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For most small and medium teams, the practical way to run a shared MLflow deployment on Google Cloud is to put the MLflow server in Cloud Run, store tracking and Model Registry metadata in Cloud SQL for PostgreSQL, and store models and other artifacts in a private Cloud Storage bucket. Publish the container through Artifact Registry, use a dedicated service account, and protect the endpoint with Cloud Run IAM or an identity layer before calling it production-ready.
This creates an MLflow tracking and registry foundation. It does not, by itself, create a model-serving endpoint, feature store, drift-monitoring system, or complete MLOps platform.
What you are building
MLflow separates the tracking server, backend store, and artifact store. On GCP, give each responsibility the service suited to it:
| Component | GCP service | What it stores or does |
|---|---|---|
| Tracking server | Cloud Run | MLflow UI, REST API, and tracking requests |
| Backend store | Cloud SQL for PostgreSQL | Experiments, runs, parameters, metrics, tags, and registered-model metadata |
| Artifact store | Cloud Storage | Model files, plots, images, logs, datasets, and other run outputs |
| Container registry | Artifact Registry | The MLflow Docker image |
| Secrets | Secret Manager | Database passwords and authentication configuration |
Cloud SQL should hold metadata, not large model files. The official MLflow GCP deployment documents this Cloud Run, Cloud SQL, and Cloud Storage arrangement: MLflow’s GCP deployment guide. MLflow’s architecture overview explains the separation in more detail at the architecture documentation.
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Choose the deployment that fits
| Option | Best for | Main trade-off |
|---|---|---|
| Local MLflow | Personal experiments and demonstrations | Not a durable, shared service |
| Cloud Run | A single containerized server with moderate or bursty traffic | Less infrastructure control than Kubernetes |
| GKE | Teams already operating Kubernetes or requiring private networking, custom ingress, service meshes, or node placement | More operational work |
| Managed MLflow, such as Databricks on Google Cloud | Managed governance, workspace administration, catalog integration, and serving | Vendor and platform cost; less lightweight than standalone MLflow |
Cloud Run is the default in this guide because it avoids VM patching, provides managed HTTPS and logs, and connects natively to Cloud SQL. MLflow also documents Kubernetes and Helm deployment options in its self-hosting documentation: MLflow self-hosting. Databricks is a separate managed offering, not a Google Cloud-managed open-source MLflow server; see Managed MLflow.
Prerequisites
- A Google Cloud project with billing enabled.
- A deliberately chosen region for Cloud Run, Cloud SQL, Artifact Registry, and the bucket. Keeping them near one another reduces latency and cross-region transfer.
- Permission to create services, databases, buckets, repositories, service accounts, IAM bindings, and secrets.
- Docker locally, or Cloud Build access.
- Python and MLflow on each client machine.
- An exact MLflow version pinned in your image. Do not use
latest; check the current release before publishing. The reference guide uses an example tag such asv3.10.0, not a permanent version recommendation. - A database password that will be kept in Secret Manager.
For a local proof of concept, install MLflow and run mlflow server --port 5000. Current documentation says a basic standalone server uses SQLite by default from MLflow 3.7.0; that is suitable for personal use, not concurrent team tracking. See the current self-hosting documentation.
Step 1: Define deployment variables and enable APIs
export PROJECT_ID="your-gcp-project"
export REGION="us-central1"
export REPOSITORY="mlflow-repo"
export IMAGE_NAME="mlflow-gcp"
export IMAGE_TAG="vX.Y.Z"
export BUCKET_NAME="mlflow-artifacts-${PROJECT_ID}"
export SERVICE_NAME="mlflow"
export SQL_INSTANCE="mlflow-postgres"
gcloud config set project "$PROJECT_ID"
gcloud services enable
run.googleapis.com
sqladmin.googleapis.com
storage.googleapis.com
artifactregistry.googleapis.com
iam.googleapis.com
secretmanager.googleapis.comGoogle Cloud API names and required permissions can change, so verify the enablement list against the current Google Cloud documentation and test it in a fresh project.
Step 2: Build and push the MLflow image
Create a Docker repository and authenticate Docker to its regional host:
gcloud artifacts repositories create "$REPOSITORY"
--repository-format=docker
--location="$REGION"
gcloud auth configure-docker "${REGION}-docker.pkg.dev"Use the MLflow image variant shown by the official guide and add the Google Cloud Storage client:
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FROM ghcr.io/mlflow/mlflow:<MLFLOW_VERSION>-full
RUN pip install --no-cache-dir google-cloud-storage
COPY start-mlflow.sh /start-mlflow.sh
RUN chmod +x /start-mlflow.sh
ENTRYPOINT ["/start-mlflow.sh"]A startup script keeps the database password out of the image and deployment command:
#!/bin/sh
set -eu
DB_PASSWORD=$(cat "$MLFLOW_DB_PASSWORD_FILE")
DB_URI="postgresql://${MLFLOW_DB_USER}:${DB_PASSWORD}@/${MLFLOW_DB_NAME}?host=/cloudsql/${CLOUD_SQL_CONNECTION_NAME}"
exec mlflow server
--backend-store-uri "$DB_URI"
--artifacts-destination "gs://${MLFLOW_ARTIFACT_BUCKET}"
--host 0.0.0.0
--port 5000In a hardened implementation, URL-encode database credentials if they contain URI-reserved characters, or use a connection mechanism that handles encoding safely. Build for Cloud Run’s target architecture; an ARM-based local Mac should explicitly use --platform linux/amd64 unless you build remotely.
docker build --platform linux/amd64
-t "${REGION}-docker.pkg.dev/${PROJECT_ID}/${REPOSITORY}/${IMAGE_NAME}:${IMAGE_TAG}" .
docker push "${REGION}-docker.pkg.dev/${PROJECT_ID}/${REPOSITORY}/${IMAGE_NAME}:${IMAGE_TAG}"Step 3: Create a private artifact bucket
gcloud storage buckets create "gs://${BUCKET_NAME}"
--location="${REGION}"
--uniform-bucket-level-access
--public-access-preventionDo not grant allUsers access just to make the UI work. Add lifecycle rules for obsolete artifacts after deciding your retention requirements.
Step 4: Create least-privilege identities
gcloud iam service-accounts create mlflow-runtime
--display-name="MLflow Cloud Run runtime"
gcloud storage buckets add-iam-policy-binding "gs://${BUCKET_NAME}"
--member="serviceAccount:mlflow-runtime@${PROJECT_ID}.iam.gserviceaccount.com"
--role="roles/storage.objectUser"The MLflow reference setup uses Storage Object User. Adjust permissions only if your artifact workflow needs additional listing, overwrite, or deletion operations; project-wide Storage Admin is unnecessarily broad for the normal case.
Step 5: Create Cloud SQL for PostgreSQL
Choose a supported PostgreSQL version and machine tier from the current Cloud SQL documentation. The following is an example, not a universal production size:
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gcloud sql instances create "$SQL_INSTANCE"
--database-version=POSTGRES_16
--cpu=2
--memory=7680MiB
--region="$REGION"
gcloud sql databases create mlflow --instance="$SQL_INSTANCE"
gcloud sql users create mlflow --instance="$SQL_INSTANCE" --password="DO-NOT-PASTE-A-PASSWORD"Set the password interactively or generate it in a controlled secret-creation workflow. Never put a real password in shell history, source control, a Dockerfile, or a reusable deployment command. Plan backups, maintenance, storage growth, connection limits, and restore testing; Cloud SQL high availability and backups are configurations, not automatic properties of every instance.
Step 6: Store the database password in Secret Manager
printf '%s' "$MLFLOW_DB_PASSWORD" |
gcloud secrets create mlflow-db-password --data-file=-
gcloud secrets add-iam-policy-binding mlflow-db-password
--member="serviceAccount:mlflow-runtime@${PROJECT_ID}.iam.gserviceaccount.com"
--role="roles/secretmanager.secretAccessor"Use a versioned secret and rotate it according to your policy. The Cloud Run service should read the mounted file at runtime rather than exposing the value in process arguments.
Step 7: Deploy MLflow to Cloud Run
export CLOUD_SQL_CONNECTION_NAME="${PROJECT_ID}:${REGION}:${SQL_INSTANCE}"
gcloud run deploy "$SERVICE_NAME"
--image="${REGION}-docker.pkg.dev/${PROJECT_ID}/${REPOSITORY}/${IMAGE_NAME}:${IMAGE_TAG}"
--region="$REGION"
--service-account="mlflow-runtime@${PROJECT_ID}.iam.gserviceaccount.com"
--port=5000
--memory=2Gi
--cpu=1
--min-instances=1
--max-instances=1
--add-cloudsql-instances="$CLOUD_SQL_CONNECTION_NAME"
--set-env-vars="MLFLOW_DB_USER=mlflow,MLFLOW_DB_NAME=mlflow,CLOUD_SQL_CONNECTION_NAME=${CLOUD_SQL_CONNECTION_NAME},MLFLOW_ARTIFACT_BUCKET=${BUCKET_NAME},MLFLOW_DB_PASSWORD_FILE=/secrets/mlflow-db-password"
--set-secrets="/secrets/mlflow-db-password=mlflow-db-password:latest"The container must listen on 0.0.0.0:5000 and remain in the foreground. The example’s minimum and maximum of one create a warm single-instance service; they do not provide horizontal high availability. min-instances=0 saves idle cost but permits cold starts. Raising max-instances permits replicas, but requires deliberate database connection sizing, migration handling, and consistency planning.
The official GCP example uses similar port, resource, Cloud SQL, and artifact settings: MLflow’s deployment guide. Its direct command form is mlflow server --backend-store-uri "<POSTGRESQL_CONNECTION_STRING>" --artifacts-destination "gs://<BUCKET_NAME>" --host 0.0.0.0 --port 5000.
Step 8: Secure the endpoint
Treat a public unauthenticated URL as development-only. The guide’s public-access example and --disable-security-middleware simplify testing but should not be left as a production configuration.
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Cloud Run IAM
Keep the service private and grant roles/run.invoker to approved users or service accounts. Clients must obtain and send a Google identity token; opening the URL in a browser is not the same as configuring a Python job or CI runner.
MLflow authentication
MLflow documents basic authentication, SSO/OIDC options, and authentication plugins. These choices can require extra packages, environment variables, and server arguments. Read the current requirements at MLflow self-hosting and the GCP guide.
Gateway or identity-aware proxy
An existing corporate gateway can centralize DNS, TLS, identity, audit logging, and policy enforcement. If you use a custom hostname or reverse proxy, configure host validation and CORS deliberately:
mlflow server
--allowed-hosts "mlflow.company.com,localhost:*"
--cors-allowed-origins "https://app.company.com"MLflow identifies invalid Host header and CORS settings as common remote-server issues. Browser and API clients should use a hostname covered by the same authentication and proxy design.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Step 9: Connect and validate a client
After obtaining the Cloud Run URL and configuring the required authentication, run a small tracking test:
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import mlflow
from pathlib import Path
mlflow.set_tracking_uri("https://YOUR_MLFLOW_URL")
mlflow.set_experiment("gcp-setup-test")
with mlflow.start_run():
mlflow.log_param("source", "gcp-validation")
mlflow.log_metric("accuracy", 0.91)
Path("healthcheck.txt").write_text("MLflow artifact test")
mlflow.log_artifact("healthcheck.txt")The official guide also provides mlflow demo --tracking-uri "<CLOUD_RUN_URL>". A successful validation shows the experiment, run, parameter, metric, and artifact in the UI. Confirm the request in Cloud Run logs, metadata in Cloud SQL, and the artifact object in Cloud Storage.
Troubleshooting
Container fails to start or Cloud Run reports a port error
- Bind MLflow to
0.0.0.0, not127.0.0.1. - Use the configured port 5000 consistently.
- Run the server in the foreground.
- Confirm the image architecture is compatible with Cloud Run.
- Increase memory if startup or selected MLflow features require it.
Cloud SQL connection failures
- Check that
--add-cloudsql-instancesuses the exactproject:region:instanceconnection name. - Verify the database, username, password secret, and runtime service-account access.
- Use the Unix-socket URI under
/cloudsql/<project>:<region>:<instance>, not an unintended public address. - Check connection-pool limits and region or project mismatches.
Cloud Storage permission denied
- Confirm the running service account, bucket name, and object role.
- Ensure the image includes
google-cloud-storage. - Do not disable public-access prevention; it is normally desirable.
- If clients upload directly, grant them appropriate bucket permissions; proxying artifacts through MLflow can centralize access control.
Invalid Host header or CORS errors
Check the hostname presented by the browser or proxy against --allowed-hosts, and add only the required browser origins with --cors-allowed-origins.
Authentication failures
Verify whether the client is expected to use a Cloud Run identity token, MLflow credentials, OIDC, or a gateway session. These are different authentication paths; a browser login does not automatically authenticate a notebook or CI job.
Artifact access design
MLflow can proxy artifact operations through the tracking server or allow clients to access Cloud Storage directly. The CLI distinguishes --default-artifact-root, --artifacts-destination, and artifact-serving behavior; see the MLflow CLI reference. Proxying simplifies client-side bucket permissions and can suit a centralized service. Direct access can reduce server load but requires carefully designed IAM and network access.
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- Monitor Cloud Run requests, errors, latency, instances, and container logs.
- Monitor Cloud SQL CPU, memory, storage, connections, backups, and maintenance events.
- Use Cloud Storage lifecycle rules, retention requirements, and cost alerts.
- Back up Cloud SQL and test restoration; artifact durability and database recoverability are separate concerns.
- Pin and document MLflow versions in the Dockerfile, deployment configuration, CI/CD, and reproducibility metadata.
- Test upgrades against a staging or backed-up database.
- Avoid service-account keys; use attached identities and IAM.
- Enable appropriate audit logging, budgets, and alerts.
Managed services do not automatically make the platform highly available. Availability depends on Cloud Run instance settings, Cloud SQL configuration, regional design, artifact durability, authentication, and client access paths.
Cloud Run, GKE, or managed MLflow?
| Criterion | Cloud Run self-hosting | GKE self-hosting | Managed MLflow |
|---|---|---|---|
| Operational burden | Low to moderate | High | Lowest for MLflow infrastructure |
| Infrastructure control | Moderate | Highest | Provider-defined |
| Best fit | One shared, containerized service | Kubernetes-standard organizations and private platform requirements | Teams wanting managed governance and integrated data/AI services |
| Cost model | Cloud Run, SQL, storage, registry, and operations usage | Cluster, nodes, storage, networking, and operations usage | Vendor workload, edition, region, and contract pricing |
Cleanup
These commands permanently delete resources. Export anything needed and confirm each prompt before running them:
gcloud run services delete "$SERVICE_NAME" --region="$REGION"
gcloud sql instances delete "$SQL_INSTANCE"
gcloud artifacts repositories delete "$REPOSITORY" --location="$REGION"
gcloud storage rm --recursive "gs://${BUCKET_NAME}"Cloud SQL and stored artifacts can continue incurring charges even after the Cloud Run service is removed.
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