A practical guide to running OpenSearch on Google Cloud Platform - from self-managed deployments on GKE and Compute Engine to managed services from Aiven, Instaclustr, and Bonsai.

Google Cloud Platform does not offer a native managed OpenSearch service. Unlike AWS with Amazon OpenSearch Service, GCP has no first-party equivalent. That does not mean OpenSearch on GCP is impractical - it means you have choices, and those choices come with different trade-offs around operational control, cost, and flexibility.

This guide covers every viable path: self-managed clusters on GKE and Compute Engine, managed services from third-party providers, and the architecture decisions that matter regardless of which route you pick.

Why Run OpenSearch on Google Cloud?

Teams running workloads on GCP often need search, log analytics, or observability capabilities that OpenSearch provides. Moving data cross-cloud to AWS just to use Amazon OpenSearch Service introduces latency, egress costs, and compliance headaches. Running OpenSearch within GCP keeps your data close to the applications that produce and consume it.

Several factors make GCP a solid platform for OpenSearch deployments. Sustained-use and committed-use discounts on Compute Engine and GKE often beat equivalent AWS pricing for memory-heavy instance families - and OpenSearch nodes are almost always memory-bound. GCP's network performance is strong, with low-latency inter-zone connectivity that matters for shard replication. If your organization already uses BigQuery, Dataflow, or Pub/Sub, running OpenSearch alongside them avoids the operational complexity of multi-cloud data pipelines.

Self-Managed OpenSearch on GKE

Google Kubernetes Engine is the most common way to run self-managed OpenSearch on GCP. The OpenSearch Kubernetes Operator and the official Helm charts both support GKE as a deployment target.

Using the OpenSearch Kubernetes Operator

The operator handles provisioning, rolling upgrades, scaling, certificate management, and security configuration. Install it via Helm:

helm repo add opensearch-operator https://opensearch-project.github.io/opensearch-k8s-operator/
  helm install opensearch-operator opensearch-operator/opensearch-operator

Then define your cluster through a custom resource:

apiVersion: opensearch.opster.io/v1
  kind: OpenSearchCluster
  metadata:
    name: my-cluster
  spec:
    general:
      version: 2.19.0
      setVMMaxMapCount: true
      serviceName: my-cluster
    dashboards:
      enable: true
      version: 2.19.0
      replicas: 1
    nodePools:
      - component: masters
        replicas: 3
        roles:
          - cluster_manager
        resources:
          requests:
            memory: 4Gi
            cpu: "1"
        persistence:
          emptyDir: {}
      - component: data
        replicas: 3
        roles:
          - data
        resources:
          requests:
            memory: 8Gi
            cpu: "2"
        persistence:
          pvc:
            storageClass: premium-rwo
            accessModes:
              - ReadWriteOnce
            storage: 100Gi

GKE-Specific Considerations

vm.max_map_count: OpenSearch requires vm.max_map_count=262144. The operator's setVMMaxMapCount: true adds a privileged init container to set this. On GKE Standard clusters, this works out of the box. On GKE Autopilot, privileged containers are restricted - you will need to use a DaemonSet with securityContext.privileged: true or switch to GKE Standard for OpenSearch workloads.

Storage classes: Use premium-rwo (SSD-backed Persistent Disks) for data nodes. The default standard-rwo uses balanced persistent disks, which may bottleneck under heavy indexing. Set volumeBindingMode: WaitForFirstConsumer on your StorageClass to ensure disks are provisioned in the same zone as the pod. For read-heavy workloads with large indices, consider hyperdisk-balanced for higher IOPS.

Node pools: Dedicate a GKE node pool to OpenSearch using taints and tolerations. Use n2-highmem or n2d-highmem machine types - OpenSearch nodes benefit from high memory-to-CPU ratios. A n2-highmem-8 (8 vCPU, 64 GB RAM) is a good starting point for production data nodes.

Pod disruption budgets: Define PDBs to prevent Kubernetes from evicting too many OpenSearch pods during node upgrades. The operator creates these automatically, but verify the settings match your availability requirements.

Self-Managed OpenSearch on Compute Engine

For teams that prefer VMs over Kubernetes, Compute Engine provides a straightforward deployment path. This approach gives you full control over the operating system, JVM settings, and network configuration.

Use n2-highmem instances with local SSD or Persistent SSD. A typical three-node production cluster might use n2-highmem-8 instances with 500 GB pd-ssd each. Deploy nodes across three zones in a single region for fault tolerance.

Key configuration steps:

  1. Set vm.max_map_count=262144 in /etc/sysctl.conf
  2. Configure a dedicated data disk mounted at /var/lib/opensearch
  3. Set up an Internal TCP Load Balancer for client traffic
  4. Use instance groups with health checks for automated recovery

The trade-off versus GKE is clear: you own every operational aspect - OS patching, OpenSearch upgrades, backup scripting, and monitoring setup. For teams without Kubernetes expertise, this can actually be simpler. For teams already running GKE, the overhead of managing individual VMs rarely makes sense.

Managed OpenSearch Services on GCP

Three providers offer managed OpenSearch that runs on GCP infrastructure, each with a different model.

Provider Deployment Model GCP Regions Starting Price Key Differentiator
Aiven Fully managed, Aiven-hosted 30+ GCP regions ~$19/mo (dev) Multi-cloud platform, GCP Marketplace billing
Instaclustr Managed, your account or theirs Multiple GCP regions ~$98/node/mo On-prem option, 99.999% SLA available
Bonsai BYOC (your GCP project) Your chosen region Contact sales Runs in your own VPC

Aiven for OpenSearch

Aiven provides fully managed OpenSearch on GCP, available directly through the Google Cloud Marketplace. This means consolidated billing through your GCP invoice - one bill instead of two. Aiven manages the cluster lifecycle: provisioning, patching, backups, and failover. Their multi-cloud capability means you can replicate data between GCP and AWS or Azure clusters if needed.

Aiven tracks upstream OpenSearch versions closely and offers a 99.99% uptime SLA. For teams already using Aiven for Kafka or PostgreSQL on GCP, adding OpenSearch to the same management plane reduces operational surface.

Instaclustr (NetApp)

Instaclustr runs managed OpenSearch on GCP using n2-highmem instances. Available through the GCP Marketplace in a "Run In Instaclustr Account" model, with a "Run In Your Own Account" option also available. Their enterprise SLA reaches 99.999%, which is notably higher than most competitors. Compliance certifications include SOC2, ISO 27001, PCI-DSS, HIPAA, and GDPR - relevant for regulated industries deploying on GCP.

Bonsai

Bonsai takes a Bring Your Own Cloud approach. They deploy OpenSearch infrastructure into a dedicated Cloud Project in your GCP account, connected via VPC peering. You pay GCP for compute and storage, and Bonsai for management. This model gives you full visibility into the underlying infrastructure and lets you use committed-use discounts on the GCP side.

Architecture Decisions for OpenSearch on GCP

Regardless of deployment method, several architecture decisions are specific to GCP.

Networking: Use Private Service Connect or VPC peering to keep OpenSearch traffic off the public internet. If your clients are in multiple VPCs, Shared VPC simplifies connectivity. For GKE deployments, use private clusters with authorized networks.

Backups: OpenSearch snapshots should target a Google Cloud Storage (GCS) bucket. The repository-gcs plugin ships with OpenSearch and supports workload identity for authentication - no service account key files needed.

Monitoring: Export OpenSearch metrics to Google Cloud Monitoring using the Prometheus exporter plugin, or deploy a dedicated monitoring stack (Prometheus + Grafana) alongside your cluster. For log-based monitoring, ship OpenSearch logs to Cloud Logging via Fluent Bit.

Cost optimization: Use committed-use discounts (CUDs) for predictable workloads - 1-year commitments save 37%, 3-year save 55% on Compute Engine. For GKE, enable cluster autoscaler on non-OpenSearch node pools but keep OpenSearch nodes on a fixed-size pool to avoid disruptive scaling.

Summary

Running OpenSearch on Google Cloud is entirely viable, though it requires more deliberate planning than on AWS where a managed service exists natively. Your options come down to three paths:

  • GKE with the OpenSearch Operator is the best option for teams with Kubernetes experience who want version control, flexibility, and cost efficiency. Use premium-rwo storage, n2-highmem machines, and watch out for the vm.max_map_count issue on Autopilot.
  • Managed services from Aiven, Instaclustr, or Bonsai reduce operational burden significantly. Aiven's GCP Marketplace integration makes billing straightforward. Instaclustr suits compliance-heavy environments. Bonsai's BYOC model keeps infrastructure in your account.
  • Compute Engine VMs work for teams that want full control without Kubernetes complexity, but you take on all operational responsibility.

Whichever path you choose, store snapshots in GCS, keep traffic private, and take advantage of GCP's committed-use discounts to bring costs below what you would pay for equivalent managed services on AWS.