Why AWS’s S3 Vectors Reveal a New Vector Storage Playbook
Amazon Web Services claims its S3 Vectors service cuts vector storage and query costs by up to 90% compared to specialized vector databases. With 2 billion vectors per index and 20 trillion vectors per bucket now supported, AWS is scaling vector storage beyond most rivals. But this move isn’t just about cost savings—it rethinks how enterprises leverage vector data at scale.
AWS’s new vector capabilities native to Amazon S3 enable firms to store and query vectors without transferring data between systems. This is a critical shift away from reliance on costly, specialized products like Pinecone and Weaviate, which maintain purpose-built databases with low-latency optimizations but at a high price.
However, AWS positions S3 Vectors as complementary rather than replacement technology, emphasizing use case-driven performance tiering. High-throughput, low-latency workloads still require dedicated vector databases, while large-scale, latency-tolerant semantic search and agent memory systems thrive on the economics and scale of S3.
“Cost reduction is powerful, but the real breakthrough is eliminating data movement between storage and compute,” says Constellation Research analyst Holger Mueller.
Contrary to the hype: This isn’t just about cutting costs
It’s commonly assumed that AWS aims to displace specialized vector databases entirely. Analysts often interpret this as a straightforward cost war. They’re missing the finesse of constraint repositioning. By embedding vector capabilities into S3’s massively durable, scalable object storage, AWS changes the infrastructure baseline enterprises build on, sidestepping performance bottlenecks imposed by data silos.
This move echoes the stepwise commoditization of vector search seen in hybrid cloud. Like the evolution of tabular data handling—where transactional systems coexist with data lakes—vectors gain a new role as a storage-native data type. Enterprises can architect vector workflows with a mix of latency tiers rather than “all or nothing.” This parallels how legacy enterprises deploy multiple databases based on workload, a nuance often glossed over in vector database debates.
Effective leverage in this tiered approach comes from avoiding duplicate data pipelines and reaping cloud-native economics on massive vector indexes.
How AWS’s scale and integration create new leverage points
S3 Vectors now handles billions of vectors in a single index with query latencies around 100 milliseconds for frequent queries, with write throughput up to 1,000 PUTs per second. This positions AWS not just as a storage vendor but a vector service platform seamlessly integrated with Amazon Bedrock for generative AI workflows.
Unlike competitors such as Pinecone, which optimize for millisecond latencies serving high concurrent queries, AWS attacks the cost and scale constraints that fragment vector datasets across multiple systems. By enabling consolidations up to 20 trillion vectors per bucket, customers like March Networks economically store billions of video and photo intelligence embeddings.
These scale and economic advantages tilt the architectural decision from multiple siloed vector stores to a layered system. Lower latency tiers lean on purpose-built databases while large, cold, or batch workloads turn to S3 Vectors—mirroring established data lake patterns.
This strategic layering unleashes compound leverage by streamlining data movement and enabling new composite AI apps that span high-scale storage and fast retrieval.
Who wins as vector databases become a feature, not a product
Enterprise architects now face a clear vector storage decision framework shaped by workload latency tolerances and scale economics. Organizations with existing AWS investments gain compelling incentives to absorb vector workloads into S3, reducing costly infrastructure sprawl.
Yet latency-sensitive use cases—recommendation engines, interactive search, synchronous user experiences—retain demands only dedicated vector databases satisfy. This bifurcation doesn’t spell the end for players like Weaviate or Qdrant; rather, it forces them to sharpen their differentiation on extreme performance and real-time updates.
This dynamic resembles the broader cloud native trend where “suites always win,” but best-of-breed components earn their place through unique constraints. AWS cements vector storage’s transition from a standalone disruptive product to a commoditized cloud-native feature, dramatically shifting cost structures and operational designs.
“The new vector baseline eliminates data silos and taps AWS’s storage scale—no data movement, big win for enterprise AI,” notes Gartner’s Ed Anderson.
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Frequently Asked Questions
What is AWS S3 Vectors and how does it differ from traditional vector databases?
AWS S3 Vectors is a service integrated into Amazon S3 that enables storage and querying of vectors without moving data between systems. Unlike traditional vector databases focused on low-latency, S3 Vectors supports massive scale with up to 20 trillion vectors per bucket and emphasizes cost efficiency over extreme low-latency performance.
How much cost savings can AWS S3 Vectors provide compared to specialized vector databases?
AWS claims that S3 Vectors can reduce vector storage and query costs by up to 90% compared to specialized vector databases, making it an economical solution for large-scale vector workloads.
What scale of vector data can AWS S3 Vectors handle?
S3 Vectors supports up to 2 billion vectors per index and 20 trillion vectors per bucket, enabling enterprises to store and manage vector data at scales much larger than most rival services.
Does AWS S3 Vectors replace dedicated vector databases entirely?
No, AWS positions S3 Vectors as a complementary technology. High-throughput, low-latency workloads still require specialized vector databases, while S3 Vectors is ideal for latency-tolerant, large-scale workloads benefiting from cost savings and scale.
What is the impact of eliminating data movement between storage and compute with AWS S3 Vectors?
Eliminating data movement between storage and compute reduces performance bottlenecks and operational complexity. This integration allows for more efficient AI workflows by tapping into AWS’s massive storage scale without costly data transfers.
How does AWS S3 Vectors integrate with other AWS AI services?
S3 Vectors is seamlessly integrated with Amazon Bedrock, AWS's generative AI platform, allowing customers to build AI workflows that combine massive vector storage with generative AI capabilities.
Which companies or use cases benefit most from AWS S3 Vectors?
Organizations with large-scale vector workloads, such as March Networks storing billions of video and photo embeddings, benefit greatly. Use cases include semantic search, agent memory systems, and other large, latency-tolerant vector searches.
What does the future of vector storage look like with AWS S3 Vectors?
Vector storage is shifting from standalone disruptive databases to a commoditized, cloud-native feature embedded in object storage. This shift enables enterprises to architect layered storage solutions by mixing purpose-built databases for low-latency needs with S3 Vectors for scale and economics.