AI Search Engines
Text/image/audio similarity search, RAG
Milvus Hosting – Scalable Vector Database for AI Applications
Database Mart offers optimized cloud environments to host Milvus – the open-source vector database designed for AI, LLMs, and similarity search at scale. Run Milvus on High-Performance GPU Servers with Simple Setup.
Choose Your Milvus Hosting Plans
Discover Milvus Hosting, the scalable vector database designed for AI applications. Enhance your data management and accelerate your AI projects today.
Express Dedicated Server - SSD
$ 49.00/mo
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Order Now- CPU: 4-Core E3-1230
- Memory: 32GB RAM
- Disk: 120GB SSD + 960GB SSD
- Bandwidth: 100Mbps Unmetered
- IP: 1 Dedicated IPv4
- Location: USA
Professional Dedicated Server - SSD
$ 65.40/mo
40% OFF (Was $109.00)
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Order Now- CPU: 16-Core Dual E5-2660
- Memory: 128GB RAM
- Disk: 120GB SSD + 960GB SSD
- Bandwidth: 100Mbps Unmetered
- IP: 1 Dedicated IPv4
- Location: USA
Basic Dedicated Server - SSD
$ 51.35/mo
35% OFF (Was $79.00)
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Order Now- CPU: 8-Core E5-2670
- Memory: 64GB RAM
- Disk: 120GB SSD + 960GB SSD
- Bandwidth: 100Mbps Unmetered
- IP: 1 Dedicated IPv4
- Location: USA
Advanced Dedicated Server - SSD
$ 84.50/mo
50% OFF (Was $169.00)
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Order Now- CPU: 24-Core Dual E5-2697v2
- Memory: 256GB RAM
- Disk: 120GB SSD+2TB SSD
- Bandwidth: 100Mbps Unmetered
- IP: 1 Dedicated IPv4
- Location: USA
Enterprise Dedicated GPU Server - RTX A6000
$ 409.00/mo
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Order Now- GPU Model: RTX A6000
- CPU: 36-Core Dual E5-2697v4
- Memory: 256GB RAM
- Disk: 240GB SSD+2TB NVMe+8TB SATA
- Bandwidth: 100Mbps Unmetered
- GPU Memory: 48 GB GDDR6
- IP: 1 Dedicated IPv4
- Location: USA
Enterprise Dedicated GPU Server - A100
$ 399.50/mo
50% OFF (Was $799.00)
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Order Now- GPU Model: A100
- CPU: 36-Core Dual E5-2697v4
- Memory: 256GB RAM
- Disk: 240GB SSD+2TB NVMe+8TB SATA
- Bandwidth: 100Mbps Unmetered
- GPU Memory: 40 GB HBM2
- IP: 1 Dedicated IPv4
- Location: USA
Enterprise Dedicated GPU Server - A100(80GB)
$ 1559.00/mo
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Order Now- GPU Model: A100(80GB)
- CPU: 36-Core Dual E5-2697v4
- Memory: 256GB RAM
- Disk: 240GB SSD+2TB NVMe+8TB SATA
- Bandwidth: 100Mbps Unmetered
- GPU Memory: 80 GB HBM2e
- IP: 1 Dedicated IPv4
- Location: USA
Enterprise Dedicated GPU Server - H100
$ 2099.00/mo
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Order Now- GPU Model: H100
- CPU: 36-Core Dual E5-2697v4
- Memory: 256GB RAM
- Disk: 240GB SSD+2TB NVMe+8TB SATA
- Bandwidth: 100Mbps Unmetered
- GPU Memory: 80 GB HBM2e
- IP: 1 Dedicated IPv4
- Location: USA
8 Typical Use Cases of Milvus Hosting
Milvus is widely adopted by companies, researchers, and developers building AI-native applications, especially those requiring vector similarity search. Below are some of the main groups and organizations using Milvus!
Recommendation Systems
Product, content, and user recommendation
Face & Object Recognition
Facial authentication, biometric ID
E-Commerce
Reverse image search, semantic product search
Healthcare
Medical image retrieval, diagnosis support
Finance
Fraud detection, anomaly detection
Smart Devices
Voice assistants, photo classification
LLM Integration
Vector store for embedding-based search (RAG)
Milvus System and Hardware Requirements
Here are the system and hardware requirements for running Milvus, the high-performance vector database, based on official documentation and best practices for production.
Milvus comes in three main versions:
- Milvus Lite - As a lightweight version of Milvus, it is ideal for quick prototyping in Jupyter Notebooks or running on smart devices with limited resources.
- Milvus Standalone – It is a single-machine server deployment. All components of Milvus Standalone are packed into a single Docker image, making deployment convenient.
- Milvus Distributed – It offers the highest scalability and availability, as well as the flexibility in customizing the allocated resources in each component.
Below are the minimum and recommended requirements:
| Component | Minimum Specs | Recommended Specs |
|---|---|---|
| OS | Ubuntu 20.04+, CentOS 7, macOS (dev only) | Ubuntu 22.04 LTS |
| CPU | 4 cores | 8–16 cores (for indexing/searching large datasets) |
| RAM | 8 GB | 32 GB+ for general workloads, 64 GB+ for large-scale deployments or high QPS |
| Storage | 100 GB SSD | 1 TB+ NVMe SSD for performance and durability |
| GPU | Not required to run Milvus itself | Recommended GPUs:NVIDIA RTX A6000, A100, or A40 for batch embedding, CUDA toolkit if using GPU-accelerated Faiss indexing |
| Docker | Docker 20.10+ and Docker Compose required | Latest stable |
| Others | Docker Compose, Python, Open ports: 19530 (Milvus), 9091 (metrics), etc. | High-speed internal LAN for multi-node setups, Monitoring + object storage |
Milvus vs ChromaDB vs Qdrant
Here’s a clear, detailed comparison of Milvus, ChromaDB, and Qdrant — three leading vector databases designed for similarity search and AI-native applications.
| Feature / Capability | Milvus | ChromaDB | Qdrant |
|---|---|---|---|
| Overview | High-performance vector DB optimized for scale and cloud-native deployments | Lightweight vector DB focused on simplicity and integration with LLM apps | Scalable vector search engine with rich filtering, payload support |
| Main Use Case | Production-grade vector search at scale | Prototyping, local LLM apps, embeddings | LLM RAG apps, hybrid filtering, real-time search |
| Performance | Very fast indexing & search, supports HNSW, IVF, and GPU-accelerated Faiss | Good for small to mid-scale apps | Fast, low-latency search with filtering and quantization |
| Data Storage | On-disk + in-memory hybrid (RocksDB or S3 backend) | In-memory (optional persistence via duckdb) | On-disk, SSD-optimized |
| Scalability | Excellent – supports cluster mode (via etcd, Pulsar, MinIO) | Limited – mostly local or dev use | Good – horizontal scaling and clustering support |
| Vector Index Types | IVF, HNSW, GPU-accelerated Faiss, DiskANN | Only HNSW (simplified options) | HNSW, PQ, SQ, Flat, Binary support |
| Filtering Support | Yes (limited in early versions, now improving) | Basic (few metadata filters) | Rich filtering (metadata + payload) |
| Hybrid Search (text + vector) | Basic support with reranking logic | None (unless you build it) | Excellent (filtering + scoring hybrid) |
| Language Bindings | Python, Java, Go, REST, C++ | Python (built for LangChain, LlamaIndex) | Python, REST, gRPC, TypeScript |
| Deployment Options | Docker, K8s, Bare Metal, Cloud | Local (pip install chromadb) | Docker, K8s, Cloud |
| GPU Support | ✅ Yes (optional Faiss GPU acceleration) | ❌ No | ❌ No (CPU only) |
| Open Source License | Apache 2.0 | Apache 2.0 | Apache 2.0 |
| Monitoring & Observability | Prometheus/Grafana integration | No native support | Prometheus-compatible metrics |
| Ease of Use | Medium (complex setup for cluster) | Very easy (pip install, Python-native) | Easy with Docker/K8s |
| Community & Ecosystem | Large (by Zilliz, backed by LF AI) | Growing, LangChain/LlamaIndex focus | Active, with REST/gRPC SDKs & docs |
How to Get Started with Milvus on Database Mart
Deploy Milvus on dedicated server or dedicated GPU Server in minutes. Reference link - How to Run Milvus Lite Locally
Choose Your Plan – Select a GPU or CPU server tailored to your workload
Receive Access – Login credentials delivered via email
Deploy Models or Vectors – Upload your dataset, embeddings, and start querying
Go Live – Your Milvus instance is ready for real-time vector search
FAQs of Milvus Hosting
The most commonly asked questions about Vector Database hosting with Milvus below.
What is Milvus?
Milvus is an open-source vector database designed to manage embedding data generated by AI models. It supports fast similarity search and is ideal for use cases like semantic search, recommendation engines, and Retrieval-Augmented Generation (RAG) with LLMs.
Is Milvus free?
Yes, Milvus is free and open-source. It is available under the Apache License 2.0.
Why host Milvus on a GPU server?
Milvus can leverage GPU acceleration (e.g., via Faiss or IVF-PQ) for faster vector indexing and search performance. Hosting on a GPU server improves latency and throughput, especially for high-dimensional or large-scale datasets.
Who should use Milvus Hosting?
Milvus Hosting is perfect for:
1. AI developers working with embeddings,
2. Teams building LLM-based RAG systems,
3. Startups deploying search and recommendation engines,
4. Researchers testing vector similarity algorithms at scale
1. AI developers working with embeddings,
2. Teams building LLM-based RAG systems,
3. Startups deploying search and recommendation engines,
4. Researchers testing vector similarity algorithms at scale
Do I need a GPU to run Milvus?
No, GPU is optional. You can run Milvus entirely on CPU, but GPU hosting significantly accelerates vector indexing and search, especially for large-scale or high-throughput applications.
How do I connect my application to the hosted Milvus instance?
We provide connection credentials via REST or gRPC. You can use Milvus Python SDK (pymilvus) or any compatible client to interact with your vector DB.
Is ChromaDB or Qdrant better than Milvus?
Each has pros and cons:
Milvus: Best for production, large-scale, and GPU-accelerated search. Rich feature set.
ChromaDB: Lightweight, easy to use locally, integrated with LangChain but lacks GPU support.
Qdrant: Fast, Rust-based engine, excellent REST APIs, CPU-optimized.
If you need maximum scalability, GPU support, or advanced indexing — Milvus is the best fit.
Milvus: Best for production, large-scale, and GPU-accelerated search. Rich feature set.
ChromaDB: Lightweight, easy to use locally, integrated with LangChain but lacks GPU support.
Qdrant: Fast, Rust-based engine, excellent REST APIs, CPU-optimized.
If you need maximum scalability, GPU support, or advanced indexing — Milvus is the best fit.
Can I use Milvus with LangChain or LlamaIndex?
Yes! Milvus integrates with LangChain, LlamaIndex, Haystack, and other vector-enabled frameworks commonly used in LLM pipelines.
What is the difference between Milvus Lite and Milvus?
Milvus Lite is recommended for smaller datasets, up to a few million vectors. Milvus Standalone is suitable for medium-sized datasets, scaling up to 100 million vectors. Milvus Distributed is designed for large-scale deployments, capable of handling datasets from 100 million up to tens of billions of vectors.