Zilliz Cloud
Pinecone
Milvus
Milvus Lite
SemaDB
Actian VectorAI DB
Supabase
Vecstore
Cachely.dev
nxCloud
Cachely is the managed self-hosted remote cache for Nx and Turborepo - the cache backend you'd otherwise build and run yourself, hosted for you on Cloudflare's edge (R2). It's a drop-in replacement for a DIY @nx/s3-cache / S3 bucket setup: point your build tool at Cachely with a token and two environment variables, and share build cache across CI and every developer's laptop.
Unlike a self-hosted cache, Cachely enforces read-only tokens at the API, so pull-request and fork builds can read but never write - closing the Nx cache-poisoning attack (CVE-2025-36852). It adds ROI reporting (the real build minutes and dollars the cache saved), per-tool insights, and build-optimization suggestions on top.
Pricing is a flat per-workspace subscription with no per-seat fees - add every developer, bot, and CI actor without watching the bill. Cachely never stores your source code; it caches only task outputs and their content hashes. Nx and Turborepo today; Bazel on the roadmap.
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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 / 5 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 / about 1 year 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.
nxCloud - nxCloud is a commercial OwnCloud provider
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