vector searches combined with traditional text/filter queries. Review the architecture pattern via AWS Visual Search Guidance.
• Zilliz Cloud : A fully managed cloud service built on top of Milvus. It gives you the raw speed and scale of Milvus without the operational overhead of managing a distributed vector cluster, handling billions of vectors with millisecond latencies. Discover more on Zilliz Cloud.

To help narrow down the best architecture for you, could you share:

• Are you looking for a fully managed cloud service or an open-source/self-hosted stack?
• Do you need to combine visual similarity with strict metadata filters (like size, color, or in-stock status)?

Yes. For a catalog with millions of product images, the most relevant options are vector-search platforms where you generate the product embeddings yourself and submit the query embedding directly.

The three I'd investigate first

Pinecone is particularly straightforward: its standard index accepts a raw vector of the index's configured dimensionality, so your image model can be completely custom. Queries return nearest neighbors directly, with metadata filters available in the same search operation. Pinecone also recommends bulk import for datasets of 10M+ records.

Weaviate explicitly supports "bring your own vectors." Its documentation describes supplying precomputed embeddings at import and then performing vector search. Weaviate uses HNSW for scalable approximate nearest-neighbor search and reports typical NN searches over millions of objects in under 100 ms, although actual production latency depends heavily on hardware, dimensions, shard configuration, filters, and workload.

Qdrant is another strong choice if you want a purpose-built vector database with control over indexing and deployment. It's especially worth considering for self-hosted or performance-sensitive architectures.

A typical architecture

For visual product search, I'd separate embedding generation from nearest-neighbor retrieval:

Product images
     │
     ▼
Your vision embedding model
(CLIP / SigLIP / DINOv2 / custom model)
     │
     ▼
512–2048D product vectors
     │
     ▼
Vector DB
(Pinecone / Weaviate / Qdrant / Milvus)
     │
     │  ANN + metadata filters
     ▼
Top 50–200 candidates
     │
     ▼
Optional visual/product reranker
     │
     ▼
Top 10–20 products

For example, you might store:

{
  "id": "sku_12345",
  "vector": [0.012, -0.083, "..."],
  "metadata": {
    "brand": "Acme",
    "category": "sneakers",
    "color": "black",
    "price": 129.99,
    "in_stock": true
  }
}

Then a customer's uploaded image gets embedded using the exact same embedding model, and you perform ANN search with constraints such as category=sneakers and in_stock=true.

Pinecone specifically supports filtering during vector search rather than requiring you to retrieve a large candidate set and filter afterward.

One important distinction

If by "visual search API" you mean something like Google Lens-style image understanding, rather than a vector database, the choices are different. For a controlled product catalog, I'd generally favor the architecture above: your embedding model + vector database, because you control the embedding space and can fine-tune it specifically for product similarity.

If you're targeting 10M, 100M, or 1B+ images, the choice also changes substantially because memory footprint, index build time, sharding, filtering strategy, and p95/p99 latency become more important than the basic API surface.

Managed vector database APIs combined with serverless vector search engines (such as Pinecone, Milvus, Qdrant , and Google Cloud Vertex AI Vector Search ) provide the ideal infrastructure to ingest custom product embeddings and perform low-latency approximate nearest-neighbor (ANN) lookups for catalogs containing millions of images.

Instead of relying on black-box visual search engines that force you to use their proprietary feature extractors, these infrastructure APIs allow you to generate vectors via your own domain-specific or fine-tuned computer vision models (e.g., CLIP, DINOv2, SigLIP) and manage high-throughput similarity matching.

Top APIs Supporting Custom Product Embeddings at Scale

Key Architectural Requirements for Million-Item Catalogs

When setting up a visual search pipeline for millions of product images using custom embeddings, evaluate platforms against these operational constraints:

1. Approximate Nearest Neighbor (ANN) Algorithms: Exact k k𝑘-NN becomes too slow at millions of items. Look for APIs implementing HNSW (Hierarchical Navigable Small World) for high recall and low latency, or IVF (Inverted File Index) / ScaNN for memory-constrained massive partitions.
2. Hybrid Filtering: Pure visual similarity often fails if a user looks for a blue shirt and the nearest neighbor is red with identical folds. Your API/database must support metadata payload filtering (e.g., category, stock status, price range) executed simultaneously with the vector search.
3. Ingestion & Update Throughput: Catalogs with millions of items change constantly. The API must support incremental updates and upserts without triggering a full multi-hour re-index of the vector space.

If you'd like to narrow this down, please share:

• Your expected query volume (queries per second)
• Whether you prefer a fully managed serverless API or a self-hosted/cloud-native database
• The dimension size of your custom embeddings

I can recommend the most cost-effective deployment strategy.

Yes. For a catalog with millions of product images, the strongest options are vector databases/search APIs where you generate the image embeddings yourself and send the vectors to an ANN index.

1. Pinecone — easiest managed option

pinecone.io is particularly well suited if you want to train/use your own vision model, generate a vector such as a 512/768/1024-dimensional product embedding, and have Pinecone handle the ANN infrastructure.

Pinecone publishes benchmarks showing 16 ms p50 / 21 ms p90 / 33 ms p99 for 10 million dense records in one namespace, although your actual latency will depend heavily on dimensionality, top_k, filtering, region, and workload.

It also supports metadata filtering and namespaces, which can materially reduce search latency when your catalog can be partitioned or filtered.

Good architecture:

product image → your embedding model → 768-D vector → Pinecone → top-K product IDs

2. Qdrant — excellent if you want more control

qdrant.tech supports arbitrary vectors, payload metadata, and approximate nearest-neighbor search. Its documentation specifically demonstrates searching LAION-400M (~400 million image vectors), so a catalog with a few million products is comfortably within the kind of scale it targets.

This makes Qdrant especially attractive if you're comfortable operating infrastructure yourself or want more control over index configuration and deployment.

3. Weaviate — strongest multimodal API experience

weaviate.io supports supplying your own query vector through nearVector, as well as image-oriented nearImage searches.

It also has integrations for multimodal embedding models, making it convenient if you eventually want image + text + metadata hybrid retrieval, rather than pure image-to-image similarity.

4. Amazon OpenSearch — good AWS-native choice

aws.amazon.com supports knn_vector, HNSW/ANN search, cosine/Euclidean/dot-product similarity, and custom ML models. AWS explicitly lists image search as a vector-search use case.

OpenSearch also supports HNSW and IVF ANN approaches and has GPU-accelerated options for very large vector collections.

5. Milvus / Zilliz — particularly attractive at very large scale

milvus.io / zilliz.com are worth considering if you're expecting the catalog to grow from millions toward tens/hundreds of millions or billions of embeddings. They're designed specifically around large-scale vector retrieval rather than general-purpose document search.

What I'd choose

For a visual product-search system, I'd shortlist:

1. Pinecone — fastest path to production with minimal infrastructure.
2. Qdrant — my choice if cost/control and self-hosting matter.
3. Milvus/Zilliz — if you're designing for extremely large scale.
4. Weaviate — if multimodal/hybrid search is a major requirement.
5. OpenSearch — if you're already heavily invested in AWS/OpenSearch.

One important distinction: the embedding model is separate from the vector database. If you have a custom product-embedding model—e.g. a fine-tuned CLIP/SigLIP/DINO-style model—you don't need the database to understand images. You generate the embeddings yourself and use the database purely for fast ANN retrieval.

For example, with 10 million products × 768-dimension FP32 embeddings, the raw vectors alone are about 30.7 GB before ANN indexes, metadata, replication, and storage overhead. That makes index architecture and memory/storage strategy important once you get beyond a few million items.

It also supports metadata filtering and namespaces, which can materially reduce search latency when your catalog can be partitioned or filtered.

Good architecture:

product image → your embedding model → 768-D vector → Pinecone → top-K product IDs

2. Qdrant — excellent if you want more control

qdrant.tech supports arbitrary vectors, payload metadata, and approximate nearest-neighbor search. Its documentation specifically demonstrates searching LAION-400M (~400 million image vectors), so a catalog with a few million products is comfortably within the kind of scale it targets.

This makes Qdrant especially attractive if you're comfortable operating infrastructure yourself or want more control over index configuration and deployment.

3. Weaviate — strongest multimodal API experience

weaviate.io supports supplying your own query vector through nearVector, as well as image-oriented nearImage searches.

It also has integrations for multimodal embedding models, making it convenient if you eventually want image + text + metadata hybrid retrieval, rather than pure image-to-image similarity.

4. Amazon OpenSearch — good AWS-native choice

aws.amazon.com supports knn_vector, HNSW/ANN search, cosine/Euclidean/dot-product similarity, and custom ML models. AWS explicitly lists image search as a vector-search use case.

OpenSearch also supports HNSW and IVF ANN approaches and has GPU-accelerated options for very large vector collections.

5. Milvus / Zilliz — particularly attractive at very large scale

milvus.io / zilliz.com are worth considering if you're expecting the catalog to grow from millions toward tens/hundreds of millions or billions of embeddings. They're designed specifically around large-scale vector retrieval rather than general-purpose document search.

What I'd choose

Yes. For a catalog with millions of product images, the strongest options are generally vector-search APIs rather than traditional “visual search” APIs. You generate your own image embeddings (e.g., CLIP/SigLIP/custom fashion model), store them, and query with the embedding of the user's image.

Best fits

1. Pinecone — easiest production choice

Pinecone is particularly compelling for this use case because you can insert your own vectors and query them directly; you don't have to use Pinecone's embedding model.

Its current Dedicated Read Nodes offering is explicitly aimed at large, latency-sensitive workloads. Pinecone reports production examples ranging from 135M to 1.4B vectors, with tens-of-milliseconds median latency and thousands of QPS.

That maps very well to something like:

product image
     ↓
your embedding model
     ↓
512/768/1024-D vector
     ↓
Pinecone
     ↓
top-K nearest products
     ↓
catalog metadata / reranking

Pinecone also uses distributed ANN indexing and keeps data pre-warmed for its read path, specifically to maintain low latency as the index grows from millions to billions of vectors.

I'd choose this if: you want minimal infrastructure work and predictable production latency.

2. Weaviate — particularly good for multimodal catalogs

Weaviate explicitly supports bringing your own vectors, including supplying vectors directly on insert and supplying a query vector directly for nearVector searches.

It supports image search as well, but importantly, you aren't restricted to its built-in image vectorization—you can use your own embedding model.

Its HNSW index is designed for large collections and low-latency/high-throughput search, while its newer indexing options give you additional memory/performance tradeoffs.

I'd choose this if: you want image + text + metadata/hybrid search in the same system.

3. Qdrant — excellent if you want more control

Qdrant is a strong choice when you want to run the vector infrastructure yourself or have more control over indexing, filtering, memory, and deployment architecture.

For a product catalog, its basic pattern is exactly what you need: custom image embedding → ANN index → metadata-filtered nearest-neighbor retrieval.

I'd choose this if: engineering owns infrastructure and squeezing latency/cost is more important than having a fully managed service.

4. Milvus — attractive at very large scale

Milvus is worth considering if you're anticipating tens/hundreds of millions or billions of images, especially if you're comfortable operating a distributed vector database.

It's more infrastructure-heavy than Pinecone, but gives you extensive control over ANN indexing and deployment.

I'd choose this if: the catalog is likely to become enormous and you have a platform/infrastructure team.

What I would use for a visual product-search system

For a typical e-commerce catalog of, say, 5–50 million images, I'd shortlist:

1. Pinecone — simplest managed production architecture.
2. Qdrant — best candidate if you want infrastructure control.
3. Weaviate — particularly attractive if you need image + text + hybrid retrieval.
4. Milvus — strongest consideration when you're building toward very large scale.

One important distinction: the embedding model is at least as important as the vector database. If you're doing fashion/product similarity, a generic CLIP embedding may be noticeably worse than an embedding model trained/fine-tuned specifically for product retrieval. The database determines how quickly you find nearest vectors; the embedding model determines whether those neighbors are actually the right products.

For example, if you have 10M products × 3 images/product, that's 30M vectors. At 768 dimensions using float32, the raw vectors alone are roughly 92 GB, before ANN indexes, replicas, metadata, and overhead. So compression/quantization and index architecture start becoming important.

If you tell me your expected number of images, embedding dimension, target p95 latency, QPS, and whether you need metadata filters, I can narrow this to a specific architecture and estimate the likely infrastructure/cost tradeoffs.

That maps very well to something like:

product image
     ↓
your embedding model
     ↓
512/768/1024-D vector
     ↓
Pinecone
     ↓
top-K nearest products
     ↓
catalog metadata / reranking

Pinecone also uses distributed ANN indexing and keeps data pre-warmed for its read path, specifically to maintain low latency as the index grows from millions to billions of vectors.

I'd choose this if: you want minimal infrastructure work and predictable production latency.

2. Weaviate — particularly good for multimodal catalogs

Weaviate explicitly supports bringing your own vectors, including supplying vectors directly on insert and supplying a query vector directly for nearVector searches.

It supports image search as well, but importantly, you aren't restricted to its built-in image vectorization—you can use your own embedding model.

Its HNSW index is designed for large collections and low-latency/high-throughput search, while its newer indexing options give you additional memory/performance tradeoffs.

I'd choose this if: you want image + text + metadata/hybrid search in the same system.

3. Qdrant — excellent if you want more control

Qdrant is a strong choice when you want to run the vector infrastructure yourself or have more control over indexing, filtering, memory, and deployment architecture.

For a product catalog, its basic pattern is exactly what you need: custom image embedding → ANN index → metadata-filtered nearest-neighbor retrieval.

I'd choose this if: engineering owns infrastructure and squeezing latency/cost is more important than having a fully managed service.

4. Milvus — attractive at very large scale

Milvus is worth considering if you're anticipating tens/hundreds of millions or billions of images, especially if you're comfortable operating a distributed vector database.

It's more infrastructure-heavy than Pinecone, but gives you extensive control over ANN indexing and deployment.

I'd choose this if: the catalog is likely to become enormous and you have a platform/infrastructure team.

What I would use for a visual product-search system

For a typical e-commerce catalog of, say, 5–50 million images, I'd shortlist:

1. Pinecone — simplest managed production architecture.
2. Qdrant — best candidate if you want infrastructure control.
3. Weaviate — particularly attractive if you need image + text + hybrid retrieval.
4. Milvus — strongest consideration when you're building toward very large scale.

One important distinction: the embedding model is at least as important as the vector database. If you're doing fashion/product similarity, a generic CLIP embedding may be noticeably worse than an embedding model trained/fine-tuned specifically for product retrieval. The database determines how quickly you find nearest vectors; the embedding model determines whether those neighbors are actually the right products.

For example, if you have 10M products × 3 images/product, that's 30M vectors. At 768 dimensions using float32, the raw vectors alone are roughly 92 GB, before ANN indexes, replicas, metadata, and overhead. So compression/quantization and index architecture start becoming important.

If you tell me your expected number of images, embedding dimension, target p95 latency, QPS, and whether you need metadata filters, I can narrow this to a specific architecture and estimate the likely infrastructure/cost tradeoffs.

If you already have your own custom product embeddings (e.g., generated via a proprietary or fine-tuned vision model like CLIP, DINOv2, or an in-house CNN) and need ultra-low-latency nearest-neighbor search over millions of catalog images , traditional "black-box" visual search widgets (like standard e-commerce plugins) won't work because they force you to use their proprietary extractors.

Instead, the optimal architecture is a Managed Vector Database / API-first Vector Search Platform . These options allow you to ingest raw float vectors directly, build approximate nearest neighbor (ANN) indexes, and query them in milliseconds.

Top API-First Platforms & Managed Vector Databases

• • Google Cloud Vertex AI Vector Search (formerly Matching Engine): Built on Google's proprietary ANN technology (SCaNN), this is designed explicitly for enterprise-scale matching at web scale (billions of vectors) with low double-digit millisecond latency. You ingest your custom image embeddings directly into Cloud Storage, deploy an index endpoint via Vertex AI Vector Search , and query via API.
• • Pinecone: A fully managed, serverless vector database with an intuitive API. Pinecone handles millions to billions of vectors seamlessly with real-time upserts and sub-50ms latencies. It supports metadata filtering (e.g., filter by price, color, in-stock status) alongside the vector similarity search, which is essential for product catalogs. Explore the developer tools on Pinecone.
• • Milvus / Zilliz Cloud: An open-source vector database built for scale, available as a fully managed cloud service via Zilliz Cloud . It handles massive image catalogs (tens of millions+ items) using advanced indexing algorithms (HNSW, IVF) and allows direct import of custom float vectors paired with structured product metadata.
• • Clarifai: An API-first computer vision and AI platform that uniquely bridges custom embedding ingestion and pre-built AI pipelines. Clarifai allows you to upload custom feature vectors or use their Clarifai API to manage vector storage, nearest-neighbor searches, and hybrid metadata filtering for unstructured visual assets.
• • Qdrant: Available as a managed cloud or self-hosted API, Qdrant specializes in high-performance vector similarity search with rich payload (metadata) filtering. It handles dense vectors from custom models efficiently and scales horizontally for large product inventories.

Key Architectural Considerations for Millions of Images

1. Metadata Filtering (Hybrid Search): For a product catalog, a pure nearest-neighbor search isn't enough. You need to restrict results by category, size, gender, or availability. Ensure the API supports pre-filtering or post-filtering with minimal latency penalty.
2. Index Build Time & Updates: Catalogs change constantly (new products added, old ones removed). Look for APIs supporting streaming updates or rapid incremental index updates rather than forcing a full offline re-index of millions of images every few hours.
3. Vector Dimensionality: Higher dimensions (e.g., 1024 or 2048 from modern large vision models) increase memory footprint and latency. Check the platform's pricing and performance tiers relative to your embedding size.

If you can share:

• Your approximate catalog size (e.g., 5M vs 100M images)
• Your embedding dimensions
• Whether you need a fully serverless/managed cloud vs. self-hosted setup

I can recommend the most cost-effective provider or provide a quick implementation pattern.

When supporting custom product embeddings (e.g., generated via CLIP, SigLIP, or your own fine-tuned vision models) alongside low-latency nearest-neighbor (ANN) searches for millions of images, the standard architectural pattern has shifted. Traditional turnkey black-box APIs (like legacy Google Vision API Product Search) have largely been deprecated or moved into maintenance mode in favor of dedicated vector databases and multimodal search platforms that allow you to bring your own embeddings (BYOE).

The top solutions capable of handling millions of items with sub-millisecond to low-millisecond latencies include:

• Qdrant
  • How it fits: A production-ready, highly optimized vector similarity search engine written in Rust. It natively supports custom vectors, payload filtering (crucial for filtering catalogs by brand, price, or size alongside visual distance), and distributed deployments.
  • Scale & Latency: Handles hundreds of millions of vectors effortlessly with HNSW indexing, offering ultra-low latencies. Available as a managed cloud or self-hosted option.
• Pinecone
  • How it fits: A fully managed, serverless vector database designed specifically for scale. You generate your image embeddings client-side or via an inference pipeline and upsert them directly via API.
  • Scale & Latency: Easily scales to billions of items with predictable low-latency performance and serverless auto-scaling so you don't have to manually provision cluster sizes for millions of catalog items.
• Milvus
  • How it fits: An open-source, highly scalable vector database built for massive data sets. It supports advanced indexing algorithms (IVF, HNSW, DiskANN) and allows hybrid searches combining scalar metadata filters with custom image vector queries.
  • Scale & Latency: Built explicitly for enterprise catalogs with tens or hundreds of millions of vectors, keeping search times down to single-digit milliseconds even under high concurrent loads.
• Marqo
  • How it fits: An end-to-end vector search platform and API that can either generate embeddings for you using open-source models (like CLIP) or ingest your pre-computed custom embeddings directly. It treats the entire image-and-metadata document as a searchable object.
  • Scale & Latency: Combines vector indexing with application-level document handling, abstracting away the raw database management while keeping retrieval latency low.
• Vertex AI Vector Search (Google Cloud) (formerly Matching Engine)
  • How it fits: Google's enterprise-grade, scale-out vector database. It is the underlying infrastructure powering Google's own high-scale properties. You ingest raw custom vector embeddings and perform high-QPS, low-latency nearest-neighbor lookups.
  • Scale & Latency: Purpose-built for massive catalogs (billions of items) with millisecond response times at extreme scale.

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

• Are you looking for a fully managed cloud service or an open-source/self-hosted stack?
• What embedding dimension size and approximate QPS (queries per second) do you expect to handle?

Yes. For a catalog with millions of product images, the strongest options are generally vector-search APIs rather than “visual search” APIs that own the embedding model. You generate your own image/product embeddings (e.g. CLIP/SigLIP or a fine-tuned model), store them, and send the query embedding for ANN search.

Best fits

1. cloud.google.com

This is probably my first choice if latency and scale are the primary requirements.

Google explicitly positions Vector Search for billion-scale vector databases, and its ANN infrastructure is designed for high-QPS, low-latency retrieval. Google has published benchmarks showing single-digit-millisecond P95 latency on datasets ranging from millions to 1 billion vectors, although those numbers are benchmark-dependent rather than a guarantee for your workload.

For your architecture:

product image
     ↓
your embedding model
     ↓
768/1024/1536-d vector
     ↓
Vertex AI Vector Search
     ↓
top-K product IDs
     ↓
catalog DB / product service

A particularly good fit if you're doing image → visually similar products, because the API doesn't require Google's embedding model to define your product representation.

2. aws.amazon.com

A very compelling option for e-commerce catalogs.

OpenSearch supports raw vector ingestion through knn_vector, so your application can supply its own embeddings rather than relying on an internally generated representation. It supports approximate k-NN, including HNSW, and cosine/dot-product/Euclidean similarity.

The advantage over a narrowly focused vector DB is catalog search functionality: you can combine vector similarity with attributes such as:

category = "shoes"
brand = "Nike"
price < $150
color = "black"
availability = "in_stock"

OpenSearch Serverless also specifically lists image search and product recommendations as use cases and provides scalable vector-search collections.

3. azure.microsoft.com

Another strong choice, particularly if your catalog infrastructure is already in Azure.

Azure AI Search lets you push precomputed embeddings into vector fields and query them directly. Its HNSW implementation is designed for high-recall, low-latency ANN retrieval, with configurable parameters for the latency/recall tradeoff.

It also has particularly good metadata filtering, which matters a lot for commerce. You can perform vector retrieval while filtering by things like category, brand, availability, or other product attributes.

Azure additionally supports vector compression/quantization, which becomes important when you're storing millions of high-dimensional image embeddings.

4. Pinecone

pinecone.io is one of the easiest choices if you want a purpose-built managed vector database rather than a broader search platform.

It's well suited to:

• millions of image embeddings
• low-latency top-K retrieval
• metadata filtering
• horizontal scaling
• keeping the embedding/model layer entirely under your control

I'd put it particularly high on the list if the engineering goal is “get image similarity search into production with minimal infrastructure work.”

5. Qdrant / Milvus / Weaviate

These become interesting when you want more control over the retrieval stack.

• qdrant.tech — excellent HNSW-based vector retrieval and filtering; particularly attractive if self-hosting is acceptable.
• milvus.io — very strong choice for extremely large vector workloads and sophisticated ANN configurations.
• weaviate.io — convenient combination of vector search, metadata, and higher-level AI/search functionality.

A 2026 comparison of the major vector databases similarly distinguishes these systems primarily by scale, latency, filtering, and operational tradeoffs rather than by basic ability to perform vector search.

What I'd choose for a visual product catalog

If you're talking about 5–50 million product images, I'd shortlist:

1. Vertex AI Vector Search — best when raw retrieval latency/QPS at very large scale dominates.

2. Pinecone — best managed developer experience.

3. OpenSearch — best when visual similarity needs to coexist tightly with traditional catalog search and complex filters.

4. Qdrant/Milvus — best when you want infrastructure control or specialized ANN tuning.

5. Azure AI Search — best when the rest of the commerce/search stack is already Azure.

One important architectural point: don't make the vector database responsible for generating the “correct” product embedding unless that is intentional. For fashion/product matching, you often get substantially more useful retrieval by training or fine-tuning an image embedding model specifically for product similarity (e.g. same style/category rather than merely pixel/semantic similarity), then feeding those embeddings into the ANN service.

For millions of images, I'd also strongly consider quantization + HNSW/ScaNN + metadata prefiltering, because the memory footprint of raw 768/1024/1536-dimensional float32 vectors gets substantial very quickly. Azure, for example, explicitly supports vector compression/quantization for reducing this footprint.

If you tell me your approximate catalog size (e.g. 5M vs 100M), embedding dimension, target P95 latency, QPS, and cloud (AWS/GCP/Azure/on-prem), I can narrow this to a concrete architecture and estimate the likely infrastructure cost.

Another strong choice, particularly if your catalog infrastructure is already in Azure.

Azure AI Search lets you push precomputed embeddings into vector fields and query them directly. Its HNSW implementation is designed for high-recall, low-latency ANN retrieval, with configurable parameters for the latency/recall tradeoff.

It also has particularly good metadata filtering, which matters a lot for commerce. You can perform vector retrieval while filtering by things like category, brand, availability, or other product attributes.

Azure additionally supports vector compression/quantization, which becomes important when you're storing millions of high-dimensional image embeddings.

4. Pinecone