Elasticsearch treated creator vectors like log data, aging 400M+ to frozen storage, pushing search to 25s. A vector-native store fixed it.
One of the world's most widely used AI image and video generation platforms, with 8 million creators building characters and narratives where every past generation becomes reference material for future scenes.
By the time the creator library exceeded several hundred million vectors, Elasticsearch's log-data lifecycle policy had aged most of the corpus to...
“Creators don't make one image and leave. They build a character, a world, a story, and everything they've ever generated becomes material for the next scene. Zilliz Cloud lets us treat that whole library as one creative memory.”
AI creation platform for generating and editing images, videos, and audio.
Vector database platform for building and scaling AI applications.
OpenArt's Asset search is part of this use case:
Related implementations across industries and use cases
Writes and queries fought for the same nodes—safety latency spiked when uploads surged. Milvus separates them, stable at 10B+ vectors.
Messy ingredient data broke text search, capping matches at 20%. AI semantic search now maps free-form inputs to unlock recipe imports.
Each team built its own stack; every request reprocessed thousands of images. A shared platform with precomputed embeddings cut latency 44x.
Each team built its own stack; every request reprocessed thousands of images. A shared platform with precomputed embeddings cut latency 44x.
Moderation couldn't keep pace with 600M users. AI agents now filter toxicity while models recognize 2.5B objects to refine search.
Scattered data and basic coding tools bottlenecked engineers. A 9-agent AI workflow shifts them from writing code to directing AI teams.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.
Elasticsearch treated creator vectors like log data, aging 400M+ to frozen storage, pushing search to 25s. A vector-native store fixed it.
One of the world's most widely used AI image and video generation platforms, with 8 million creators building characters and narratives where every past generation becomes reference material for future scenes.
By the time the creator library exceeded several hundred million vectors, Elasticsearch's log-data lifecycle policy had aged most of the corpus to...
“Creators don't make one image and leave. They build a character, a world, a story, and everything they've ever generated becomes material for the next scene. Zilliz Cloud lets us treat that whole library as one creative memory.”
AI creation platform for generating and editing images, videos, and audio.
Vector database platform for building and scaling AI applications.
OpenArt's Asset search is part of this use case:
Related implementations across industries and use cases
Writes and queries fought for the same nodes—safety latency spiked when uploads surged. Milvus separates them, stable at 10B+ vectors.
Messy ingredient data broke text search, capping matches at 20%. AI semantic search now maps free-form inputs to unlock recipe imports.
Each team built its own stack; every request reprocessed thousands of images. A shared platform with precomputed embeddings cut latency 44x.
Each team built its own stack; every request reprocessed thousands of images. A shared platform with precomputed embeddings cut latency 44x.
Moderation couldn't keep pace with 600M users. AI agents now filter toxicity while models recognize 2.5B objects to refine search.
Scattered data and basic coding tools bottlenecked engineers. A 9-agent AI workflow shifts them from writing code to directing AI teams.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.