Cloud VPS 16 GB
Most popularA single-team experiment workspace, sized for real headroom.
$59.99per month
- 16 GB RAM
- 6 vCPU cores
- 200 GB NVMe SSD
- 30 TB
- Unlimited
MLflow Hosting Live In 5 Minutes
Track experiments, compare runs and store artifacts from one familiar MLflow interface, without hand-building the server, the authentication layer and the storage underneath it. xCloud configures the pinned MLflow 3.15.2 image behind an authenticated HTTPS gateway, one persistent volume for metadata and artifacts, and the reverse proxy in minutes, ready to sign in with credentials generated for you.
- No terminal
- No technical skills needed
- No lock-in, cancel anytime
From Sign-Up to a Live MLflow Workspace In Just 3 Easy Steps
Self-hosting MLflow normally means a container, an authentication layer in front of it, persistent storage for metadata and artifacts, and a domain and certificate before a single run is logged. This is the same result without assembling any of it.
Step 01
Sign Up
Create your xCloud account and pick MLflow from the OneClick catalog. Enter a site title and domain, confirm an administrator username, and let xCloud generate a strong password.
Step 02
We Deploy It For You
One click. We bring up the official MLflow 3.15.2 image, pinned by immutable digest, behind a Caddy gateway that protects the entire UI, tracking API and artifact API with your generated credentials, mount the mlflow_data volume for SQLite metadata and served artifacts, and publish it over HTTPS on your domain.
Step 03
Sign In And Log Your First Run
Open your HTTPS URL and sign in with the generated administrator credentials shown in your xCloud credential panel. Create an experiment, start a run, log a parameter and a metric, upload a small artifact, and read it all back to confirm the workspace works end to end.
Everything MLflow Does, On Your Own Server
MLflow runs in full here — experiment tracking, run comparison and artifact storage, on infrastructure you control.
Experiment And Run Tracking
Create experiments, start runs and browse the full run history from the familiar MLflow interface.
Metrics, Parameters And Tags
Log and compare metrics, parameters and tags across runs to see what actually changed between them.
Artifact Storage And Retrieval
Upload and read back artifacts — files, models, plots — served directly by MLflow from its own volume.
Authenticated Access, Front To Back
A Caddy gateway protects the entire UI, tracking API and artifact API with your generated credentials — nothing is reachable unauthenticated.
MLflow Stays Off The Public Network
The MLflow container itself has no path to the internet — only the authenticating gateway is published.
Persistent Metadata And Artifacts
SQLite metadata and served artifacts share one persistent volume, so a run and everything it produced stay together.
Everything You Need To Run Your Experiment Workspace 24/7
Managed hosting for MLflow, so the parts that keep it online are not your problem.
- MLflow 3.15.2 deployed instantly
- Official pinned image digest
- Administrator credentials generated for you
- Authenticated gateway in front of the full API
- Free SSL certificate
- Server backups and snapshots
- Server security updates included
- 30+ server locations
- Unused balance refunded
Simple Pricing. Everything Included.
Dedicated Cloud VPS sizes, pre-configured for MLflow and its authenticated gateway. Pick the one that fits, and resize any time.
Cloud VPS 24 GB
More concurrent experiments and a growing artifact store.
$84.99per month
- 24 GB RAM
- 8 vCPU cores
- 300 GB NVMe SSD
- 30 TB
- Unlimited
Every size is a dedicated Cloud VPS we provision, patch, monitor and restore. Billed monthly with no lock-in — delete the server and the unused balance comes back to your account. The 6 GB rung sold elsewhere on this site is deliberately absent: this app's own template minimum is 3 GB, which does not leave comfortable headroom on it once the operating system is counted. MLflow itself is free under the Apache License 2.0.
Already Have a Server? Bring Your Own
Connect any VPS — DigitalOcean, Vultr, Linode, AWS, Hetzner or bare metal — and deploy MLflow on it through the same panel, for a per-server management fee instead of a Cloud VPS rental. Size it above MLflow's own 3 GB minimum.
Free
Start your journey without cost.
$0forever
- 1 server
- 10 sites
Starter
Most popularYour rate with 2–5 servers connected.
$5per server / month
- 2–5 servers
- Unlimited sites
- All features included
- Bring any VPS provider
Professional
The same platform, discounted at 6–10 servers.
$4per server / month
- 6–10 servers
- Unlimited sites
- All features included
- Bring any VPS provider
Agency
The same platform, discounted at 11+ servers.
$3per server / month
- 11+ servers
- Unlimited sites
- All features included
- Bring any VPS provider
Priced per connected server, so your rate adjusts automatically as you add or remove servers. You pay your own provider for the server itself.
Ready To Run Your Own Experiment Workspace?
MLflow on xCloud runs on a dedicated Cloud VPS that we provision, patch, monitor and restore. Deploy faster. Own the server. Stay in control.
Fast Provisioning
Deploy faster using an xCloud Managed Server built with optimized stacks and high-speed SSDs.
Enterprise-grade security
Protect your infrastructure with a free SSL certificate and more advanced security features at no additional cost.
Intuitive Control Panel
From Server creation to application deployment, everything can be managed from our intuitive control panel.
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Blaine MooreFrequently Asked Questions
Feel free to contact us with any questions or concerns you may have.
What is MLflow?
MLflow is a free, open-source platform for tracking machine-learning experiments — runs, metrics, parameters, tags and artifacts — from one interface. This template deploys version 3.15.2.
Is MLflow free?
Yes — it is free and open source under the Apache License 2.0. What you pay xCloud for is the dedicated server it runs on and the management around it, not the software itself.
Can I self-host MLflow?
Yes — that is exactly what this template deploys. It brings up the official MLflow image behind an authenticated Caddy gateway, both pinned by immutable digest, mounts a persistent volume for metadata and artifacts, and publishes it over HTTPS on your domain in minutes instead of assembled by hand.
Who becomes the administrator, and how do I sign in?
The administrator username you choose at deploy time, with a password xCloud generates and shows once in your credentials panel. Sign in at your HTTPS URL with those credentials — they gate the entire MLflow UI and API, not just a login page.
Is this MLflow's own built-in login, or something else?
It's a Caddy gateway sitting in front of MLflow, using HTTP Basic Authentication with your generated credentials. The upstream MLflow image doesn't include the optional package its own built-in auth app needs, so this template uses Caddy as the actual authentication boundary instead — the effect is the same: nothing is reachable without your credentials.
What does xCloud configure automatically?
The administrator username and generated password, the Caddy gateway that authenticates every request, an exact allowed-hosts and CORS-origin restriction scoped to your domain, the persistent data volume, the domain and the HTTPS route. Only the gateway is published — the MLflow container itself has no path to the public internet.
Can this template handle high-concurrency or team-scale production workloads?
Not as configured. This baseline uses SQLite for metadata and local server-served artifacts, which is appropriate for a compact single-node workspace. The upstream MLflow image doesn't include database or object-storage drivers, and this template intentionally doesn't add any — larger, multi-worker or high-concurrency use needs an external database and artifact store, which is outside what this template deploys.
What should I back up?
The mlflow_data volume together with your xCloud Environment values (the administrator credentials), from the same point in time. The volume holds both the SQLite metadata database and every served artifact — restoring metadata without its matching artifacts, or without the credentials that authenticate access, produces an incomplete workspace.
Does xCloud automatically back up or update MLflow?
No. The template pins MLflow by immutable digest; a catalog re-sync affects new installations only. Review MLflow's migration and release notes, take a consistent backup of mlflow_data, then verify sign-in and experiment/run/artifact read-back before changing the pinned image.
What are the server requirements, and why does this page sell 16 GB?
The template manifest asks for 3 GB of RAM, two CPU cores and 20 GB of disk as a floor. That leaves only around 3 GB of headroom above the app's own floor on a 6 GB server once the operating system is counted — not comfortable — so this page sells the 16 GB tier as the entry rung instead.
What survives a restart?
Everything on the mlflow_data volume, because it's a persistent named volume independent of the containers. Restarting does not touch your experiments, runs, metrics or artifacts. Rotating admin credentials does require an xCloud Restart to take effect, since the gateway reads them on every start.
Is xCloud officially partnered with MLflow?
No. No official partnership or endorsement between xCloud and the MLflow project has been verified. This is a deployment and server-management option for the open-source MLflow project.
Can I cancel anytime?
Yes. There is no lock-in — cancel any time. Hosting is billed monthly and charged for what you actually use, so when you delete the server the unused balance is refunded to your account, less up to 10% in payment-processing fees.
Ready to Run Your Own Experiment Workspace?
Connect your first server in minutes. No credit card required, and no plan you have to grow into.