Milvus Hosting – Scalable Vector Database for AI Applications

Infotronics Integrators (I) Pvt. Ltd 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.

  • Linux, Windows 10/11
  • Remote Access – Full access via SSH or RDP
  • Built for Performance – Powered by NVIDIA GPUs and high-speed NVMe SSDs
  • Private & Isolated – Your vector data is never shared or exposed
  • Free 24/7/365 Expert Online Support

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

  • 32GB RAM
  • 4-Core E3-1230 @3.20 GHz
  • (4 Cores & 8 Threads)
  • 120GB SSD + 960GB SSD
  • 100Mbps-1Gbps Bandwidth


    • OS: Windows / Linux

  • 1 Dedicated IPv4 IP
  • No Setup Fee




    • Basic Dedicated Server - SSD

  • 64GB RAM
  • 8-Core E5-2670 @2.60 GHz
  • (8 Cores & 16 Threads)
  • 120GB SSD + 960GB SSD
  • 100Mbps-1Gbps Bandwidth


    • OS : Windows / Linux

  • 1 Dedicated IPv4 IP
  • No Setup Fee




    • Professional Dedicated Server - SSD

  • 128GB RAM
  • 16-Core Dual E5-2660 @2.20
       GHz
  • (16 Cores & 32 Threads)
  • 120GB SSD + 960GB SSD
  • 100Mbps-1Gbps Bandwidth


    • OS : Windows / Linux

  • 1 Dedicated IPv4 IP
  • No Setup Fee




    • Advanced Dedicated Server - SSD

  • 256GB RAM
  • 24-Core Dual E5-2697v2 @2.70
       GHz
  • (24 Cores & 48 Threads)
  • 120GB SSD + 2TB SSD
  • 100Mbps-1Gbps Bandwidth


    • OS : Windows / Linux

  • 1 Dedicated IPv4 IP
  • No Setup Fee

    • Enterprise GPU Dedicated Server - RTX A6000

  • 256GB RAM
  • GPU: Nvidia Quadro RTX A6000
  • Dual 18-Core E5-2697v4
  • (36 cores & 72 threads)
  • 240GB SSD + 2TB NVMe +
         8TB SATA
  • 100Mbps-1Gbps


    • OS: Windows / Linux
    Single GPU Specifications:

  • Microarchitecture: Ampere
  • CUDA Cores: 10,752
  • Tensor Cores: 336
  • GPU Memory: 48GB GDDR6
  • FP32 Performance: 38.71
        TFLOPS



    • Enterprise GPU Dedicated Server - A100

  • 256GB RAM
  • GPU: Nvidia A100
  • Dual 18-Core E5-2697v4
  • (36 cores & 72 threads)
  • 240GB SSD + 2TB NVMe +
         8TB SATA
  • 100Mbps-1Gbps


    • OS: Windows / Linux
    Single GPU Specifications:

  • Microarchitecture: Ampere
  • CUDA Cores: 6912
  • Tensor Cores: 432
  • GPU Memory: 40GB HBM2
  • FP32 Performance: 19.5
        TFLOPS


    • Enterprise GPU Dedicated Server - A100(80GB)

  • 256GB RAM
  • GPU: Nvidia A100
  • Dual 18-Core E5-2697v4
  • (36 cores & 72 threads)
  • 240GB SSD + 2TB NVMe +
         8TB SATA
  • 100Mbps-1Gbps


    • OS: Windows / Linux
    Single GPU Specifications:

  • Microarchitecture: Ampere
  • CUDA Cores: 6912
  • Tensor Cores: 432
  • GPU Memory: 80GB HBM2e
  • FP32 Performance: 19.5
        TFLOPS


    • Enterprise GPU Dedicated Server - H100

  • 256GB RAM
  • GPU: Nvidia H100
  • Dual 18-Core E5-2697v4
  • (36 cores & 72 threads)
  • 240GB SSD + 2TB NVMe +
         8TB SATA
  • 100Mbps-1Gbps


    • OS: Windows / Linux
    Single GPU Specifications:

  • Microarchitecture: Ampere
  • CUDA Cores: 14,592
  • Tensor Cores: 456
  • GPU Memory: 80GB HBM2e
  • FP32 Performance: 183
        TFLOPS


    • 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!

    AI Search Engines

    AI Search Engines

    Text/ image/ audio similarity search, RAG.

    Recommendation Systems

    Recommendation Systems

    Product, content and user recommendation.

    Face & Object Recognition

    Face & Object Recognition

    Facial authentication, biometric ID.

    E-Commerce

    E-Commerce

    Reverse image search, semantic product search.

    Healthcare

    Healthcare

    Medical image retrieval, diagnosis support.

    Finance

    Finance

    Fraud detection, anomaly detection.

    Smart Devices

    Smart Devices

    Voice assistants, photo classification.

    LLM Integration

    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:

    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 Infotronics Integrators (I) Pvt. Ltd

    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.
    Yes, Milvus is free and open-source. It is available under the Apache License 2.0.
    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.
    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
    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.
    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.
    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.
    Yes! Milvus integrates with LangChain, LlamaIndex, Haystack, and other vector-enabled frameworks commonly used in LLM pipelines.
    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.

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