Writes and queries fought for the same nodes—safety latency spiked when uploads surged. Milvus separates them, stable at 10B+ vectors.
An AI image and video generation platform with over 100 million users across 224 countries, part of Kuaishou, one of the world's largest short-video and live-streaming platforms with over 400 million daily active users.
As the workload climbed toward tens of billions of vectors, Elasticsearch's shared read/write nodes caused latency spikes in content safety and risk...
AI platform for cinema-grade video and image generation.
Vector database platform for building and scaling AI applications.
Kling AI's Semantic search is part of this use case:
Related implementations across industries and use cases
Elasticsearch treated creator vectors like log data, aging 400M+ to frozen storage, pushing search to 25s. A vector-native store fixed it.
Safety reads and high-volume writes fought for the same nodes. Milvus disaggregates them: 10B+ vectors, latency steady under load.
Messy ingredient data broke text search, capping matches at 20%. AI semantic search now maps free-form inputs to unlock recipe imports.
Safety reads and high-volume writes fought for the same nodes. Milvus disaggregates them: 10B+ vectors, latency steady under load.
A phone launch, a breaking story, a Vietnamese phrase — keyword search came up short. Semantic retrieval now covers 92% of AI 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.
Writes and queries fought for the same nodes—safety latency spiked when uploads surged. Milvus separates them, stable at 10B+ vectors.
An AI image and video generation platform with over 100 million users across 224 countries, part of Kuaishou, one of the world's largest short-video and live-streaming platforms with over 400 million daily active users.
As the workload climbed toward tens of billions of vectors, Elasticsearch's shared read/write nodes caused latency spikes in content safety and risk...
AI platform for cinema-grade video and image generation.
Vector database platform for building and scaling AI applications.
Kling AI's Semantic search is part of this use case:
Related implementations across industries and use cases
Elasticsearch treated creator vectors like log data, aging 400M+ to frozen storage, pushing search to 25s. A vector-native store fixed it.
Safety reads and high-volume writes fought for the same nodes. Milvus disaggregates them: 10B+ vectors, latency steady under load.
Messy ingredient data broke text search, capping matches at 20%. AI semantic search now maps free-form inputs to unlock recipe imports.
Safety reads and high-volume writes fought for the same nodes. Milvus disaggregates them: 10B+ vectors, latency steady under load.
A phone launch, a breaking story, a Vietnamese phrase — keyword search came up short. Semantic retrieval now covers 92% of AI 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.