Ollama on GKE Autopilot

Ollama is an open-source LLM inference server that serves large language models — Llama, Mistral, Gemma, Phi, and others — via a REST API. This module deploys Ollama on GKE Autopilot on top of the App_GKE foundation, which provisions and manages the shared Google Cloud and Kubernetes infrastructure.

This guide focuses on the cloud services Ollama uses and how to explore and operate them from the Google Cloud Console and the command line. For the mechanics that are common to every GKE application — Workload Identity, ingress, autoscaling, CI/CD, Cloud Armor, IAP, Binary Authorization, VPC Service Controls, backups, and the deployment lifecycle — refer to the App_GKE foundation guide rather than repeating them here.

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What Ollama costs on RAD, and how that compares

Ollama on RAD's GKE Autopilot module costs about US$323–396 a month, with the module fee charged once in RAD credits. GKE Autopilot suits Ollama that must stay up, scale across pods or run beside other Kubernetes workloads; for the lowest cost, the Cloud Run module runs the same Ollama for about US$85 a month. If you only need it occasionally — a demo, a seasonal project, something you're evaluating — a RAD-managed project can be deleted and restored within 30 days for a handful of credits (under US$1) instead of paying for it to sit idle. See Pause it for free, below. Figures are as at 8 October 2026; sources are listed at the end of this section.

What you pay on RAD

RAD deploys Ollama, which is open source, so there is no licence fee per user. You pay for three things:

	In a Google Cloud project you own	In a project RAD manages for you
Module fee, once per deployment	300 credits (US$30 at the top-up price)	270 credits (10% lower)
Build time	About 3-6 credits per build	The same
Google Cloud running cost	Billed by Google to your own billing account, about US$323 a month for the default configuration (table below) — more if this isn't your only Autopilot cluster	Metered hourly in credits; RAD publishes 180 credits a day, about 5,400 a month (about US$540 at the top-up price, US$432 at the Scale plan's rate)
Who owns what	You own the project and its billing; RAD deploys and updates it	RAD owns the project, with guardrails, quotas and budget alerts; you get console access, and a minimum purchased balance is held in reserve

10 credits cost US$1 on a one-off top-up, and less on a monthly plan. In a project RAD manages, the database, file server and network are shared by every application in the project, so a second application does not add a second set of them.

Default running cost in your own project (us-central1, Google list prices):

Resource (module default)	Per month
GKE Autopilot pod, 8x vCPU / 16 GiB	US$317.38
Cloud NAT and networking	US$5
Cloud Storage (add-ons, backups)	US$1
GKE cluster management fee	shared across every GKE app in the project; $0 if this is your only cluster, else ~73
Total	about US$323 (about US$396 if this isn't your only Autopilot cluster)

How it compares

• No turnkey managed SaaS was verified for Ollama itself, so the honest comparison is a self-managed server: a Hetzner CPX22 (2 vCPU/4 GB) costs about $24/month, a DigitalOcean 2 vCPU/4 GB Droplet about $24/month, and a GCP Compute Engine e2-standard-2 (2 vCPU/8 GB) about $49/month.
• This needs considerably more CPU and memory than the reference boxes below, so a self-managed equivalent would cost noticeably more than the $24-49/month entry-level class — likely a dedicated high-memory instance.
• There is no managed SaaS that hosts your own Ollama instance the way this module does — a hosted model-inference API is a different product from a private inference server, so it isn't a like-for-like comparison and no figure is given here. The realistic alternative is self-hosting on your own hardware or a cloud VM sized for the models you run.
• The VPS price is not the whole cost. On a bare VPS you are also the one patching the OS, rotating backups and keeping the database (if any) healthy. RAD's managed Cloud SQL, Secret Manager and monitoring cover that for you, which is the real argument for RAD over a cheaper box — not the sticker price.

Pause it for free: delete a RAD-managed project, restore it when you need it

If Ollama runs in a project RAD manages for you, you have a second option that goes well beyond scaling to zero: delete the whole project, and restore it within 30 days for close to nothing. This suits Ollama you only need occasionally — a demo, an evaluation, a seasonal project — far better than running it continuously.

• How it works. Deleting a RAD-managed project unlinks its billing first, then asks Google to delete the project. Google does not remove the project immediately: it keeps it, recoverable, for 30 days. Because billing is already unlinked, nothing is charged while it waits. Unlike deleting one module, this does not tear down the cluster or the compute resource one by one — the whole project, and everything in it, simply stops.
• Restoring costs a few credits, not a rebuild. Within 30 days, the project's owner can restore it. RAD asks Google to undelete the project and reattaches its billing account, then asks you to run Update on each deployment to confirm everything came back. Because nothing was individually destroyed, that Update finds the same resources already there — it is a check, not a rebuild, and an Update never charges the module fee again. That costs roughly a handful of credits (under US$1) in total for a typical 2-3-deployment chain.
• So a month of occasional use can cost a few dollars, not US$323. Deploy Ollama, use it for a while, delete the project. Restore it next time you want it, confirm with Update, and delete it again when you're done. You pay only for the module fee once, the builds, and whatever time Ollama was actually live.
• What this needs. You must own the project (not a bring-your-own one RAD only manages billing for), and you restore it yourself within the 30 days — after that, Google deletes it for good. Restoring is admitted like creating a new project: your purchased credit balance must still clear the tier's floor (100 credits for the sandbox tier most study and demo use fits). Google says most services are fully working again within 36 hours of a restore.
• One real gap: nightly backups don't survive. Backups are written to a bucket inside the project, and that bucket is not protected by Cloud Storage's soft-delete, so it is very likely gone as soon as you delete the project — even though the project itself is recoverable for 30 days. If you've customised Ollama and want to keep that work, copy a backup out (to Google Drive, or a bucket outside the project) before you delete. For a default installation with nothing irreplaceable in it, this does not matter.

Pay only while you use it, the other way: delete and redeploy

The option above only applies to a RAD-managed project; in your own project, or once the 30-day window has passed, the way to stop paying is to delete the deployment and deploy it again when you need it.

• What a redeploy costs. The module fee again plus the builds: about 305 credits (US$30.50) in your own project, or 275 credits in a RAD-managed one. Deleting saves money once Ollama would otherwise sit unused for about 3 days or more in your own project (about US$10.78 a day), or about 2 days or more in a RAD-managed one (180 credits a day).
• Most of the running cost is usually the database and any shared file/cache VM. They stop only when nothing else in the project uses them, so deleting Ollama while something else shares the project saves only this app's own compute part.
• Keep your data first. Nightly backups go to a bucket inside the deployment and are deleted with it, so copy the latest backup out before deleting if you want to keep it.

Lab sessions and Managed Environments

• Lab sessions, for training. A trainer runs a session for a class. Each participant gets Ollama in their own Google Cloud project for 15 minutes to 24 hours, within an allowance the trainer sets. Either the trainer funds every place, or each participant pays for their own. Everything is deleted when the session ends and unused credits go back to the trainer.
• Managed Environments, for consultancies. A partner runs Ollama for a client from a ring-fenced wallet it funds, and settles with the client directly. If the wallet runs low, billing pauses and the data is kept, so nobody receives an unexpected charge. At the end the partner hands the project over and the deployments become the client's own.
• Credits can be bought in more than 20 currencies, including XAF, XOF, NGN, GHS, KES and ZAR, by card, bank transfer or mobile money.

Sources (8 October 2026): Google Cloud list prices from the Cloud Billing Catalog API; Delete and restore projects for the 30-day recovery window and what it says about Cloud Storage objects without soft delete; RAD fees and the daily-credit estimates from radmodules.dev/pricing. Prices change; check each source before relying on a figure.

1. Overview

Ollama runs as a containerised inference server. The deployment wires together a focused set of Google Cloud services:

Capability	Google Cloud service	Notes
Compute	GKE Autopilot	Ollama pods, 8 vCPU / 16 GiB by default (7B models), horizontally autoscaled
Model storage	Cloud Storage + GCS Fuse CSI	Models bucket mounted at /mnt/gcs; weights persist across pod restarts
Secrets	Secret Manager	No app-managed secrets — Ollama requires no credentials
Ingress	Kubernetes ClusterIP	Internal-only by default; use LoadBalancer only when external access is required

Sensible defaults worth knowing up front:

• No database, no Redis. Ollama is stateless beyond its GCS-backed model cache. Neither Cloud SQL nor Redis is provisioned.
• GCS Fuse is the persistence layer. Model weights are stored in a dedicated GCS bucket and mounted into the pod at /mnt/gcs. Pod restarts load models from GCS rather than re-downloading them.
• ClusterIP by default. Ollama is designed as a shared in-cluster inference endpoint. Any pod in the same cluster reaches it at http://<service-name>.<namespace>.svc.cluster.local:11434. Setting service_type = "LoadBalancer" exposes the unauthenticated API publicly — do not do this without IAP or Cloud Armor.
• Automatic model pull. When default_model is set and no custom initialization_jobs are provided, a Kubernetes Job named model-pull is created on first deployment and stores the model in the GCS bucket.
• Three environment variables are always injected: OLLAMA_MODELS, OLLAMA_HOST, and OLLAMA_KEEP_ALIVE. Do not override OLLAMA_MODELS or OLLAMA_HOST in environment_variables.

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2. Google Cloud Services & How to Explore Them

All commands assume you have run gcloud container clusters get-credentials <cluster> --region <region> --project <project> and that PROJECT, REGION, and NAMESPACE are set. The namespace and other identifiers are reported in the deployment Outputs.

A. GKE Autopilot — the Ollama workload

Ollama pods are scheduled on Autopilot, which bills for the CPU and memory the pods actually request. Horizontal Pod Autoscaling scales the deployment between the minimum and maximum replica counts.

• Console: Kubernetes Engine → Workloads → select the Ollama workload to see pods and events. Kubernetes Engine → Services & Ingress shows the ClusterIP (or external IP when service_type = "LoadBalancer").
• CLI:

  kubectl get pods,svc,hpa -n "$NAMESPACE"
  kubectl logs -n "$NAMESPACE" deploy/<service-name> --tail=100
  kubectl describe hpa -n "$NAMESPACE"          # current vs target utilisation
  # Verify Ollama is responding inside the cluster:
  kubectl exec -n "$NAMESPACE" deploy/<service-name> -- \
    curl -s http://localhost:11434/api/tags | jq '.models[].name'

See App_GKE for how Autopilot, scaling, and the workload type (Deployment vs StatefulSet) are managed.

B. Cloud Storage — model weight persistence

Ollama model weights are stored in a dedicated GCS bucket (named <resource_prefix>-models) and mounted into every pod via the GCS Fuse CSI driver at /mnt/gcs. The environment variable OLLAMA_MODELS is set to /mnt/gcs/ollama/models so Ollama discovers and caches models there.

• Console: Cloud Storage → Buckets → select the models bucket to browse downloaded model files.
• CLI:

  # Bucket name is in the Outputs (models_bucket)
  gcloud storage ls gs://<models-bucket>/ollama/models/
  gcloud storage buckets describe gs://<models-bucket> --project "$PROJECT"
  # Confirm the GCS volume is mounted inside a running pod:
  kubectl exec -n "$NAMESPACE" deploy/<service-name> -- ls /mnt/gcs/ollama/models/

The bucket name is reported as the models_bucket output. See App_GKE for GCS Fuse, CMEK options, and bucket lifecycle policies.

C. Secret Manager

Ollama requires no application-managed credentials — there is no admin password and no database password. Secret Manager is available for any custom secrets you inject via secret_environment_variables.

• Console: Security → Secret Manager.
• CLI:

  gcloud secrets list --project "$PROJECT"
  gcloud secrets versions access latest --secret=<secret-name> --project "$PROJECT"

See App_GKE for the Secret Store CSI integration and rotation.

D. Networking & ingress

By default the Ollama service is exposed only within the cluster (ClusterIP). When service_type = "LoadBalancer" is set, an external Cloud Load Balancing IP is provisioned. A custom domain, Cloud Armor WAF, and IAP can be layered on for external access scenarios.

• Console: Kubernetes Engine → Services & Ingress; Network services → Load balancing; VPC network → IP addresses.
• CLI:

  kubectl get svc -n "$NAMESPACE"
  # Internal cluster URL (printed as ollama_cluster_url output):
  echo "http://<service-name>.$NAMESPACE.svc.cluster.local:11434"
  gcloud compute addresses list --project "$PROJECT"

See App_GKE for custom domains, Cloud CDN, and static IP details.

E. Cloud Logging & Monitoring

Pod stdout/stderr flows to Cloud Logging; GKE metrics flow to Cloud Monitoring. Optional uptime checks and alert policies are available.

• Console: Logging → Logs Explorer; Monitoring → Dashboards / Alerting.
• CLI:

  gcloud logging read \
    'resource.type="k8s_container" AND resource.labels.namespace_name="'"$NAMESPACE"'"' \
    --project "$PROJECT" --limit 50

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3. Ollama Application Behaviour

• No first-deploy database setup. Ollama has no database. There is no db-init job and no Cloud SQL instance.
• Model pull on first deploy. When default_model is set and initialization_jobs is empty, a Kubernetes Job named model-pull runs once. It starts a local Ollama server in the background, pulls the named model, stores it in the GCS bucket, then shuts down. The job mounts the ollama-models GCS volume so the weights persist. The timeout is controlled by model_pull_timeout_seconds (default 3600 seconds); large models (7B+) can take 20–30 minutes on first pull.
• Cold-start model loading. On each pod start, Ollama loads model weights from GCS Fuse. This typically takes 30–120 seconds depending on model size. The startup probe uses a 30 s initial delay with 20 failure attempts (roughly 5 minutes) to accommodate this. OLLAMA_KEEP_ALIVE is set to "24h" so loaded models stay resident in memory between requests. Override via environment_variables.
• Automatically injected variables. OLLAMA_MODELS (/mnt/gcs/ollama/models), OLLAMA_HOST (0.0.0.0:11434), and OLLAMA_KEEP_ALIVE (24h) are injected automatically. Do not override the first two; the third may be overridden.
• Health endpoint. The Ollama root path (/) responds with "Ollama is running" once the server is ready. Readiness and liveness probes target this path.
• Additional tuning. Use environment_variables to set OLLAMA_NUM_PARALLEL (default 1, increase for concurrent callers), OLLAMA_ORIGINS (restrict CORS), and other Ollama environment variables.
• Manual model management. Pull additional models or remove existing ones directly:

  kubectl exec -n "$NAMESPACE" deploy/<service-name> -- ollama pull mistral
  kubectl exec -n "$NAMESPACE" deploy/<service-name> -- ollama list
  kubectl exec -n "$NAMESPACE" deploy/<service-name> -- ollama rm mistral

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4. Configuration Variables

Variables are grouped exactly as they appear on the deployment platform. Only settings specific to or notable for Ollama are listed; every other input is inherited from App_GKE with its standard behaviour and defaults.

Group 1 — Project & Identity

Variable	Default	Description
project_id	(required)	Target Google Cloud project.
region	us-central1	Region for the workload and regional resources.

Group 2 — Deployment Environment

Variable	Default	Description
tenant_id	demo	Short suffix that makes resource names unique per environment.
support_users	[]	Emails granted project access and monitoring alerts.
resource_labels	{}	Labels applied to all resources for cost/ownership tracking.

Group 3 — Application Identity

Variable	Default	Description
application_name	ollama	Base name for resources. Do not change after first deploy.
application_display_name	Ollama LLM Server	Friendly name shown in the Console.
application_description	(set)	Workload description annotation.
application_version	latest	Ollama image tag; pin to a specific version (e.g., 0.3.12) in production.

Group 4 — Runtime & Scaling

Variable	Default	Description
deploy_application	true	Set false to provision storage and IAM without deploying the workload.
container_resources	{ cpu_limit="8", memory_limit="16Gi", cpu_request="4", mem_request="8Gi" }	Container CPU and memory. 3B models: cpu_limit="4", memory_limit="8Gi". 7B models: cpu_limit="8", memory_limit="16Gi".
min_instance_count	1	Minimum replicas. Keep ≥ 1 to avoid cold-start model-load latency on each request.
max_instance_count	3	Maximum replicas (autoscaler ceiling). Each pod independently loads the model into memory — size accordingly.
timeout_seconds	300	Pod termination grace period. Increase for long-running inference requests.
termination_grace_period_seconds	60	Seconds Kubernetes waits before force-killing the pod after SIGTERM.
service_type	ClusterIP	Kubernetes Service type. ClusterIP keeps the API internal. LoadBalancer exposes it publicly without authentication.
session_affinity	None	Session affinity for the Kubernetes Service. ClientIP improves multi-turn context continuity but skews load distribution.
enable_image_mirroring	true	Mirror ollama/ollama to Artifact Registry to avoid Docker Hub rate limits.
enable_vertical_pod_autoscaling	false	Let Autopilot tune resource requests automatically.
container_image_source	prebuilt	Not forwarded — main.tf pins "prebuilt", so ollama/ollama:<application_version> is always mirrored and never built.
container_image	ollama/ollama	Full image URI when container_image_source = "prebuilt".
enable_cloudsql_volume	false	Not needed for Ollama (no database).

Group 5 — Access & Networking

Variable	Default	Description
enable_iap	false	Require Google sign-in via Identity-Aware Proxy. Recommended when service_type = "LoadBalancer".
iap_authorized_users / iap_authorized_groups	[]	Who may access through IAP.
iap_oauth_client_id / iap_oauth_client_secret	""	Required when IAP is enabled.
enable_cloud_armor	false	Attach a Cloud Armor (WAF) policy to the Ingress backend.
application_domains	[]	Custom hostnames for the load balancer.
enable_custom_domain	true	Configure a custom domain and managed certificate.
reserve_static_ip	true	Reserve a stable external IP address.
enable_vpc_sc	false	Enforce a VPC-SC perimeter (requires organization_id).
enable_audit_logging	false	Detailed Cloud Audit Logs.

Group 6 — Environment Variables & Secrets

Variable	Default	Description
environment_variables	{}	Extra non-secret settings. OLLAMA_MODELS, OLLAMA_HOST, and OLLAMA_KEEP_ALIVE are injected automatically. Use OLLAMA_NUM_PARALLEL, OLLAMA_ORIGINS, etc. here.
secret_environment_variables	{}	Map of env var → Secret Manager secret name.

Group 7 — Backup & Maintenance

Not applicable for Ollama — model weights are stored durably in GCS. These variables are present for interface compatibility only. See App_GKE.

Group 8 — CI/CD & GitHub Integration

Standard App_GKE Cloud Build / Cloud Deploy integration — see App_GKE. Key inputs: enable_cicd_trigger, github_repository_url, github_token, enable_cloud_deploy, enable_binary_authorization.

Also in this group: enable_resource_quota, quota_cpu_requests, quota_cpu_limits, quota_memory_requests, quota_memory_limits, quota_max_pods, quota_max_services, quota_max_pvcs. When enable_resource_quota = true, memory values must use binary unit suffixes (4Gi, 8192Mi) — bare integers are treated as bytes by Kubernetes and block all pod scheduling.

Group 9 — Jobs & Scheduled Tasks

Variable	Default	Description
initialization_jobs	[]	Leave empty and set default_model to use the auto-generated model-pull job. Provide a non-empty list to replace it entirely.
cron_jobs	[]	Recurring Kubernetes CronJobs for model management or other tasks.
additional_services	[]	Additional containers deployed as separate Kubernetes Deployments (e.g., a Qdrant vector database alongside Ollama).
enable_topology_spread	false	Spread pods across zones for higher availability.

Group 11 — Storage & Filesystem

Variable	Default	Description
create_cloud_storage	true	Provision GCS buckets in storage_buckets. The models bucket is always created regardless.
storage_buckets	[]	Additional GCS buckets beyond the models bucket.
enable_nfs	false	Not required for Ollama (uses GCS Fuse).
gcs_volumes	[]	Additional GCS Fuse volume mounts. The ollama-models bucket at /mnt/gcs is always appended automatically.
manage_storage_kms_iam / enable_artifact_registry_cmek	false	CMEK options.

Group 12 — Database Backend

Not applicable for Ollama. database_type is fixed to "NONE" — no Cloud SQL instance is provisioned. These variables are present for interface compatibility only.

Group 14 — Observability & Health

Variable	Default	Description
startup_probe	{ type="HTTP", path="/", initial_delay_seconds=30, failure_threshold=20 }	20-attempt threshold allows ~5 minutes for model loading from GCS.
liveness_probe	{ type="HTTP", path="/", initial_delay_seconds=60, failure_threshold=3 }	60 s delay avoids false restarts during model load.
uptime_check_config	{ enabled=false, path="/" }	Cloud Monitoring uptime check; disabled by default, enable explicitly to activate.
alert_policies	[]	Optional metric alert policies.

Group 15 — Redis

Not applicable for Ollama. enable_redis is hard-coded to false regardless of this setting.

Group 16 — GKE Backend & Cluster

Variable	Default	Description
gke_cluster_name	""	GKE cluster name; leave empty for auto-discovery from Services_GCP.
namespace_name	""	Kubernetes namespace; auto-generated from the application name when empty.
enable_pod_disruption_budget	true	Protect availability during node upgrades.
pdb_min_available	1	Ensure max_instance_count ≥ 2 when using a PDB so rolling upgrades can proceed.
deployment_timeout	1800	Seconds Terraform waits for the Deployment rollout. Increase to 1200 for large models (13B+).

Group 17 — StatefulSet

Applies only when workload_type = "StatefulSet". The default Deployment workload with GCS Fuse persistence is recommended; these settings are not needed in the typical case.

Variable	Default	Description
stateful_pvc_enabled	null	Enable a PVC for local model storage. Not required when using GCS Fuse.
stateful_pvc_size	50Gi	PVC size (e.g., "100Gi" for multiple large models).
stateful_pvc_mount_path	/mnt/data	Container path for the PVC.
stateful_pvc_storage_class	standard-rwo	Kubernetes StorageClass.

Group 19 — Ollama Model Configuration

Variable	Default	Description
default_model	""	Model to pull on first deployment (e.g., "llama3.2:3b", "mistral", "phi3:mini"). Leave empty to skip the auto-pull job.
model_pull_timeout_seconds	3600	Timeout for the model-pull Kubernetes Job. Large models (7B+) can take 20–30 minutes. Valid range: 300–7200.

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5. Outputs

These values are returned on a successful deployment and are the quickest way to locate and explore the running resources.

Output	Description
service_name	Kubernetes Service name.
namespace	Namespace the workload runs in.
ollama_cluster_url	Internal Kubernetes URL: http://<service-name>.<namespace>.svc.cluster.local:11434. Use this in other pods to call the Ollama API.
service_cluster_ip	In-cluster ClusterIP.
stage_service_cluster_ips	Map of ClusterIPs for stage-specific services (Cloud Deploy).
service_external_ip	External LoadBalancer IP (when service_type = "LoadBalancer" and a static IP is reserved).
api_url	Service URL.
models_bucket	GCS bucket name where Ollama model weights are persisted.
storage_buckets	All provisioned Cloud Storage buckets.
network_name / network_exists / regions	VPC network, presence, available regions.
container_image / container_registry	Deployed image and Artifact Registry repo.
monitoring_enabled / monitoring_notification_channels	Monitoring status and channels.
initialization_jobs	Names of the setup jobs (including model-pull when triggered).
deployment_id / tenant_id / resource_prefix	Naming identifiers.
project_id / project_number	Project identifiers.
cicd_enabled / cicd_configuration	CI/CD status and details.
kubernetes_ready	true when the cluster endpoint is available and workloads are deployed. false on first apply of a new inline cluster — re-run apply to complete.
vpc_sc_enabled / vpc_sc_perimeter_name / vpc_sc_dry_run_mode	VPC-SC status.
audit_logging_enabled / artifact_registry_cmek_enabled	Audit logging and CMEK status.

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6. Configuration Pitfalls & Sensible Defaults

Risk: Critical (data loss / outage / security) — High (service degraded) — Medium (cost or partial degradation) — Low (minor).

Setting	Sensible value	Risk	Consequence if wrong
service_type	ClusterIP	Critical	LoadBalancer exposes the unauthenticated Ollama API publicly on port 11434. Ollama has no built-in auth.
container_resources.memory_limit	16Gi (7B) / 8Gi (3B)	Critical	Insufficient memory causes OOM-kill mid-inference and crash-loops the pod. Allocate at least 2× the quantised model weight size.
quota_memory_requests / _limits	binary units (4Gi, 8Gi)	Critical	Bare integers (e.g., "4") are treated as bytes by Kubernetes and block all pod scheduling in the namespace.
enable_iap	true if service_type = "LoadBalancer"	Critical	Without IAP or VPC restriction, the Ollama API is unauthenticated and publicly reachable.
container_resources.cpu_limit	8 (7B) / 4 (3B)	High	Too few CPUs makes token generation extremely slow. For production 7B inference, 6–8 cores are needed.
min_instance_count	1	High	0 enables scale-to-zero but causes 60–120 s cold starts while the model reloads from GCS.
model_pull_timeout_seconds	3600	High	A short timeout causes the model-pull Job to fail before the download completes for models over 2 GB.
deployment_timeout	600	High	Too short for a large model (13B+) loading from GCS on first start. Increase to 1200.
max_instance_count	3	High	Each pod independently loads the full model into memory. Three 7B replicas require ~48 GiB.
default_model	set to desired model	Medium	Leaving empty is safe for initial deploy but the API returns an error on all inference requests until a model is pulled manually.
environment_variables.OLLAMA_NUM_PARALLEL	2–4 for shared use	Medium	Default 1 serialises all requests. Increase for shared cluster deployments with concurrent callers.
environment_variables.OLLAMA_KEEP_ALIVE	24h (injected automatically)	Medium	Ollama's own default (5m) evicts models from memory after inactivity, causing 30–60 s reload delays. The module injects 24h automatically.
enable_pod_disruption_budget	true	Medium	With pdb_min_available = 1 and only one replica, rolling node upgrades stall. Ensure max_instance_count ≥ 2.
enable_resource_quota	enable for shared clusters	Medium	Without a ResourceQuota, a misconfigured pod can consume all cluster resources.
gcs_volumes mount options	include implicit-dirs	Medium	Without implicit-dirs, GCS Fuse directory listings fail and Ollama cannot discover cached models.
enable_image_mirroring	true	Medium	Disabling pulls from Docker Hub, which is rate-limited. Keep true in production.

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For the foundation behaviour referenced throughout — Workload Identity, autoscaling, ingress and certificates, CI/CD, Cloud Armor, IAP, Binary Authorization, VPC-SC, backups, and image mirroring — see App_GKE. Ollama-specific shared application configuration is described in Ollama_Common.

Related guides

• Hands-on lab: Ollama on GKE Autopilot — deploy it step by step, with the console screens and commands at each stage.
• Ollama on Google Cloud Run — the same application on Cloud Run, for when you need the other deployment target.
• Ollama Common — Shared Application Configuration — the configuration shared by both deployment targets.