Best Image Similarity Search Tools in 2026

import requests

API_URL = "https://matchengine.tineye.com/your-collection/rest/"
HEADERS = {"Authorization": "Basic YOUR_API_KEY"}

# Add image to index
requests.post(
    f"{API_URL}add/",
    headers=HEADERS,
    files={"image": open("product.jpg", "rb")},
    data={"filepath": "product-001.jpg"},
)

# Search for matches
response = requests.post(
    f"{API_URL}search/",
    headers=HEADERS,
    files={"image": open("query.jpg", "rb")},
)
matches = response.json()["result"]

from qdrant_client import QdrantClient
from qdrant_client.models import Distance, VectorParams
from PIL import Image
import clip, torch

# Generate image embedding
model, preprocess = clip.load("ViT-B/32")
image = preprocess(Image.open("query.jpg")).unsqueeze(0)
with torch.no_grad():
    embedding = model.encode_image(image).squeeze().tolist()

# Search Qdrant
client = QdrantClient("localhost", port=6333)
results = client.query_points(
    collection_name="images",
    query=embedding,
    limit=10,
)

from pinecone import Pinecone
import clip, torch
from PIL import Image

# Generate image embedding with CLIP
model, preprocess = clip.load("ViT-B/32")
image = preprocess(Image.open("query.jpg")).unsqueeze(0)
with torch.no_grad():
    embedding = model.encode_image(image).squeeze().tolist()

# Search Pinecone
pc = Pinecone(api_key="YOUR_API_KEY")
index = pc.Index("images")

results = index.query(
    vector=embedding,
    top_k=10,
    include_metadata=True,
)

// imgix uses URL-based image operations
// Similarity features are part of their enterprise API

const imgixClient = new ImgixClient({
  domain: "your-source.imgix.net",
  secureURLToken: "YOUR_TOKEN",
});

// Serve optimized image
const url = imgixClient.buildURL("product.jpg", {
  w: 400,
  h: 400,
  fit: "crop",
  auto: "format,compress",
});

// Visual similarity via imgix API (enterprise)
const response = await fetch(
  "https://api.imgix.com/v1/images/similar",
  {
    method: "POST",
    headers: { Authorization: "Bearer YOUR_TOKEN" },
    body: JSON.stringify({ image_url: url, limit: 10 }),
  }
);

from google.cloud import vision

client = vision.ImageAnnotatorClient()

with open("query.jpg", "rb") as f:
    image = vision.Image(content=f.read())

# Web detection — finds similar images across the web
response = client.web_detection(image=image)
web = response.web_detection

for page in web.pages_with_matching_images:
    print(f"Found on: {page.url}")

for match in web.visually_similar_images:
    print(f"Similar: {match.url}")

import boto3

rekognition = boto3.client("rekognition", region_name="us-east-1")

# Create a face collection
rekognition.create_collection(CollectionId="employees")

# Index a face
with open("employee.jpg", "rb") as f:
    rekognition.index_faces(
        CollectionId="employees",
        Image={"Bytes": f.read()},
        ExternalImageId="emp-001",
    )

# Search for matching faces
with open("query.jpg", "rb") as f:
    matches = rekognition.search_faces_by_image(
        CollectionId="employees",
        Image={"Bytes": f.read()},
        MaxFaces=5,
        FaceMatchThreshold=90,
    )

import clip
import torch
from PIL import Image

model, preprocess = clip.load("ViT-L/14", device="cuda")

# Image embedding
image = preprocess(Image.open("product.jpg")).unsqueeze(0).to("cuda")
with torch.no_grad():
    image_embedding = model.encode_image(image)
    image_embedding /= image_embedding.norm(dim=-1, keepdim=True)

# Text embedding (same space — enables text-to-image search)
text = clip.tokenize(["red running shoes"]).to("cuda")
with torch.no_grad():
    text_embedding = model.encode_text(text)
    text_embedding /= text_embedding.norm(dim=-1, keepdim=True)

# Cosine similarity
similarity = (image_embedding @ text_embedding.T).item()

from clarifai.client.user import User

client = User(user_id="YOUR_USER_ID", pat="YOUR_PAT")
app = client.app(app_id="my-visual-search")

# Add images to search index
dataset = app.dataset(dataset_id="products")
dataset.upload_from_url(
    url="https://example.com/product.jpg",
    input_id="prod-001",
    metadata={"category": "shoes"},
)

# Visual similarity search
model = app.model(model_id="general-image-embedding")
results = model.predict_by_url(
    url="https://example.com/query.jpg",
    input_type="image",
)

import torch
from PIL import Image
from torchvision import transforms

model = torch.hub.load("facebookresearch/dinov2", "dinov2_vitl14")
model.eval().cuda()

transform = transforms.Compose([
    transforms.Resize(518, interpolation=3),
    transforms.CenterCrop(518),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406],
                         std=[0.229, 0.224, 0.225]),
])

image = transform(Image.open("query.jpg")).unsqueeze(0).cuda()
with torch.no_grad():
    embedding = model(image)  # [1, 1024]

# Store in any vector database for similarity search
print(f"Embedding shape: {embedding.shape}")