Zilliz Cloud
Pinecone
Milvus
Milvus Lite
SemaDB
Actian VectorAI DB
Supabase
Vecstore
Getwebstack
MarsX
Getwebstack is for development teams that implement a lot of different projects. It can help outsourcing companies, accelerators, freelancers, or dev studios to develop fast. It is also for individuals that want to test a technology or an idea for a startup with a quick setup and deployment. Getwebstack provides a complete solution that covers all the technical aspects of a web app. It has an affordable monthly subscription instead of an expensive one-time payment.
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Based on our record, Zilliz Cloud seems to be more popular. It has been mentiond 5 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
By default, it uses Milvus Lite with a local .db file โ no server needed. For production, switch to Milvus standalone/cluster or Zilliz Cloud. - Source: dev.to / 3 months ago
As an engineer designing real-time RAG pipelines, I consistently face the challenge of selecting infrastructure capable of handling massive vector datasets without compromising latency or reliability. My recent evaluation of Zilliz Cloud deployed on AWS revealed several architecturally significant patterns worth sharing. - Source: dev.to / 11 months ago
As an engineer managing AI workloads, Iโve learned that observability isnโt optionalโitโs survival gear. When my team adopted Zilliz Cloud for vector search in our RAG pipeline, we needed granular visibility into latency, memory, and throughput. Prometheus emerged as the logical choice, but integration reveals subtle pitfalls. Hereโs what I discovered deploying this stack. - Source: dev.to / about 1 year ago
As an engineer scaling semantic search systems, Iโve learned that observability separates functional prototypes from production-grade AI. Last quarter, I hit critical bottlenecks in our retrieval-augmented generation pipeline when QPS spiked unexpectedly. The core issue? Our monitoring couldnโt correlate Milvus-based vector search latency with downstream LLM inference. Thatโs when I integrated Zilliz Cloudโs... - Source: dev.to / about 1 year ago
Retrieval-Augmented Generation (RAG) is a game-changer for GenAI applications, especially in conversational AI. It combines the power of pre-trained large language models (LLMs) like OpenAIโs GPT with external knowledge sources stored in vector databases such as Milvus and Zilliz Cloud, allowing for more accurate, contextually relevant, and up-to-date response generation. - Source: dev.to / over 1 year ago
Pinecone - Search through billions of items for similar matches to any object, in milliseconds. Itโs the next generation of search, an API call away.
MarsX - MarsX leverages the power of AI to help users build mobile and web applications using code and no-code technology. MarsX is highly accessible, allowing even non-developers and those with zero building and coding experience to create their own mobile
Milvus - Vector database built for scalable similarity search Open-source, highly scalable, and blazing fast.
Milvus Lite - Pip-install Vector Search for your GenAI Applications
SemaDB - No fuss vector database for AI
Actian VectorAI DB - The portable vector database for AI agents beyond the cloud
