Milvus vs. self-managed Milvus - Vector Retrieval Service for Milvus

This topic compares Vector Retrieval Service for Milvus (Milvus) with self-managed Milvus on ECS and self-managed Milvus on ACK in terms of cost, capabilities, production O&M, and selection.

Cost comparison

Item	Milvus	Self-managed Milvus on ECS	Self-managed Milvus on ACK
Resource cost	You can create a Milvus instance with one click and do not need to bear additional resource costs for components such as Kafka and etcd. Auto scaling is supported. You can flexibly change node specifications and the number of nodes.	You must purchase Alibaba Cloud ECS instances and components such as Kafka and etcd. In addition, technical experts must manually build and maintain the Kubernetes environment. You may face ECS supply chain issues during traffic peaks, and idle resources during off-peak hours may cause resource waste.	You can reuse an existing ACK (managed Kubernetes) cluster and deploy Milvus by using Helm or an Operator, which saves you the work of building your own Kubernetes cluster. You still need to deploy and maintain Milvus itself, its dependency components such as Kafka, etcd, and object storage, and multi-replica resources on your own.
Network fees	Communication over the internal network within the same region is free of charge. Milvus provides free Internet access. Communication over the Internet across regions incurs no traffic fees.	Communication over the internal network within the same region is free of charge. Communication over the Internet across regions requires you to pay Internet traffic fees. For more information about billing, see Public bandwidth billing.	Communication over the internal network within the same region is free of charge. Communication over the Internet across regions requires you to pay Internet traffic fees.
Labor and time	Milvus is fully managed, ready to use out of the box, and billed on a pay-as-you-go basis. GUI-based cluster O&M and management capabilities keep O&M and management costs extremely low.	You must purchase instances and manually build Milvus clusters. Iteration is slow and time costs are high. You must manage resources and perform cluster O&M on your own, which requires a team of professional Milvus engineers and results in high labor costs.	With Kubernetes container orchestration and rolling upgrades, deployment and scaling are more efficient than on ECS alone. You still need a dedicated team with O&M expertise in both Kubernetes and Milvus, which results in relatively high labor costs.
Risk cost	The cloud service guarantees 99.9% service reliability, which minimizes IT risks and keeps risks to upper-layer business under control.	Self-managed Milvus clusters have no minimum guarantee. Strong technical capabilities and significant investment are required to reduce business risks.	No official SLA is provided. Availability depends on the architecture built by your team and its Kubernetes and Milvus O&M and disaster recovery capabilities.
After-sales cost	Alibaba Cloud provides after-sales technical support.	No after-sales technical support team is available. You must troubleshoot issues on your own.	No official Milvus after-sales support is available. You must rely on the community and your own team to troubleshoot issues.

Capability comparison

Item	Milvus	Self-managed Milvus on ECS	Self-managed Milvus on ACK
Ease of use	Ready to use out of the box with elastic scaling. You can flexibly adjust cluster configurations with one click. You can upgrade the cluster version with one click. The console provides more than 100 monitoring metrics and supports custom alert rules and alerting services.	Deployment is complex, and resources must be adjusted manually. You must ensure the stability of the cluster kernel version and the upgrade process on your own. O&M is difficult. You must run commands to check the health status of clusters, nodes, and indexes. Only basic monitoring capabilities are available. Complex metrics require secondary development.	ACK container orchestration, elastic scheduling, and rolling upgrades reduce part of the deployment complexity. You still need to build version compatibility verification, monitoring and alerting, health checks, and slow query analysis on your own (by using Prometheus, Grafana, SLS, and so on).
Scenario capabilities	Retrieval capabilities: Retrieval is optimized for the Sparse and Binary data types, delivering several times the performance of the open source version. AI capabilities: Seamlessly compatible with frameworks such as LangChain and LlamaIndex, and integrated with Alibaba Cloud AI products such as Platform for AI (PAI)-EAS and Qwen, helping you build RAG solutions on the cloud with one click.	You must develop the capabilities on your own or integrate open source capabilities.	You must develop the capabilities on your own or integrate open source capabilities.
Performance	Provides more powerful capabilities such as ANN algorithms and sparse query algorithms, multiplying performance in areas such as cluster reads and writes and vector retrieval.	You must ensure performance on your own, which is technically difficult to implement.	You can optimize by using Kubernetes resource scheduling and affinity policies, but advanced performance architectures such as read/write splitting and storage-computing separation must still be implemented on your own.
Availability	Data and service reliability reaches 99.9%. Provides visual data management capabilities and multi-replica component services for high availability.	You must back up data manually and ensure cluster availability on your own. Disaster recovery deployment is technically difficult to implement. You must call APIs to manually build and maintain the deployment on your own.	You can achieve partial high availability by using Kubernetes multi-replica deployments, anti-affinity, and probes. You must still design backup and restoration and cross-zone or cross-region disaster recovery on your own and conduct drills regularly.
Security	Supports encrypted HTTP transmission and data storage encryption. By default, clusters are accessed in a virtual private cloud (VPC), and cluster access policies based on Resource Access Management (RAM) are provided. The data storage system provides a multi-replica redundancy mechanism within the same city to ensure normal access to business data. Alibaba Cloud RAM and the RBAC (Role-Based Access Control) authentication mechanism together form a multi-layer security framework that ensures secure user access.	Only basic ECS security protection is available, and cluster security risks are high. You must purchase cloud security products on your own to isolate and control incoming IP addresses. You must manually install third-party security plug-ins to implement authentication and authorization.	You must harden security on your own: VPC and security group network isolation, RAM or third-party authentication, Milvus RBAC, and transmission and storage encryption must all be configured by yourself.

Production O&M comparison

In addition to differences in resources and features, the managed service and self-managed Milvus also differ significantly in SLA, backup, upgrades, monitoring, fault diagnosis, and O&M collaboration.

O&M area	Milvus	Self-managed Milvus on ECS	Self-managed Milvus on ACK
SLA and availability	Milvus provides service level guarantees based on the purchased instances and applicable conditions, and is responsible for operating and maintaining the managed platform, server-side components, and underlying dependencies. Multi-zone deployment can further improve recovery capabilities in the event of a zone failure. The specific scope and metrics are subject to the Service Level Agreement.	The open source Milvus software itself does not provide a business-oriented SLA. Overall availability depends on the capabilities of cloud resources such as ECS, storage, and network, as well as on the self-built architecture, on-call system, and fault recovery capabilities. Business teams usually need to define and guarantee internal SLOs on their own.	ACK provides service level guarantees for the Kubernetes control plane, but the Milvus software itself does not provide a business-oriented SLA. Overall availability depends on the self-built Milvus architecture, multi-replica and anti-affinity scheduling, on-call system, and fault recovery capabilities. Business teams still need to define and guarantee internal SLOs on their own.
Backup and restoration	Provides manual backup in the console, automatic backup, snapshot management, and data restoration. For production use, we recommend that you configure backup times and retention policies based on your business RPO and RTO and regularly verify restoration results. Queries may be temporarily affected during restoration. After restoration is complete, you must reload collections as required by the product.	You must deploy and maintain backup tools on your own and coordinate the consistency of Milvus data, metadata, object storage, and related dependencies. You must also manage backup storage, retention periods, version compatibility, restoration processes, and regular drills on your own.	You must deploy backup tools (such as milvus-backup) on your own and coordinate the consistency of Milvus data, metadata, object storage, and dependencies. You can use Kubernetes volume snapshots and CronJobs to orchestrate backup tasks, but backup storage, retention periods, version compatibility, restoration processes, and regular drills must still be managed on your own.
Version upgrades	Provides product-verified versions and an upgrade entry, and performs server-side upgrade orchestration and platform health checks. We recommend that you back up data, verify SDK and application compatibility before upgrading, and perform upgrades during off-peak hours. Upgrade paths between different kernel versions are subject to the latest documentation.	You can choose versions and upgrade times independently, but you must verify the compatibility of Milvus and dependency components on your own, plan the upgrade sequence, data migration, downtime windows, and rollback plans, and handle exceptions during the upgrade.	You can perform declarative upgrades by using Helm or an Operator and reduce downtime through rolling updates, but you must verify the compatibility of Milvus and dependency components on your own, plan the upgrade sequence, data migration, and rollback plans, and handle exceptions during the upgrade.
Monitoring and alerting	Provides metrics such as node liveness, CPU, memory, storage, QPS, failure rate, P99 latency, throttling, slow queries, and data volume, and allows you to configure alerts by using CloudMonitor. You can also ship instance runtime logs to Simple Log Service (SLS) for centralized query and analysis.	You must deploy Prometheus, Grafana, logging, and alerting systems on your own and cover the complete chain, including Milvus components, etcd, object storage, messaging services, Kubernetes, ECS, and network.	You can reuse the Prometheus, Grafana, and log services integrated with ACK to quickly build a monitoring foundation, but you must still implement metric collection, alert rules, and slow query analysis for Milvus components, etcd, object storage, messaging services, and so on.
Fault diagnosis	You can diagnose issues by using console metrics, CloudMonitor, runtime logs, and technical support. For managed platform and server-side exceptions, Alibaba Cloud provides troubleshooting and recovery support. Issues related to application calls, SDKs, schemas, index parameters, access permissions, and business traffic can be analyzed jointly by the business team together with application logs.	You must perform end-to-end troubleshooting on your own, from applications, SDKs, proxies, coordinator components, and compute nodes to etcd, object storage, messaging services, Kubernetes, ECS, and network. This places high demands on the team's technical breadth and emergency response capabilities.	Kubernetes control plane and node exceptions can be handled by using ACK cluster diagnostics and technical support. However, end-to-end Milvus troubleshooting, from applications, SDKs, proxies, coordinator components, and compute nodes to etcd, object storage, and messaging services, must still be performed by your own team.
O&M responsibility boundaries	A shared responsibility model is adopted. Alibaba Cloud is responsible for the managed infrastructure, the Milvus server, and product O&M capabilities. Business teams mainly focus on data usage, schema and index design, application integration, access policies, alert response, capacity targets, and business continuity. Both parties can collaborate through monitoring, logs, and technical support processes to resolve production incidents end to end.	Apart from the service capabilities provided by underlying cloud products such as ECS, storage, and network, the deployment, configuration, upgrade, backup, monitoring, capacity, security, and fault recovery of Milvus clusters and their dependency components are mainly planned and maintained by your own team.	A shared responsibility model that lies between the two. Alibaba Cloud is responsible for the O&M of the Kubernetes control plane, node pools, and underlying cloud resources. The deployment, configuration, upgrade, backup, monitoring, capacity, security, and fault recovery of Milvus clusters and their dependency components are mainly planned and maintained by your own team by using Helm or an Operator.

Production scenarios better suited to the managed service

The following production scenarios are usually better suited to Milvus:

Online business with clear availability requirements

Interruptions to online applications such as RAG, semantic search, recommendation, intelligent customer service, and multimodal retrieval directly affect the end-user experience. The managed service provides productized high availability, monitoring and alerting, backup and restoration, and technical support, which reduces the impact of database infrastructure failures on your business.

Fast launch with less investment in underlying O&M

If your team wants to focus engineering resources on models, data, and application effectiveness, or currently lacks dedicated engineers in areas such as Milvus, Kubernetes, etcd, object storage, and messaging systems, the managed service can reduce the work of cluster deployment and dependency component maintenance.

Standardized upgrade, backup, and restoration processes

For production systems that require regular upgrades, automatic backups, visual restoration, and operation records, Milvus provides console-based O&M capabilities that help establish unified and repeatable change management and data protection processes.

Rapid growth in data and traffic

The number of vectors, dimensions, index types, and access traffic continuously affect computing and storage requirements. For scenarios with fast business growth, significant traffic fluctuations, or frequent capacity adjustments, the resource calculation, specification change, and elastic scaling capabilities of the managed service can reduce the complexity of capacity management.

Requirements for monitoring, auditing, and security governance

Milvus works with Alibaba Cloud capabilities such as CloudMonitor, Simple Log Service, ActionTrail, RAM, VPC, and KMS, making it suitable for enterprise scenarios that require unified account management, network isolation, alert notifications, audit retention, and data protection.

Database capabilities shared by multiple teams

When multiple business teams or applications share a vector database, the console, RAM, and Milvus RBAC capabilities of the managed service help centrally manage instances, users, roles, permissions, and O&M processes.

Continuous product capabilities and technical support

The managed service continuously provides version evolution, platform optimization, and productized O&M capabilities. For teams that want to avoid tracking open source component versions, vulnerabilities, and compatibility changes on their own, the managed model can reduce long-term maintenance costs.

Selection recommendations

The managed service and self-managed Milvus each have their own applicable scenarios:

If your business values fast launch, stability, backup and restoration, monitoring and alerting, security governance, and technical support, and you want to reduce the complexity of underlying O&M, we recommend that you evaluate Milvus first.
If your business must run in a fully offline or specific private environment, requires modifying the Milvus source code, pinning a specific version, or deeply customizing the deployment topology, and you already have a mature platform and database O&M team, you can evaluate a self-managed solution based on your actual situation.

Performance and cost are affected by factors such as vector scale, dimensions, index type, filter conditions, QPS, recall targets, and update frequency. We recommend that you validate candidate solutions with representative business data and comprehensively evaluate resource costs, O&M investment, availability targets, and long-term evolution costs.

Vector Retrieval Service for Milvus:Milvus vs. self-managed Milvus