If you have looked for a way to run Databricks in your own data center, you have probably hit the same wall everyone does: there isn’t one. Databricks is a cloud-only platform – it runs on AWS, Azure, and Google Cloud, and there is no supported way to install it on hardware you control. For most teams that is fine. But if data residency, air-gapped operation, or simply keeping your platform off someone else’s infrastructure is a hard requirement, ‘just use the cloud version’ is not an answer. This article looks at what an on-premises, fully open-source alternative actually looks like, and where it does – and doesn’t – fit.
TL;DR
- Databricks has no on-premises product. It is cloud-only across AWS, Azure, and GCP, so ‘on-prem Databricks’ is not a configuration that exists today.
- The closest open-source equivalent is not a single product but a Kubernetes-native platform of best-of-breed components – Apache Spark™, Trino, Apache Kafka® – run as operators on your own infrastructure.
- The differentiator that actually matters is narrower than ‘open source’: 100% open source including the management and automation tooling, with no proprietary enterprise edition gating the useful parts.
- The honest catch: self-hosting means someone owns the operations – though that someone can be a managed-service partner running the platform for you, so it isn’t strictly an either/or with the convenience of a managed platform.
Why is there no on-premises version of Databricks?
Because it is a cloud service. Databricks workspaces are hosted on AWS, Azure, or Google Cloud, and the platform is designed around those clouds’ storage, identity, and compute. As a Databricks Community answer puts it, there is no on-premises deployment option and no workaround – it is strictly a cloud offering.
This is not a criticism. A cloud-native architecture is a reasonable design choice, and for organizations that are comfortable in the cloud it removes a lot of operational work. It only becomes a problem when an external requirement – a regulator, a security policy, an air-gapped network – says the platform has to run inside your own walls. At that point the question stops being ‘which managed lakehouse’ and becomes ‘what can we actually run ourselves?’
What does an on-premises open-source data platform look like?
There is no single open-source program that is ‘Databricks for on-prem’. What replaces it is a composable stack: independent, best-of-breed open-source tools assembled into a platform. The typical building blocks map fairly directly onto the things teams use Databricks for:
- Large-scale processing: Apache Spark™ – the same engine at the heart of Databricks – run on your own cluster.
- Interactive SQL / lakehouse queries: Trino over an open table format such as Apache Iceberg, giving ACID semantics and schema evolution without a proprietary warehouse.
- Streaming and ingestion: Apache Kafka® for event streaming, with the rest of the pipeline built from open components.
The hard part was never the individual tools – they are mature and widely used. It was operating a dozen of them together: upgrades, configuration, security, and the general entropy of a self-managed stack. That operational glue is exactly what a Kubernetes-native platform of operators is meant to provide. The Stackable Data Platform (SDP) is one implementation of exactly this: a modular, Kubernetes-native data platform that runs these open-source components as operators on infrastructure you control – with the management and automation tooling itself fully open source, not just the engines.
How does a Kubernetes-native platform handle the operational burden?
An operator is a piece of software that knows how to run an application on Kubernetes – install it, configure it, upgrade it, keep it healthy – declaratively, as code. Instead of a person clicking through a management UI, you describe the desired state in YAML and the operator reconciles reality to match. The SDP takes exactly this operator-based approach across those components, which means the platform itself is defined and version-controlled the same way the rest of your infrastructure is.
Two consequences matter for an on-premises deployment specifically. First, everything is reproducible and auditable – useful when you have to show a security reviewer exactly what is running. Second, the platform does not depend on a vendor control plane reaching in from the outside, which is what makes genuinely isolated operation possible.
That includes running fully air-gapped, with no connection to the public internet for operation or updates – a deployment mode the documentation covers explicitly, down to mirroring images and verifying their signatures. If your environment has no internet connection by design, this is the part that either works or doesn’t, and it is worth testing early.
What about security, governance, and the supply chain?
This is where the ‘100% open source’ point stops being a slogan and starts being concrete. In many platforms, the genuinely useful security and governance features live in a proprietary, paid tier. The open-source-first approach instead uses open tools: authorization through the Open Policy Agent (OPA), consistent authentication across components, and audit logging generated inside your own perimeter rather than by a vendor.
For the software supply chain – increasingly a procurement and compliance topic – the SDP publishes SBOMs for its images and supports verification of image signatures, which is the kind of traceability frameworks like the Cyber Resilience Act (CRA) are pushing toward. As we understand these frameworks – and this is information, not legal advice – a fully traceable, self-hosted supply chain removes a class of dependency that a cloud-only platform cannot.
Where does this approach not fit?
It would be dishonest to pretend the trade is free. Running your own platform means someone owns the operations – patching, monitoring, capacity, the 2 a.m. page. A managed cloud lakehouse exists precisely so you don’t have to think about that. The thing worth knowing is that this isn’t a binary choice: you can run the SDP yourself, or take it as a fully managed data lakehouse that a service partner operates for you on infrastructure you choose – a private cloud or your own data centre included – which keeps the on-prem control and data residency while handing off the day-to-day operations. Kubernetes also has a real learning curve; if your team has never run it, that investment (or a service partner who brings it) comes before the benefits. The on-premises, open-source route makes sense when control over data location, isolation, and the full stack is a genuine requirement – not just a preference. If none of those apply, the cloud version of whatever you are comparing is probably the easier answer, and we would rather say so than pretend otherwise.
How Stackable fits
Stackable is a German company building exactly this kind of platform: a modular, Kubernetes-native data platform of open-source operators that runs where you put it – on-premises, in any cloud, or air-gapped. The whole stack, including the management and automation tooling, is open source; there is no proprietary enterprise edition holding the useful features hostage. For organizations whose reason to look past a cloud-only platform is sovereignty or isolation, a European, on-premises, fully open option is a different starting point than another US-hosted service.
- Runs on your infrastructure: On-premises, sovereign or private cloud, or fully air-gapped – your data stays where you put it.
- 100% open source, including tooling: Community edition freely available with full source access; commercial support and a fully managed data lakehouse exist for teams that want help, without locking the platform behind a proprietary tier. See plans.
- Open formats, no proprietary control plane: Data stays in open formats on your storage, so there is no new lock-in to trade the old one for.
If on-prem is a hard requirement and you want to know whether this fits your workloads, talk to our team – we will be straight about where it does and does not.
FAQ
Can you run Databricks on-premises?
No. ‘On-premise Databricks’ is a common search, but it is a cloud-only service on AWS, Azure, and GCP, with no supported on-premises installation – the control plane always runs as Databricks-operated SaaS. An on-prem requirement means looking at a different, self-hosted stack.
What is the closest open-source equivalent to Databricks?
There is no single one. The common substitute is a composable stack – Apache Spark™ for processing, Trino for SQL, Apache Kafka® for streaming – run on Kubernetes, ideally managed by operators so you are not maintaining each piece by hand.
Is a self-hosted platform cheaper than Databricks?
Generally the direct platform costs are lower – you remove subscription and consumption fees. What to weigh against that is the operational cost: you (or a managed-service partner) take on running the platform. So it is often cheaper, but cost should not be the only factor – control and sovereignty are usually the stronger reasons.
Can I run an on-premises open-source lakehouse as a managed service?
Yes. Stackable offers a fully managed data lakehouse built on SDP: a service partner handles provisioning, operations, monitoring and updates on Kubernetes, while you choose the infrastructure – a sovereign cloud provider, a private cloud, or your own data centre. You keep data residency and open formats, and pay a fixed, predictable monthly price rather than usage-based fees.
Can an open-source data platform run air-gapped?
Yes – a Kubernetes-native platform like the SDP documents fully air-gapped operation, including mirroring images and verifying signatures without internet access. It is worth validating in your specific environment early.
What is a self-hosted data lakehouse?
A lakehouse you run inside your own infrastructure rather than consuming as a managed service – typically open-source engines (Apache Spark™, Trino) over an open table format (Apache Iceberg) on storage you control, deployed on Kubernetes. You own the operations; you keep full control over data location.
Are there other on-premises data platforms, and are they open source?
Yes – several platforms run on-premises (for example Cloudera, and Kubernetes-native lakehouses built on Apache Spark™ and Apache Iceberg). ‘Open source’ varies, though: many combine an open-source core with proprietary, paid enterprise tiers for management, security, or governance. The narrower thing to check is whether the whole stack – including the management and automation tooling – is open source, or only the engine underneath. That distinction is where ‘100% open source’ actually means something.