Best Multimodal Search APIs in 2026
We tested the top multimodal search APIs on cross-modal retrieval quality, query flexibility, and production scalability. This guide covers platforms enabling search across text, images, video, and audio through unified APIs.
How We Evaluated
Cross-Modal Retrieval
Quality of search results when querying across modalities: text-to-image, text-to-video, image-to-text, and mixed queries.
Modality Coverage
Number of content types searchable through a single API: text, images, video, audio, and documents.
Query Sophistication
Support for advanced queries: hybrid search, filtered search, multi-vector search, and re-ranking.
Production Scale
Query latency, indexing throughput, and reliability at production scale with large multimodal collections.
Overview
Mixpeek
Purpose-built multimodal search platform with unified ingestion and retrieval across text, images, video, audio, and PDFs. Features multi-stage retrieval pipelines with ColBERT, hybrid search, and configurable re-ranking.
Only platform offering native multi-stage retrieval pipelines (filter, sort, reduce, enrich) across five modalities in a single API call, with ColBERT late-interaction scoring for fine-grained relevance.
Strengths
- +True multimodal search across five content types in one API
- +Multi-stage retrieval with filter, sort, reduce, and enrich stages
- +ColBERT, ColPaLI, and SPLADE for advanced retrieval quality
- +Self-hosted deployment for data sovereignty
Limitations
- -Pipeline and retriever concepts require learning investment
- -More complex than simple search-as-a-service
- -Enterprise pricing for high-query-volume applications
Real-World Use Cases
- •E-commerce visual product search where shoppers upload a photo and find matching items across product images and videos
- •Media asset management for broadcast companies searching across archived footage, audio clips, and transcripts simultaneously
- •Legal discovery platforms that search across contracts, scanned documents, recorded depositions, and email attachments
- •Content moderation systems that match flagged content across images, video, and text using a single query
Choose This When
When you need production-grade cross-modal search with advanced retrieval strategies like hybrid search, re-ranking, and metadata filtering across text, image, video, audio, and PDF content.
Skip This If
When you only need simple keyword search within a single content type, or you are prototyping with fewer than 1,000 documents and want a zero-config setup.
Integration Example
from mixpeek import Mixpeek
client = Mixpeek(api_key="YOUR_API_KEY")
# Search across all modalities with a text query
results = client.retrievers.search(
retriever_id="ret_abc123",
query="red sports car driving on a coastal highway",
modalities=["text", "image", "video"],
filters={"category": "automotive"},
top_k=20
)
for result in results:
print(result.score, result.modality, result.document_id)Google Vertex AI Search
Google Cloud's managed search service supporting text, images, and structured data. Part of the Vertex AI platform with grounding capabilities for reducing hallucinations in generative answers.
Grounding capabilities that attach citations and confidence scores to generative search answers, reducing hallucinations in enterprise question-answering workflows.
Strengths
- +Managed service with Google-scale infrastructure
- +Grounding for generative search answers
- +Multi-modal document understanding
- +Strong GCP ecosystem integration
Limitations
- -Limited video search capabilities
- -GCP vendor lock-in
- -Complex pricing across multiple dimensions
Real-World Use Cases
- •Enterprise knowledge bases that need grounded generative answers from internal documentation and images
- •Retail product catalogs combining text descriptions and product images for unified search
- •Customer support portals searching across help articles, screenshots, and structured FAQs
Choose This When
When your organization is already on GCP and you need managed search with generative answer capabilities and strong document understanding.
Skip This If
When you need deep video or audio search, or when GCP vendor lock-in is unacceptable for your deployment requirements.
Integration Example
from google.cloud import discoveryengine_v1 as discoveryengine
client = discoveryengine.SearchServiceClient()
request = discoveryengine.SearchRequest(
serving_config="projects/my-project/locations/global/collections/default_collection/engines/my-engine/servingConfigs/default_search",
query="product safety compliance document",
page_size=10,
)
response = client.search(request=request)
for result in response.results:
print(result.document.derived_struct_data)Jina AI
Developer-focused AI company offering multimodal embeddings, reranking, and search infrastructure. Known for jina-embeddings and jina-clip models that enable text-image unified search.
Open-weight multimodal embedding models (jina-clip, jina-embeddings) that can be self-hosted for full data control while maintaining competitive quality against proprietary alternatives.
Strengths
- +Strong multimodal embedding models
- +Open-weight models for self-hosting
- +Good reranking capabilities
- +Competitive embedding pricing
Limitations
- -Limited video and audio search support
- -Requires building retrieval infrastructure
- -Smaller enterprise feature set
Real-World Use Cases
- •Building a custom image-text search engine using jina-clip embeddings with your own vector database
- •Document retrieval systems using jina-reranker to improve precision on initial search results
- •Academic research platforms embedding papers and figures into a shared vector space for cross-modal discovery
Choose This When
When you want high-quality multimodal embeddings at low cost and are comfortable building your own retrieval infrastructure on top.
Skip This If
When you need an end-to-end managed search platform with video and audio support, or when you lack the engineering resources to build retrieval infrastructure.
Integration Example
import requests
url = "https://api.jina.ai/v1/embeddings"
headers = {"Authorization": "Bearer YOUR_API_KEY"}
data = {
"model": "jina-clip-v2",
"input": [
{"text": "a photo of a sunset over the ocean"},
{"image": "https://example.com/sunset.jpg"}
]
}
response = requests.post(url, json=data, headers=headers)
embeddings = response.json()["data"]Weaviate
Vector database with built-in multi-modal vectorization modules. Supports text-to-image and image-to-text search through CLIP and other multimodal embedding integrations.
Built-in vectorizer modules that generate embeddings at query time without external API calls, combining vector database and embedding generation in a single deployment.
Strengths
- +Built-in multimodal vectorizer modules
- +Hybrid search combining BM25 and vector
- +Open source with managed cloud option
- +GraphQL and REST API flexibility
Limitations
- -Multimodal search limited to text and images
- -No native video or audio content processing
- -Vectorizer modules add query latency
Real-World Use Cases
- •E-commerce product search where users can search by text description or upload a product image to find similar items
- •Digital asset management systems with hybrid BM25 + vector search over image collections with text metadata
- •Content recommendation engines that find visually similar articles or products using CLIP embeddings
Choose This When
When you want an open-source vector database with integrated embedding generation for text-image search and prefer GraphQL or REST APIs.
Skip This If
When you need search across video or audio content, or when you require a fully managed end-to-end search pipeline without building ingestion workflows.
Integration Example
import weaviate
client = weaviate.connect_to_weaviate_cloud(
cluster_url="https://your-cluster.weaviate.network",
auth_credentials=weaviate.auth.AuthApiKey("YOUR_API_KEY"),
)
collection = client.collections.get("Products")
results = collection.query.near_text(
query="vintage leather jacket",
limit=5,
return_metadata=weaviate.classes.query.MetadataQuery(distance=True)
)
for obj in results.objects:
print(obj.properties["name"], obj.metadata.distance)Vectara
Managed search platform with multimodal indexing supporting text, images, and documents. Offers hallucination-aware retrieval with factual consistency scoring.
Built-in factual consistency scoring (HHEM) that flags potentially hallucinated answers, providing confidence metrics that other search platforms do not offer natively.
Strengths
- +Managed infrastructure with no vector database to operate
- +Factual consistency scoring for grounded results
- +Multi-modal document understanding
- +Simple API for quick prototyping
Limitations
- -Limited video and audio search support
- -Less customization than pipeline-based platforms
- -Pricing less transparent at scale
Real-World Use Cases
- •Enterprise knowledge search with factual consistency scoring to surface reliable answers from internal documents
- •Customer-facing FAQ and documentation search with grounded answers that cite source passages
- •Compliance and regulatory search where answer accuracy is critical and hallucinations must be flagged
Choose This When
When you need a zero-ops managed search platform with built-in hallucination detection and do not require video or audio search capabilities.
Skip This If
When you need deep customization of retrieval pipelines, self-hosted deployment, or search across video and audio content.
Integration Example
import requests
url = "https://api.vectara.io/v2/corpora/my-corpus/query"
headers = {
"x-api-key": "YOUR_API_KEY",
"Content-Type": "application/json"
}
data = {
"query": "what are the return policy terms",
"search": {"limit": 10},
"generation": {"max_used_search_results": 5}
}
response = requests.post(url, json=data, headers=headers)
print(response.json()["summary"])Twelve Labs
Video-native multimodal search API built on proprietary video understanding models. Indexes visual, audio, and textual content within videos for precise temporal search with natural language queries.
Video-native foundation models that understand visual scenes, spoken dialogue, and on-screen text simultaneously, enabling temporal search precision that general-purpose multimodal APIs cannot match.
Strengths
- +Best-in-class video understanding and temporal search
- +Natural language queries against video content
- +Indexes visual, audio, and text layers simultaneously
- +Simple API with pre-built video processing pipeline
Limitations
- -Focused primarily on video, limited text-only or image-only search
- -Cloud-only with no self-hosting option
- -Per-minute pricing can be expensive for large libraries
- -Smaller ecosystem and fewer integrations than general-purpose platforms
Real-World Use Cases
- •Media companies searching for specific moments across thousands of hours of broadcast footage using natural language
- •E-learning platforms letting students search lecture videos for specific concepts or demonstrations
- •Ad tech companies analyzing competitor video ads by searching for visual themes, products, or messaging patterns
Choose This When
When video is your primary content type and you need precise temporal search with natural language queries across visual, audio, and text layers.
Skip This If
When you need to search across documents, images, and text alongside video in a single unified index, or when you require self-hosted deployment.
Integration Example
from twelvelabs import TwelveLabs
client = TwelveLabs(api_key="YOUR_API_KEY")
search_results = client.search.query(
index_id="idx_abc123",
query_text="person explaining a whiteboard diagram",
options=["visual", "conversation", "text_in_video"],
threshold="medium"
)
for clip in search_results.data:
print(f"{clip.start}-{clip.end}s: {clip.score}")Marqo
Open-source tensor search engine that combines vector and lexical search with built-in multimodal support. Handles embedding generation internally using CLIP and other models, eliminating the need for separate embedding pipelines.
Integrated tensor search that generates embeddings, indexes, and searches in one system with no external embedding API or vector database required.
Strengths
- +Built-in embedding generation for text and images
- +Tensor search combining dense and lexical scoring
- +Open source with self-hosting flexibility
- +Simple document-in, search-out API design
Limitations
- -Limited to text and image modalities
- -Smaller community than established vector databases
- -GPU requirements for self-hosted embedding generation
- -Cloud offering still maturing
Real-World Use Cases
- •E-commerce product search with automatic CLIP embedding generation for product images and descriptions
- •Internal asset search systems where teams can search images and documents without managing a separate embedding service
- •Prototype multimodal search applications where fast setup is more important than modality breadth
Choose This When
When you want a self-contained, open-source multimodal search engine that handles embedding generation internally and you primarily work with text and images.
Skip This If
When you need video or audio search, or when you require a fully managed cloud service with enterprise SLAs.
Integration Example
import marqo
client = marqo.Client(url="http://localhost:8882")
client.index("products").add_documents(
[{"title": "Red Sneakers", "image": "https://example.com/sneakers.jpg"}],
tensor_fields=["title", "image"]
)
results = client.index("products").search(
q="comfortable running shoes",
searchable_attributes=["title", "image"]
)
for hit in results["hits"]:
print(hit["title"], hit["_score"])Cohere Embed + Rerank
Enterprise AI platform offering Embed v3 for multimodal embeddings and Rerank for precision improvement. Supports text and image embeddings in a shared vector space with strong multilingual capabilities.
Best-in-class multilingual embeddings with a dedicated Rerank API that can be layered on top of any retrieval system for a measurable precision boost.
Strengths
- +Embed v3 supports text and image in a shared embedding space
- +Rerank API significantly boosts retrieval precision
- +Strong multilingual support across 100+ languages
- +Enterprise-ready with SOC 2 compliance
Limitations
- -No native video or audio embedding support
- -Requires external vector store for indexing and search
- -Per-API-call pricing can be unpredictable at high volume
- -Embedding-only -- no end-to-end search pipeline
Real-World Use Cases
- •Multilingual e-commerce search where product queries in any language match images and descriptions across locales
- •Two-stage retrieval pipelines using Embed for initial recall and Rerank for precision on shortlisted results
- •Cross-lingual document search across international offices where documents and queries are in different languages
Choose This When
When you need multilingual multimodal embeddings with enterprise compliance requirements and plan to pair them with your own vector database and retrieval logic.
Skip This If
When you need a turnkey search solution or require video and audio content understanding as part of your search pipeline.
Integration Example
import cohere
co = cohere.ClientV2(api_key="YOUR_API_KEY")
response = co.embed(
texts=["red leather handbag"],
images=["https://example.com/bag.jpg"],
model="embed-v4.0",
input_type="search_query",
embedding_types=["float"]
)
text_emb = response.embeddings.float_[0]
image_emb = response.embeddings.float_[1]
# Store in your vector database and searchAmazon Bedrock Knowledge Bases
AWS managed RAG service that ingests documents and images into a vector store for retrieval-augmented search. Integrates with S3, OpenSearch, and foundation models for grounded search within the AWS ecosystem.
Fully managed RAG pipeline within the AWS ecosystem that connects S3 data sources directly to foundation models with automatic chunking, embedding, and retrieval -- no external services required.
Strengths
- +Deep AWS ecosystem integration with S3, Lambda, and OpenSearch
- +Managed ingestion pipeline with automatic chunking and embedding
- +Multiple foundation model options for generation
- +IAM-based access control for enterprise security
Limitations
- -Limited to text and document modalities, weak image search
- -No video or audio content understanding
- -AWS vendor lock-in for the full pipeline
- -Less flexible retrieval strategies than specialized search platforms
Real-World Use Cases
- •Enterprise chatbots that answer questions using internal documents stored in S3 buckets
- •Compliance search systems scanning regulatory filings and policies for relevant clauses
- •Internal knowledge management where employees search across company wikis, PDFs, and reports
Choose This When
When your data already lives in AWS (S3, RDS, etc.) and you want a managed RAG pipeline without leaving the AWS ecosystem.
Skip This If
When you need true multimodal search across video, audio, and images, or when you want to avoid cloud vendor lock-in.
Integration Example
import boto3
client = boto3.client("bedrock-agent-runtime")
response = client.retrieve(
knowledgeBaseId="KB-ABC123",
retrievalQuery={"text": "what is our vacation policy"},
retrievalConfiguration={
"vectorSearchConfiguration": {"numberOfResults": 5}
}
)
for result in response["retrievalResults"]:
print(result["content"]["text"][:200])OpenAI Embeddings + Responses API
OpenAI's embedding models for text and images combined with the Responses API for grounded search. Supports file search over uploaded documents with automatic chunking and vector storage.
Seamless integration with the broader OpenAI ecosystem (GPT-4o, Responses API, file search) for teams already building on OpenAI who want search without adding new vendors.
Strengths
- +High-quality text embeddings with text-embedding-3 models
- +Built-in file search in the Responses API handles chunking and retrieval
- +Wide developer adoption and extensive documentation
- +Image understanding through GPT-4o vision capabilities
Limitations
- -No unified multimodal embedding space for cross-modal search
- -File search limited to text documents, no image or video indexing
- -No self-hosted option for data-sensitive applications
- -Per-token pricing can escalate with large corpora
Real-World Use Cases
- •Chatbots with document grounding that search uploaded PDFs and text files for accurate answers
- •Rapid prototyping of semantic search features using OpenAI embeddings with a managed vector store
- •Customer support automation searching through product documentation and knowledge base articles
Choose This When
When you are already using OpenAI APIs and want to add semantic search to your application with minimal additional complexity.
Skip This If
When you need true cross-modal search (image-to-text, video search), self-hosted deployment, or advanced retrieval strategies like hybrid search and re-ranking.
Integration Example
from openai import OpenAI
client = OpenAI()
# Generate embeddings for multimodal comparison
response = client.embeddings.create(
model="text-embedding-3-large",
input=["red sports car on a mountain road"],
dimensions=1024
)
embedding = response.data[0].embedding
# Use with your vector database for similarity search
print(f"Embedding dimension: {len(embedding)}")Frequently Asked Questions
What is multimodal search?
Multimodal search enables querying across different content types through a unified interface. You can search for images using text descriptions, find videos matching an image, or search documents using audio clips. It works by embedding all content types into a shared vector space where similarity can be measured.
How is multimodal search different from traditional search?
Traditional search matches keywords within a single content type. Multimodal search understands the semantic meaning of content across types, enabling cross-modal queries. For example, typing 'golden retriever playing in snow' returns matching images and video clips, even without those exact tags.
What are the key challenges in building multimodal search?
The main challenges are aligning embeddings across modalities so that similar concepts in text and images map to nearby vectors, handling the computational cost of processing video and audio at scale, and managing the complexity of multi-stage retrieval pipelines. Platforms like Mixpeek address these challenges as managed infrastructure.
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