Amazon Nova Multimodal Embeddings: Model Comparison

Amazon Nova Multimodal Embeddings vs Competitors: Which Should You Choose?

Amazon Nova Multimodal Embeddings combined with OpenSearch Service is best for media and entertainment organizations building large-scale video semantic search on AWS, offering a fully managed path to natural language and video-to-video retrieval at $0.00056 per second of video, while Twelve Labs and Google Vertex AI provide stronger out-of-the-box multimodal understanding for teams willing to manage integration outside AWS.

Overview

This article compares the newly detailed AWS solution for multimodal video embeddings at scale — built on Amazon Nova Multimodal Embeddings and Nova Pro — against leading alternatives from Twelve Labs, Google Vertex AI (multimodalembedding@001), and Databricks/LanceDB lakehouse approaches. The comparison is based exclusively on the official AWS blog post that demonstrates processing 792,270 videos (8,480 hours / 30.5M seconds) in 41 hours at a first-year cost of $23,632–$27,328.

The AWS solution moves beyond keyword tagging by generating 1024-dimensional audio-visual embeddings in AUDIO_VIDEO_COMBINED mode, segmenting videos into 15-second chunks, adding taxonomy-based tags via Nova Pro, and indexing them in dual OpenSearch indexes for text-to-video, video-to-video, and hybrid search.

Feature Comparison Table

Detailed Analysis

Worth upgrading? (Nova vs previous approaches) The AWS post does not position this as an upgrade to a prior Nova version but rather as a new reference architecture for building an AI data lake for media workloads. The improvement over traditional manual tagging and keyword search is substantial: the system enables true semantic search that understands the combined audio-visual content.

Key technical decisions include choosing 1024-dimensional embeddings instead of 3072-dimensional ones for 3× storage cost savings “with minimal accuracy impact.” Video segmentation into 15-second chunks was chosen as a balance between capturing scene changes and keeping the number of embeddings manageable. The architecture explicitly handles Bedrock’s 30 concurrent async job limit per account through a job queue and polling mechanism, enabling 19,400 videos processed per hour on 4× c7i.48xlarge Spot instances.

vs the competition Twelve Labs is purpose-built for video understanding and aggregates embeddings from different modalities into a unified semantic search engine. The AWS solution uses Nova’s AUDIO_VIDEO_COMBINED mode to achieve similar multimodal fusion but relies on fixed 15-second chunking and separate Nova Pro tagging. Google’s multimodalembedding@001 model offers straightforward embeddings for video, image, and text within Vertex AI, but the AWS blog demonstrates production-scale ingestion of nearly 800k videos — a scale rarely shown in competitor documentation.

The Databricks/LanceDB approach emphasizes building a true multimodal lakehouse where vectors coexist with raw data, rich metadata, timestamps, and lineage. AWS achieves similar goals using S3 as the data lake, Nova for embedding generation, and OpenSearch for retrieval, but with tighter integration into the AWS ecosystem.

Migration effort Switching to the Nova-based architecture from a keyword-only system requires:

• Reprocessing the entire video corpus through Bedrock’s async multimodal embedding API
• Implementing job queue + polling logic to respect the 30 concurrent job quota
• Setting up dual OpenSearch indexes (k-NN for embeddings, BM25 for tags)
• Updating applications to use hybrid search (70% vector similarity + 30% keyword matching)

Teams already on AWS with large S3 video repositories will find the migration relatively straightforward using the provided architecture. Teams on Google Cloud or using Twelve Labs will face higher migration effort due to data egress and re-embedding costs.

Pricing Comparison

AWS Nova Solution (first year, 792k videos / 8,480 hours)

• One-time ingestion cost: $18,088
  • Nova Multimodal Embeddings: $17,096 ($0.00056/second × 30.5M seconds)
  • Nova Pro tagging: $571
  • EC2 compute (4× c7i.48xlarge Spot × 41 hours): $421
• Annual OpenSearch Service: $9,240 (on-demand) or $5,544 (Reserved Instances)
• Total first year: $27,328 (on-demand) or $23,632 (Reserved)

The embedding generation cost is dimension-agnostic, making the choice of 1024-dim embeddings purely beneficial for storage and query costs in OpenSearch.

Competitor pricing is not detailed in the source document; organizations should check latest official pricing for Twelve Labs, Vertex AI multimodal embeddings, and Databricks/LanceDB.

Use Case Recommendations

Best for startups Startups with video content should evaluate Twelve Labs first for its specialized video understanding capabilities. The AWS solution’s $23k–$27k first-year cost for 8,480 hours may be high for smaller collections, though the per-second pricing becomes attractive at scale.

Best for enterprise media & entertainment Large media companies already on AWS with petabyte-scale video archives in S3 will find the Nova + OpenSearch architecture highly compelling. The demonstrated ability to process 792k videos in 41 hours and the predictable cost structure make it suitable for production deployment.

Best for hybrid cloud or Google Cloud users Teams deeply invested in Google Cloud should consider Vertex AI’s multimodalembedding@001 for simpler integration, despite the lack of a published 800k-video benchmark in the source material.

Best for data platform teams Organizations prioritizing a true multimodal lakehouse with rich metadata, versioning, and unified analytics should look at Databricks + LanceDB rather than a pure embedding + search architecture.

Price/Performance Verdict

The $0.00056 per second batch pricing for Nova Multimodal Embeddings delivers acceptable performance for semantic video search when combined with OpenSearch. The total first-year cost of approximately $23.6k–$27.3k for ingesting 8,480 hours of video is reasonable for enterprises but represents a significant investment for smaller organizations.

The pricing is justified for workloads that heavily leverage the hybrid search capabilities and already operate within AWS. The 3× storage savings from 1024-dim embeddings and the ability to use Spot instances for compute help control costs. However, the fixed concurrency limit of 30 async jobs per account creates an architectural bottleneck that required custom queueing logic — something competitors may or may not impose.

Migration and upgrade recommendations:

• Must upgrade: Large AWS-based media archives currently using only keyword or manual metadata search.
• Wait and see: Teams not yet at multi-terabyte video scale or those outside the AWS ecosystem.
• Skip it: Organizations needing deep temporal reasoning or complex video event understanding beyond semantic similarity, where Twelve Labs’ specialized models may provide better results.

The Nova Multimodal Embeddings solution is a strong, production-validated option for building an AI data lake specifically for media and entertainment workloads within AWS.

Sources

• Multimodal embeddings at scale: AI data lake for media and entertainment workloads
• The Rise of the Multimodal Lakehouse
• Get multimodal embeddings | Generative AI on Vertex AI
• Mastering Multimodal AI for Advanced Video Understanding | Databricks Blog
• Building a multimodal lakehouse for AI | dbt Labs

All technical specifications, pricing, and benchmark data in this article are sourced directly from official announcements. Competitor comparisons use publicly available data at time of publication. We update our coverage as new information becomes available.