Yes — there are several image search / visual similarity APIs that can handle large catalogs and near-duplicate detection, though the exact fit depends on whether you need:

• exact/near-duplicate detection for catalog cleanup,
• general visual similarity search,
• product matching / SKU matching, or
• custom embeddings + vector search at very large scale.

Here are the main options:

1) Google Cloud Vision / Vertex AI Vector Search

• Good for large-scale similarity search if you build embeddings and index them.
• Not a turnkey “near-duplicate detector,” but works well for catalog-scale search.
• Best when you want to own the pipeline and scale on Google infrastructure.

2) AWS Rekognition + OpenSearch / KNN

• Rekognition can help with image analysis, but for similarity at scale you’d usually pair image embeddings with:
  • OpenSearch k-NN
  • Aurora PostgreSQL pgvector
  • SageMaker embeddings
• Strong option if you’re already on AWS.

3) Azure AI Vision + Azure AI Search / Vector Search

• Similar pattern: use embeddings + vector index.
• Good for enterprise catalog search and dedup workflows.

4) Clarifai

• Has visual similarity, embeddings, and large-scale indexing features.
• Often used for media and enterprise image search.
• Can be a practical managed solution if you want less infrastructure work.

5) Imagga

• Offers image tagging, categorization, and visual search.
• More turnkey than pure vector infrastructure.
• Often used for product catalogs and media libraries.

6) TinEye

• One of the best-known reverse image search / duplicate detection systems.
• Very strong for near-duplicate and exact/modified duplicate matching.
• Less of a general ML platform, more of a specialized matching service.
• Worth checking if your primary need is duplicate detection.

7) PicPurify / other moderation-oriented image APIs

• Some offer duplicate detection or content matching features, but capabilities vary.
• Usually more niche and less suited to huge general-purpose catalogs.

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If your main need is near-duplicate detection at scale

The strongest approaches are usually:

A. Specialized duplicate-matching service

• TinEye is the most obvious candidate.
• Pros: easiest path for duplicates.
• Cons: less flexibility, may be expensive or limited for custom workflows.

B. Embeddings + vector database

Use a modern embedding model (e.g., CLIP-like or product-specific embeddings) and index in:

• Pinecone
• Weaviate
• Milvus
• Qdrant
• OpenSearch / Elasticsearch kNN
• pgvector

This is the most scalable and customizable approach for large catalogs.

C. Hybrid approach

For best results:

1. Use perceptual hashing or lightweight filtering to catch obvious duplicates.
2. Use embeddings for broader similarity.
3. Re-rank with a stronger model or business rules.

This is common for catalogs with millions of images.

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What to look for in an API

If you’re evaluating vendors, ask whether they support:

• perceptual hash / near-duplicate detection
• embedding generation
• vector indexing/search
• batch ingestion for millions of images
• low-latency query at scale
• metadata filtering like brand, category, SKU
• incremental updates
• false-positive controls / thresholds
• support for crops, rotations, watermarks, and compression artifacts

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Practical recommendation

If you want the fastest path to production:

• For duplicates specifically: start with TinEye
• For large-scale catalog similarity + custom control: use embeddings + vector DB
• For enterprise managed services: consider Clarifai, Imagga, or cloud-native vector search stacks

If you want, I can also give you:

1. a vendor comparison table, or
2. a recommended architecture for millions of product images and near-duplicate detection.