How to Seamlessly Deploy Milvus for Scalable and Reliable AI Search

The purpose of this post is to inform readers about the basics of vector databases, Milvus' architecture and features, and how to deploy the vector database with Docker, Kubernetes, with Helm, or through a managed service. This article also includes a section on how to manage production deployments with CI/CD and carry out vector search operations efficiently; therefore, it provides best practices and troubleshooting advice as well as techniques for designing scalable, robust AI Search workloads.

Understanding Vector Databases

Vector databases.are purpose-built systems designed to store, manage, and retrieve vector embeddings with high speed and accuracy. By leveraging mathematical distance calculations, optimized indexing strategies, and low-latency search, they enable efficient similarity matching at scale. These capabilities allow teams to evaluate and improve the performance, accuracy, and reliability of the AI-driven applications they develop.

What is a Vector Database?

A vector database is a specialized system used to store vector embeddings and perform efficient vector search using Approximate Nearest Neighbor (ANN) algorithms. In testing environments, vector databases enable validation of similarity search accuracy, search relevance, and response consistency, rather than relying on simple exact-match queries.

Vector databases are commonly used in testing to:

• Validate AI and ML model outputs
• Test semantic search relevance and ranking
• Perform performance and load testing on AI search systems
• Support automated regression testing for recommendation engines
• Validate Retrieval-Augmented Generation (RAG) responses

Key Features of Milvus

The Milvus vector database is an open source vector database built for scale and performance.

Key capabilities include:

• Advanced vector search capabilities
• Multiple index types and optimized index building
• Support for Milvus Standalone, Milvus Cluster, Milvus Distributed, and Milvus Lite
• Scalable distributed systems architecture
• Native Milvus SDK support
• Logical data organization via Milvus Collection
• Enterprise-grade Milvus architecture using query nodes, data nodes, and meta store
• Robust metadata storage and metadata management

Benefits of Using Open Source Vector Databases

Using an open source vector database offers flexibility, transparency, and cost efficiency.

Benefits include:

• Lower total cost compared to proprietary vector search database platforms
• Community-driven innovation and a strong developer community
• Freedom to deploy as free vector databases or managed services
• Compatibility with Generative AI and OpenAI embedding models

Setting Up Milvus

Effective deployment of Milvus necessarily requires extensive planning and execution with regards to setup, architectural model, and application configurations. Milvus' deployment methods offer multiple choices based on use-case scenarios; whether you want to use Milvus for testing artificial intelligence workloads, validating semantic search capabilities, or leveraging vector search capacity at scale, Milvus has the proper deployment options available to meet those requirements.

Milvus Installation Options

Milvus supports multiple Milvus installation models:

By choosing the appropriate setup,teams have the ability to:

• Assess vector search effectiveness and efficiency
• Assess the validity of results provided by machine learning (ML) & Recommender Algorithms
• Automate the Deployment & Scale Process via Continuous Integration and Continuous Delivery (CI/CD).
• Deliver Consistent Trustworthiness of AI-based Workloads Under Real-World High-Volume Workloads.

Integrate with other services such as Alibaba Cloud's Container Service for Kubernetes (ACK), Amazon's Simple Storage Service (S3) and Aurora Database as well as its Serverless offering, ECS Fargate, and Virtual Private Cloud (VPC).

Step-by-Step Installation Guide for different types of  Milvus 

1. Milvus Standalone

Use Case: Local testing and development
Description: Single-node deployment, easy to install, ideal for testing similarity search and AI queries.

Steps:

1. Download Milvus Standalone:Visit Milvus Standalone Releases and download the appropriate package.
2. Start Milvus:./milvus run standalone
3. Verify Deployment:Access Milvus using the default port (19530 for gRPC or 19121 for HTTP).
4. Connect via Python SDK:

from pymilvus import MilvusClient
client = MilvusClient(uri="tcp://localhost:19530")

2. Milvus Docker

Use Case: Testing using containers. Lightweight Deployment is a Use Case for the Milvus Project.
Description: Created with Docker Compose to make deployment in isolated environments very easy.

Steps:

1. Install Docker and Docker Compose
2. Download Milvus Docker Compose File: curl -O https://github.com/milvus-io/milvus/releases/download/v2.2.9/docker-compose.yml
3. Start Milvus with Docker Compose: docker-compose up -d
4. Check Running Containers: docker ps
5. Connect Using SDK or REST API for testing vector search and similarity queries.

3. Milvus Distributed

Use Case : High Availability Production Grade Workload.
Description:-  Milvus is deployed o multi node arrangements to handle large datasets and heavy AI workloads.

Steps:

1. Set up multiple nodes (VMs or cloud instances)
2. Install dependencies: Docker, Docker Compose, and Kubernetes if using hybrid mode
3. Download Distributed Deployment Configs:git clone https://github.com/milvus-io/milvus.git

          cd milvus/deployments/distributed

4. Start Distributed Milvus:docker-compose -f docker-compose.yml up -d
5. Verify Cluster Health:Ensure all services (QueryNode, DataNode, IndexNode, Proxy, etc.) are running.

4. Milvus Operator (Kubernetes)

Use Case : Cloud Native, Scalable, Automated Management OF THE MILVUS SQL DATABASE.
Description :- MIlvus operator for automatic scaling and upgrading of milvus(Native to Kubernetes)

Steps:

1. Install cert-manager (for secure communication):kubectl apply -f https://github.com/jetstack/cert-manager/releases/download/v1.5.3/cert-manager.yaml
2. Install Milvus Operator:kubectl apply -f https://raw.githubusercontent.com/zilliztech/milvus-operator/main/deploy/manifests/deployment.yaml

5. Zilliz Cloud

Use Case: Fully-managed cloud deployment

Description: Hosted Milvus service that removes infrastructure management overhead.

Steps:

1. Sign Up for Zilliz Cloud: https://zilliz.com/cloud
2. Create a Milvus Cluster via the web console
3. Configure Cluster Settings:
   
   • Choose instance size.
   • Enable storage (S3, NFS, etc.)
   • Configure networking and security
4. Access Cluster via the provided Endpoints (gRPC, HTTP, or SDK).
5. Integrate with CI/CD and Testing Workflows:
   
   • Run automated vector search tests
   • Perform regression tests on AI models
   • Monitor performance and scaling via Zilliz Cloud dashboard

Key Notes for Testing & Production:

• Standalone → Best for quick local testing
• Docker → Easy containerized testing, isolated environments
• Distributed → Production with high availability
• Operator → Kubernetes-native, automated scaling, CI/CD ready
• Zilliz Cloud → Managed solution, minimal operational overhead

H3-Deploying Milvus on Kubernetes

Milvus Operator (Kubernetes)

1. The Milvus Operator is a Kubernetes-native, cloud-ready deployment and management solution for managing the lifecycle of your Milvus Clusters. It provides automated scaling, upgrades, and Maintenance for production-grade AI workloads and development/testing environments.
   To deploy your Milvus Cluster on Kubernetes (The two main components of the Operator).
   STEPS:-
   1)Install the cert-manager.
   2)By using cert-manager, you can set up secure communication between all components of your cluster.
   3)Deploy the Operator.
   4)Create a Milvus Cluster by using available YAML configurations or creating your own custom configuration.
   5)Check to ensure that Pods and Services are up and running in your cluster.
   6)Port forward to access Milvus locally for testing.

Use the Python SDK to connect to perform operations such as creating collections, inserting vectors, and performing testing against similarity search or semantic search features of a product.

By using this methodology, it provides a scalable, reliable, and production-quality environment for vector search deployments while also supporting Quality Assurance (QA), Continuous Integration/Continuous Delivery (CI/CD), and Performance Testing

Steps:

1. Deploy a Milvus Cluster:Deploy a default Milvus cluster using the operator

kubectl apply -f

https://raw.githubusercontent.com/zilliztech/milvus-operator/main/config/samples/milvus_cluster_default.yaml

2. Verify Cluster

kubectl get milvus my-releasekubectl get pods

Customizing the cluster:

Replace YAML files to adjust resource allocation, storage, or node configurations

Use the Milvus Sizing Tool to optimize cluster resources for production workloads

3. Port Forwarding for Access

kubectl get pod <YOUR_MILVUS_PROXY_POD> --template='{{(index (index .spec.containers 0).ports 0).containerPort}}{{"\n"}}'

kubectl port-forward --address 0.0.0.0 service/my-release-milvus <LOCAL_PORT>:<SERVICE_PORT>

This allows local testing of vector search, similarity search, and semantic search queries.

4. Connect Using Python SDK for testing: Install PyMilvus to interact with the cluster

pip install pymilvus

Example Python script:

from pymilvus import MilvusClient
client = MilvusClient(uri="http://localhost:<LOCAL_PORT>")
collection_name = "example_collection"
if client.has_collection(collection_name):
    client.drop_collection(collection_name)
client.create_collection(
    collection_name=collection_name,
    dimension=768
)

Managing Milvus in Production

Once the deployment of Milvus has been completed, a business utilizes Milvus for its production systems. The appropriate management of Milvus will provide businesses with an excellent uptime level, reliable vector searches, and consistent workloads for AI algorithm development and execution.

Configure and Optimize

Configuration of the Milvus system is critical when working with an extensive amount of vector data and thus having fast search query performance:
Appropriately configure resources (CPU, memory, GPU) based on work tasks
Create and configure distributed deployments of query node(s), data node(s), and index nodes
Select the storage options that match the size/volume of the data being utilized (NFS, S3, Local Storage)
Use the Milvus Sizing Tool to optimize cluster resources for AI algorithm testing and production work loads.

CI/CD for Milvus Deployments

Integrating Milvus with your CI/CD pipeline provides fast, reliable updates and testing:
Utilize Kubernetes manifests or Helm charts to automate the deployment(s) and scaling(s) of your clusters
Execute automated regression tests to verify the accuracy of vector searches and the results of AI algorithm model generation and validation.

Ensure performance testing for similarity search, semantic search, and RAG workloads
Maintain version consistency across clusters for production and testing environments

Common CI/CD services include:

• Jenkins
• GitLab CI
• GitHub Actions
• Azure DevOps
• AWS CodePipeline

Helm for Managing Kubernetes Applications

Using Helm, you can manage deployments of your applications (Kubernetes Applications) in an easier manner and achieve repeatable results by packaging up all your Milvus Application configurations into Helm Charts.
Manage upgrades, rollbacks, and environment-specific configurations for you. You can combine your Helm charts with your CI/CD Pipeline to create a full automated system for managing your applications.
Ensure every cluster works the same for all your development, staging and production environments.

Key workflows:

• Install Helm
• Use helm template for validation
• Apply production-ready helm commands

Common helm commands:

• helm install
• helm upgrade
• helm rollback
• helm uninstall

Vector Search with Milvus

Vector Searches are the primary function of the Milvus platform. With a Vector Search, you can perform similarity searches, semantic searches and other AI assisted methods against arbitrary amounts of unstructured data. Proper testing and optimization are important to providing high-quality results and the reliability of AI workflows.

Understanding Vector Search

Instead of finding exact matches, Vector Searches locate items that are similar based on meaning and/or features from the vector embedding. Therefore, a Vector Search can use Approximate Nearest Neighbor (ANN) algorithms for fast return of results. Vector Searching powers many applications including Semantic Searching, Recommendation Systems, Image Recognition and Retrieval-Augmented Generation (RAG) and all Quality Assurance (QA) and Dev Ops teams to check that their models are accurate, that the search results are relevant and the responses are consistent.

Querying Data in Milvus

Milvus enables querying through its Python SDK, REST API, or gRPC endpoints:

• Create collections and insert vector embeddings for testing
• Run similarity search queries to measure search relevance
• Test response times and performance under load
• Evaluate AI model outputs for semantic search and RAG tasks

Example Python query snippet:

from pymilvus import MilvusClient
client = MilvusClient(uri="tcp://localhost:19530")
results = client.search(collection_name="example_collection", query_vectors=[vector], top_k=5)

Integrating Milvus with Other Databases

Milvus can work alongside relational or NoSQL databases to enrich AI applications:

• Combine MongoDB vector database or Chroma vector database with Milvus for hybrid workloads
• Store metadata in traditional databases while keeping vectors in Milvus
• Test end-to-end workflows in AI pipelines and recommendation engines
• Ensure data consistency, query accuracy, and performance across systems

Best Practices and Troubleshooting

Effectively managing Milvus requires proactive monitoring, optimization, and troubleshooting. Following best practices ensures high availability, accurate vector search, and reliable AI workloads

Common Issues and Solutions

Milvus may face slow queries, pod crashes, inconsistent results, or deployment errors. Fixes include optimizing indexes, adjusting resources, verifying metadata, and validating YAML/configurations.

Monitoring and Maintenance

Proactive monitoring ensures cluster health and performance:

• Use Kubernetes tools (kubectl, dashboards) to track pod and node health
• Monitor CPU, memory, and GPU usage for query and index nodes
• Schedule regular backups of collections and metadata
• Perform re-indexing or optimize partitions for large-scale workloads
• Integrate with CI/CD pipelines to catch issues during updates or scaling

Future Trends in Vector Databases

• Increasing adoption of Generative AI and large language models driving more complex queries
• Integration with cloud-managed services like Zilliz Cloud for fully hosted Milvus deployments
• Advancements in ANN algorithms for faster and more accurate similarity search
• Greater automation in CI/CD pipelines, monitoring, and scaling to support enterprise workloads

Conclusion

Utilizing tools such as Docker, Kubernetes, Helm, CI/CD, etc. provides organisations with an efficient and reliable way to deploy Milvus in a scalable manner as part of a performance based Artificial Intelligence (AI) search solution. Solutions like Milvus Operator and Zilliz Cloud allow organisations to streamline the management of Milvus and facilitate the process of continuously testing and updating Milvus via CI/CD pipelines/Helm charts. By employing industry best practices and optimising their respective configurations, organisations employing Milvus will be able to obtain accurate vector, semantic and similarity search results across large (multiple million records) datasets.

how BuildNexTech can help 

BuildNexTech helps organizations implement these strategies effectively for AI applications, recommendation systems, and RAG workflows by utilizing their expertise to deploy, manage, and optimize Milvus for each specific use case.