Amazon Web Services has quietly rolled out one of the more consequential building blocks in its generative AI arsenal — and it has nothing to do with chatbots. Amazon Nova Multimodal Embeddings, a foundation model now available through Amazon Bedrock, is designed to convert text, images, and video into unified numerical representations that can power search, recommendation, and retrieval-augmented generation systems at enterprise scale. While large language models grab headlines, embeddings models like this one represent the connective tissue that makes modern AI applications actually work.

The model, detailed in a comprehensive technical guide published on the AWS Machine Learning Blog, supports three modalities — text, image, and video — and can produce vector embeddings in configurable dimensions of 256, 512, or 1,024. This flexibility allows developers to balance performance against storage and latency requirements, a critical consideration for production systems handling millions or billions of records. The practical implications are significant: a single model can now understand and relate content across fundamentally different data types, enabling use cases that previously required stitching together multiple specialized systems.

A Single Model to Rule Three Modalities

At its core, Amazon Nova Multimodal Embeddings solves a problem that has bedeviled enterprise search and recommendation systems for years: how to meaningfully compare and retrieve content that exists in different formats. Traditional systems required separate pipelines for text search, image search, and video search, each with its own indexing strategy and retrieval logic. The Nova model collapses these into a shared vector space where a text query can surface relevant images, a video frame can find related documents, and an image can retrieve similar videos — all through the same mathematical framework.

According to the AWS technical guide, the model accepts text inputs of up to 512 tokens, images in formats including PNG, JPEG, GIF, WebP, and BMP with a maximum size of 5 MB, and video inputs up to 30 seconds in length with a 50 MB file size cap. Video processing supports formats such as MP4, MOV, MKV, WebM, FLV, MPEG, MPG, WMV, and 3GP. The model automatically samples frames from video inputs to create representative embeddings, abstracting away what would otherwise be a complex preprocessing pipeline. These specifications reveal a model designed not for research demonstrations but for real-world production workloads where data arrives in heterogeneous formats and at significant volume.

How the Technical Architecture Works Under the Hood

The embedding process itself is invoked through the Amazon Bedrock runtime API, specifically through the invoke_model method. Developers construct a request body specifying the input type — text, image, or video — along with the desired embedding dimension and the content itself. For images and videos, content can be passed either as base64-encoded data or via an S3 URI, the latter being particularly useful for large-scale batch processing workflows. The API returns a JSON response containing the embedding vector, which can then be stored in a vector database for subsequent similarity search operations.

The choice of embedding dimension has meaningful implications for system design. The 1,024-dimension option provides the highest fidelity representation and is best suited for applications where retrieval accuracy is paramount, such as medical image search or legal document retrieval. The 256-dimension option, by contrast, reduces storage requirements by 75 percent and accelerates similarity computations, making it appropriate for high-throughput, latency-sensitive applications like real-time product recommendations. The 512-dimension option occupies a middle ground that AWS suggests is suitable for most general-purpose applications. This tiered approach reflects a mature understanding of the tradeoffs that engineering teams face when deploying AI systems at scale.

Vector Databases and the Retrieval Pipeline

Generating embeddings is only half the equation. The other half involves storing and querying those vectors efficiently, which is where vector databases enter the picture. The AWS guide details integration with Amazon OpenSearch Serverless, which supports approximate k-nearest neighbor (k-NN) search using algorithms like HNSW (Hierarchical Navigable Small World) and FAISS (Facebook AI Similarity Search). These algorithms enable sub-second retrieval across millions of vectors by constructing graph-based index structures that trade a small amount of accuracy for dramatic improvements in query speed.

Setting up the vector store involves creating a collection in OpenSearch Serverless with a “vectorsearch” type, configuring appropriate IAM policies for data access and network security, and creating an index with a field mapped to the knn_vector data type. The index configuration specifies the vector dimension (matching the embedding model’s output), the similarity metric (cosine similarity being the default choice), and the engine (FAISS or NMSLIB). Documents are then ingested into the index as JSON objects containing both the embedding vector and any metadata fields needed for filtering or display. This architecture enables hybrid queries that combine vector similarity with traditional keyword or metadata filters, a capability that is increasingly important for enterprise search applications.

Multimodal Search: Where Text Meets Image Meets Video

The most compelling capability enabled by Amazon Nova Multimodal Embeddings is true cross-modal search. In a demonstration outlined in the AWS guide, a developer can index a collection of images by generating embeddings for each one, then query that index using a natural language text description. The model’s shared vector space ensures that semantically related content clusters together regardless of modality. A text query like “sunset over a mountain lake” will return images depicting that scene, even though the image was never explicitly tagged or described with those words.

This capability extends to video as well. A short video clip of a product demonstration can be embedded and later retrieved using a text query describing the product’s features, or using a still image of the product itself. The implications for media asset management, e-commerce, content moderation, and surveillance are substantial. Organizations that have accumulated vast libraries of unstructured visual content — which is to say, virtually every large enterprise — can now make that content searchable and discoverable without the labor-intensive process of manual tagging and annotation that has historically been required.

Retrieval-Augmented Generation Gets a Multimodal Upgrade

Perhaps the most strategically significant application of multimodal embeddings is in retrieval-augmented generation (RAG) systems. RAG has emerged as the dominant architecture for building enterprise AI applications that need to ground their responses in proprietary data. By retrieving relevant documents, images, or video clips based on a user’s query and feeding them as context to a large language model, RAG systems can produce responses that are both contextually accurate and grounded in factual content. Amazon Nova Multimodal Embeddings extends this pattern beyond text, enabling RAG systems that can retrieve and reason over visual content.

The AWS guide describes a workflow where a user’s question triggers an embedding-based search across a multimodal knowledge base. The retrieved results — which might include technical diagrams, product photos, instructional videos, and text documents — are then passed to a generative model like Amazon Nova or Anthropic’s Claude (both available through Bedrock) to synthesize a comprehensive answer. This multimodal RAG pattern is particularly powerful for industries like manufacturing, healthcare, and engineering, where critical knowledge is often encoded in visual formats that traditional text-based RAG systems cannot access.

Benchmarks, Pricing, and Competitive Positioning

AWS has not published extensive third-party benchmark comparisons for Nova Multimodal Embeddings, but the model’s specifications position it competitively against alternatives like OpenAI’s CLIP, Google’s multimodal embedding models available through Vertex AI, and Cohere’s Embed v3. The key differentiator for AWS is integration depth: Nova Multimodal Embeddings works natively with Amazon Bedrock, OpenSearch Serverless, Amazon S3, and the broader AWS ecosystem, reducing the integration overhead that often plagues multi-vendor AI architectures.

Pricing follows Bedrock’s standard on-demand model, with costs calculated per input token for text and per image or per second for visual content. For organizations already committed to the AWS ecosystem, the operational simplicity of using a first-party embeddings model — with unified billing, IAM-based security, and native service integrations — represents a significant advantage over assembling a best-of-breed stack from multiple providers. The model is currently available in the US East (N. Virginia) region, with broader regional availability expected to follow.

What This Means for Enterprise AI Strategy

The release of Amazon Nova Multimodal Embeddings reflects a broader shift in the enterprise AI market away from monolithic model deployments and toward composable AI architectures. In these architectures, specialized models — for embedding, generation, classification, and other tasks — are orchestrated together to build applications that are more capable, more efficient, and more controllable than any single model could be on its own. Embeddings models are the foundation of this composable approach, providing the semantic understanding layer that connects raw data to intelligent applications.

For enterprise architects and AI engineering teams, the practical takeaway is clear: multimodal embeddings should be evaluated as a core infrastructure component, not an afterthought. The ability to unify text, image, and video into a single searchable vector space has implications that extend well beyond search — into knowledge management, compliance monitoring, customer experience personalization, and operational intelligence. As the volume of unstructured multimodal data continues to grow exponentially, the organizations that invest in robust embedding and retrieval infrastructure today will be best positioned to extract value from that data tomorrow.

Amazon’s approach with Nova Multimodal Embeddings is characteristically pragmatic: rather than chasing benchmark leaderboards, AWS has focused on building a model that integrates cleanly into existing workflows, scales predictably, and addresses the real-world constraints — dimension flexibility, format support, storage optimization — that determine whether an AI capability actually gets deployed in production. For the growing community of builders constructing AI-powered applications on AWS, this model represents a significant new tool in an increasingly sophisticated toolkit.