Match 10% more products than Voyage multimodal‑3.5, 20% cheaper

SigLIP turns product photos and search text into matching vectors. SIE is the open-source inference engine that serves it, so shoppers can find products without captions or tags.

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SIE SigLIP so400m

Matches query

QUERY

A blue lace-up shoe with a white sole.

1cat-01.jpg
2cat-02.jpg
3cat-03.jpg

ABO photos · CC BY 4.0

Voyage and GPT-6.1 Sol captions put the wrong colour first

gold metal ceiling light

Voyage multimodal-3.5

Wrong colour: black

black metal ceiling light — First result

beige leather stool

GPT-6.1 Sol (high reasoning)

Wrong colour: brown

brown leather stool — First result · Captions + text-embedding-3-large

Baymard Institute 56% of online stores fail shoppers’ searches; 39% handle searches for a product feature badly 29 April 2026

More right matches than Voyage, at the lowest price per photo

Run this eval

Right product first

309 searches ↑

SIE SigLIP so400m

SIE SigLIP so400m 83.1%

Cohere Embed v5 Pro

Cohere Embed v5 Pro 82.6%

Voyage multimodal-3.5

Voyage multimodal-3.5 75.1%

GPT-6.1 Sol captions

GPT-6.1 Sol captions 71.6%

Claude Sonnet 5.5 captions

Claude Sonnet 5.5 captions 71.2%

Voyage multimodal-3.5 (224 px)

Voyage multimodal-3.5 (224 px) 70.3%

Cost per 1M photos

indexing, USD ↓

SIE SigLIP so400m

$24 SIE SigLIP so400m

Voyage multimodal-3.5 (224 px)

$30 Voyage multimodal-3.5 (224 px)

Cohere Embed v5 Pro

$335 Cohere Embed v5 Pro

Voyage multimodal-3.5

$514 Voyage multimodal-3.5

GPT-6.1 Sol captions

GPT-6.1 Sol captions $3,025

Claude Sonnet 5.5 captions

Claude Sonnet 5.5 captions $3,082

Evaluation

Tested: 309 searches over 2,573 catalogue photos with similar alternatives
Scored: First photo matches the requested colour, material and product
Prices: SIE Serverless; provider list prices on recorded usage

Encode your photos once, then search by text

from urllib.request import urlopen
from sie_sdk import SIEClient
client = SIEClient(api_key="API keysk-sie-…")
query = "queryA blue lace-up shoe with a white sole."
images = [
    "image 1cat-01.jpg", "image 2cat-02.jpg", "image 3cat-03.jpg",
    # 17 more photos; Copy and Run include all 20.
]
model = "modelgoogle/siglip-so400m-patch14-384"
# Index once; keep these vectors for later searches.
items = [{"images": [{"data": urlopen("https://superlinked.com/reference/image-search/pilot/" + name).read(), "format": "jpeg"}]} for name in images]
index = []
for start in range(0, len(items), 10):
    index.extend(client.encode(model, items[start:start + 10], is_query=False))
q = client.encode(model, [{"text": query}], is_query=True)[0]["dense"]
print(q)
print(index)

import { SIEClient } from '@superlinked/sie-sdk';

const client = new SIEClient(undefined, { apiKey: 'sk-sie-…' });

const query = "A blue lace-up shoe with a white sole.";
const images = [
    "cat-01.jpg", "cat-02.jpg", "cat-03.jpg",
    // 17 more photos; Copy and Run include all 20.
];
const model = "google/siglip-so400m-patch14-384";

// Index once; preserve the JPEG bytes.
const items = await Promise.all(images.map(async (name) => {
  const response = await fetch("https://superlinked.com/reference/image-search/pilot/" + name);
  if (!response.ok) throw new Error('Could not load image');
  return { images: [new Uint8Array(await response.arrayBuffer())] };
}));
const index: { dense?: Float32Array }[] = [];
for (let start = 0; start < items.length; start += 10) {
  index.push(...await client.encode(model, items.slice(start, start + 10), { isQuery: false }));
}
const [q] = await client.encode(model, [{ text: query }], { isQuery: true });
console.log(q, index);

Build the "Image search" capability into my app using the Superlinked Inference Engine (SIE).

Context
- SIE is an OpenAI-style inference API. Python SDK: `from sie_sdk import SIEClient`; TypeScript: `@superlinked/sie-sdk`.
- Base URL: https://api.superlinked.com (or my regional endpoint). Auth: Bearer key from env `SIE_API_KEY` (never hard-code it).
- Model: google/siglip-so400m-patch14-384 (SIE primitive: /encode). Keep the model id configurable.

Task
- Input: a text query plus one or more candidate images.
- Behaviour: return native dense vectors for the query and candidate images
- Fetch the original JPEG bytes from https://superlinked.com/reference/image-search/pilot/. Encode images in batches of 10 with is_query false, then encode the query separately with is_query true. Python: client.encode with is_query. TypeScript: client.encode with isQuery. Return the native dense vectors without application scoring or ranking.

Deliverables
- A typed client wrapper, an application-level function for this task, error handling for timeouts/empty input, and unit tests with a stubbed client.
- Wire it into my existing stack (ask me which framework if unclear) and add a short usage example.

Run

Output

Recorded vectors, ranked by cosineTop 5 of 20

1
cat-01.jpg0.1055 cosine
2
cat-02.jpg0.0977 cosine
3
cat-03.jpg0.0718 cosine
4
cat-11.jpg0.0415 cosine
5
cat-12.jpg-0.0047 cosine

Encode returns dense vectors. Your code or vector database ranks the photos.

Index a million photos for $24, 20% cheaper than Voyage multimodal-3.5

Serverless API

We host the models; you call one API.

No idle GPUs, pay for what you use
Fits your stack: SDK, API, CLI, MCP
Self-host the same open-source engine
SOC 2 Type 2, US or EU data residency
Create an account to try Serverless

Get started

Self-hosted (Kubernetes)

Your data stays in your cloud, backed by our engineers.

Terraform modules for your cloud
Apache-2.0, same engine as Serverless
GPU workers scale to zero between jobs
Separate worker pools for each tenant

Agent prompt Deploy SIE to our AWS account with the superlinked/sie/aws Terraform module. Docs: superlinked.com/docs/deployment.

Deploy SIE to our Google Cloud project with the superlinked/sie/google Terraform module. Docs: superlinked.com/docs/deployment.

Deploy SIE to our Azure subscription with the superlinked/sie/azure Terraform module. Docs: superlinked.com/docs/deployment.

Deploy SIE to our Alibaba Cloud account with the superlinked/sie/alicloud Terraform module. Docs: superlinked.com/docs/deployment.

Talk to us Deploy guide

Run locally

The open-source server on your own machine.

Runs on your NVIDIA GPU
One container, no cluster
Process your data fully offline
Same SDK and model IDs as Serverless

docker run --gpus all -p 8080:8080 ghcr.io/superlinked/sie-server:latest-cuda12-default

Quickstart

Switch to SIE in 5 minutes

import voyageai
from PIL import Image

client = voyageai.Client()
model = "voyage-multimodal-3.5"
image_vector = client.multimodal_embed(
    [[Image.open("photo.jpg")]], model=model, input_type="document",
).embeddings[0]
query_vector = client.multimodal_embed(
    [[query]], model=model, input_type="query",
).embeddings[0]

SIE

from pathlib import Path
from sie_sdk import SIEClient

client = SIEClient(api_key="sk-sie-...")
model = "google/siglip-so400m-patch14-384"
image_bytes = Path("photo.jpg").read_bytes()
image_vector = client.encode(model, [{"images": [image_bytes]}])[0]["dense"]
query_vector = client.encode(model, [{"text": query}], is_query=True)[0]["dense"]