Ingesting vector data into S3 Vector using AWS CLI or SDKs requires using the new s3vectors service namespace and the PutVectors API operation. With the AWS CLI, you use commands like aws s3vectors put-vectors to submit vector data, while SDKs provide language-specific methods through dedicated S3 Vector clients. Before ingestion, you must have a vector bucket and vector index already created with the appropriate configuration (dimension size, distance metric, and metadata settings). The ingestion process involves formatting your vector data as JSON objects containing unique keys, float32 arrays for vector data, and optional metadata key-value pairs.
Using the AWS SDK for Python (Boto3) as an example, you first create an S3 Vectors client with boto3.client('s3vectors'), then prepare your vector data in the required format. Each vector object must include a unique key, the vector data as a dictionary with a ‘float32’ key containing the numerical array, and optional metadata. For instance: {'key': 'doc-1', 'data': {'float32': [0.1, 0.2, 0.3, ...]}, 'metadata': {'title': 'Sample Document', 'category': 'research'}}. You can batch multiple vectors in a single put_vectors() call for better performance, with each batch containing up to the service limits for optimal throughput.
The ingestion process includes error handling and validation to ensure data quality and compatibility. The service validates that vector dimensions match the index configuration, keys are unique within the index, and metadata follows the supported data types (string, number, boolean, list). Common errors include dimension mismatches, duplicate keys, or metadata that exceeds size limits. Best practices include generating embeddings using consistent models and parameters, including meaningful metadata for future filtering, implementing retry logic for batch operations, and monitoring ingestion metrics through CloudWatch. For large-scale ingestion, consider processing data in parallel batches and using exponential backoff for rate limiting. The service provides strong consistency, meaning ingested vectors are immediately available for similarity searches without waiting for background processing.
Will Amazon S3 vectors kill vector databases or save them?
S3 vectors looks great particularly in terms of price and integration into the AWS ecosystem. So naturally, there are a lot of hot takes. I’ve seen folks on social media and in engineering circles say this could be the end of purpose-built vector databases—Milvus, Pinecone, Qdrant, and others included. Bold claim, right?
As a group of people who’s spent way too many late nights thinking about vector search, we have to admit that: S3 Vectors does bring something interesting to the table, especially around cost and integration within the AWS ecosystem. But instead of “killing” vector databases, I see it fitting into the ecosystem as a complementary piece. In fact, its real future probably lies in working with professional vector databases, not replacing them.
Check out James’ post to learn why we think that—looking at it from three angles: the tech itself, what it can and can’t do, and what it means for the market. We’ll also share S3 vectors’ strenghs and weakness and in what situations you should choose an alternative such as Milvus and Zilliz Cloud.
Will Amazon S3 Vectors Kill Vector Databases—or Save Them?
Or if you’d like to compare Amazon S3 vectors with other specialized vector databases, visit our comparison page for more details: Vector Database Comparison