iVedha Managed Elasticsearch Platform
Deploy Elasticsearch and Kibana from Azure Marketplace into your own Azure subscription, then use supported interfaces to connect data, manage access, and operate the platform with iVedha.
This documentation guides service owners, Azure administrators, network and identity teams, Elastic administrators, and data users from evaluation through day-two operations.
The platform runs in your Azure subscription
You retain ownership of the Azure subscription, infrastructure, data, and Elastic license. iVedha provides and supports the managed service. Opsflw supplies deployment and lifecycle automation. The exact operational scope depends on your approved Marketplace plan and service agreement.
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Customers control the Azure subscription, connectivity, identity, and data. iVedha operates the supported platform through Opsflw automation and managed interfaces. A monitoring agent in the managed resource group forwards platform telemetry to the iVedha monitoring cluster for health monitoring and operational response.
What the platform is
The iVedha Managed Elasticsearch Platform is an Azure Managed Application that deploys a pre-engineered Elasticsearch and Kibana environment on Azure Kubernetes Service. Azure creates an application resource for customer-facing status and lifecycle actions and a managed resource group for the supporting platform infrastructure.
The service is intended for teams that need an Elasticsearch platform without designing and operating the entire Azure and Elastic foundation themselves. Depending on the approved service scope, iVedha can manage platform administration, health monitoring, supported upgrades and patching, performance and capacity review, security configuration, troubleshooting, and lifecycle operations.
This is not Elastic Cloud hosted outside your environment. It is also not an unmanaged Kubernetes package that customers must maintain directly. Review the architecture and deployment model and shared responsibility before purchasing or deploying.
What you can use it for
| Workload | Typical outcome | Start here |
|---|---|---|
| Search | Index and query application, product, knowledge, or other document collections | Onboard search data |
| Observability | Collect logs, metrics, traces, and events from applications and infrastructure | Onboard observability data |
| Analytics | Explore indexed operational or business data with Elasticsearch and Kibana | Search and visualize data |
| AI-ready search and operations | Use eligible vector, inference, machine-learning, or AI-assisted features after version, license, and configuration checks | What AI-ready means |
AI-ready does not mean that every AI model or Elastic feature is enabled in every plan. Verify the deployed version, Elastic license, topology, optional machine-learning capacity, connectivity, and data-governance requirements before committing to an AI use case.
Understand who manages what
| Party | Primary responsibilities |
|---|---|
| Customer | Azure subscription and policy, network connectivity and DNS, Microsoft Entra ID, Elastic licensing, data onboarding, data use, governance, and application-level requirements |
| iVedha | Managed-service support and the platform administration, monitoring, maintenance, upgrades, and troubleshooting included in the approved service |
| Opsflw | Deployment and lifecycle automation used to provision, configure, and reconcile the managed platform |
| Elastic | Elasticsearch and Kibana technology, product behavior, version compatibility, and license-based feature definitions |
Customers use the Azure managed application view, Elasticsearch and Kibana interfaces, and documented support actions. Do not modify infrastructure in the managed resource group unless the application explicitly exposes that change.
Follow the customer journey
- Evaluate the service. Review what the platform provides, responsibilities, and support.
- Plan the deployment. Confirm Marketplace prerequisites, network design, and profiles and capacity.
- Deploy and follow readiness. Use the first-instance tutorial and do not treat Azure acceptance as service readiness.
- Connect and secure access. Choose public or private access, verify TLS, and configure users or Microsoft Entra single sign-on.
- Onboard production data. Select a supported ingestion path, create least-privilege credentials, and test failure and recovery behavior.
- Operate and get support. Monitor health and capacity, use supported maintenance and upgrade workflows, and submit a support request when required.
Choose your role
| Role | Start here |
|---|---|
| Buyer or service owner | Evaluate outcomes, profiles, responsibilities, and support |
| Azure administrator | Prepare and deploy the managed application |
| Network or DNS administrator | Plan public or private connectivity |
| Security or identity administrator | Configure access, roles, and SSO |
| Elastic administrator | Onboard data and operate Elasticsearch |
| Data user | Sign in, search, and visualize data |
Before production use, record the deployed versions, approved support scope, recovery objectives, capacity evidence, credential owners, and escalation route. Check compatibility and release notes before adopting a new feature or version.
For another iVedha application, return to the Opsflw documentation catalog.