ApertureDB

ApertureDB stores embeddings next to the media they describe, so a single query can match text, image, audio, or video vectors without copying data around. The company reports search responses under 10 milliseconds and throughput above 13,000 queries per second. Those figures come from ApertureData's own benchmarks, so test them against your own workload. The practical gain is retrieval that stays fast as the library grows into the billions of objects.

Vector search alone finds lookalikes. ApertureDB adds a knowledge graph so you can traverse relationships between objects and their metadata. That matters for GraphRAG, where an answer depends on how records connect, not just how similar two vectors are. ApertureData says graph lookups run at 15 milliseconds on billion-scale graphs.

The database keeps text, documents, images, video, audio, and binary blobs in their native form. There is no format conversion step and no separate object store to keep in sync. For teams that juggle media files and their metadata, that removes a whole class of pipeline bugs.

Common jobs, like generating embeddings, running object detection, or loading a Jupyter notebook, come as ready workflows and plugins. You wire them into your own pipeline instead of writing the glue code yourself. The goal is to cut the setup that usually eats the first months of a project.

ApertureDB acts as a memory store for AI agents. An agent can commit what it learns, with context attached to each entry, and query that memory on later runs. ApertureData frames this as the step past shallow, text-only agents toward ones that hold onto visual and relational context.

ApertureDB Cloud is fully managed, running on AWS, Google Cloud, or Azure, with tiers from a small team setup up to replicated production clusters. The Custom tier allows a customer-managed deployment and Azure in all US regions. You pick how much control you want and how much you hand off.