Kaapana is live · free · last verified 30 Oct 2026 · viability monitor
Kaapana
Open-source toolkit from DKFZ for building medical imaging platforms, from DICOM curation to federated analysis.
Kaapana is the pick when you are a research institution or multi-center study group that needs DICOM-native curation, TotalSegmentator or nnU-Net processing on whole datasets, and federated analysis where only models and aggregated results leave the site. It is more opinionated toward clinical imaging than general federated frameworks, and faster to stand up than wiring dcm4chee, Airflow and OpenSearch together yourself. The trade-off is real: you run Kubernetes and Helm on your own hardware, and extending it means containerizing methods as Helm charts. Skip it for a managed service, a certified medical device, or a single-site study where pooling data centrally is simpler.
Verdict generatedVerified 5h ago · liveness 57/100 · cite: rightaichoice.com/tools/kaapana
- Research institutions with dedicated IT infrastructure
- Multi-center study coordinators needing privacy-preserving analysis
- Medical imaging researchers building AI models on decentralized data
- Clinical data scientists running reproducible containerized pipelines
- Teams wanting a fully managed SaaS platform
- Groups needing a certified medical device for clinical use
- Single-site studies where centralizing data is simpler
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Skip Kaapana if you need a certified medical device for clinical deployment or want a managed service, since you would run Kubernetes and Helm yourself.
Running the platform means owning a server with at least 8 cores, 64 GB RAM and 200 GB storage, plus whatever object storage your archive grows into, so the infrastructure bill sits entirely on your side.
Kaapana is AGPL-3.0 open source: no licence fee at any stage, and cost is entirely infrastructure plus the people to run it. A single 8-core, 64 GB, 200 GB machine covers a pilot; multi-center federated studies push you toward a multi-node Kubernetes cluster with real engineering time. Compare that against general federated learning frameworks, which are lighter to install but leave you to assemble DICOM handling, a PACS and search index yourself.
In short
Kaapana — Open-source toolkit from DKFZ for building medical imaging platforms, from DICOM curation to federated analysis. Best for Research institutions with dedicated IT infrastructure, Multi-center study coordinators needing privacy-preserving analysis, Medical imaging researchers building AI models on decentralized data. Free to use.
What is Kaapana?
Kaapana — Open-source toolkit from DKFZ for building medical imaging platforms, from DICOM curation to federated analysis. Best for Research institutions with dedicated IT infrastructure, Multi-center study coordinators needing privacy-preserving analysis, Medical imaging researchers building AI models on decentralized data. Free to use.
Kaapana is an open-source toolkit (AGPL-3.0) from DKFZ, developed since 2020, for curating, processing and analyzing radiological and radiotherapeutic images at scale. It is a collection of specialized services rather than a fixed application: an integrated dcm4chee PACS and object store hold the data, OpenSearch indexes the metadata of every DICOM image for full-text search and cohort building, and Apache Airflow orchestrates containerized processing pipelines as DAGs. Out-of-the-box methods include TotalSegmentator and nnU-Net for segmentation and radiomics. Digital pathology converts whole-slide images to DICOM for review in the SLIM Viewer or OHIF, with automatically generated thumbnails for browsing large slide sets. Analysts work in integrated JupyterLab and RStudio environments or Collabora spreadsheets, and can stream full desktop applications such as MITK Workbench and 3D Slicer in the browser next to the data. Federated analysis lets multiple sites train models and share only models and aggregated results, so patient data stays on-site. Everything is packaged as Helm charts on Kubernetes, so any algorithm, tool or workflow can be containerized and installed from an extension marketplace with one click. Getting started means provisioning one Ubuntu or AlmaLinux machine with 8+ cores, 64 GB RAM and 200 GB storage, running an install script that sets up Kubernetes, and deploying the platform yourself. It suits research institutions and multi-center study teams with IT capacity. It is research software, not a certified medical device.
Key features
- Federated analysis across institutions without moving patient data
- Interactive DICOM curation with metadata filtering and tagging for cohort building
- Integrated PACS via dcm4chee archive
- Metadata indexing and full-text search in OpenSearch
- Workflow orchestration with Apache Airflow DAGs
- Containerized processing on Kubernetes and Docker
- Helm-charted extensions installed with one click from a marketplace
- nnU-Net integration for segmentation and model training
- TotalSegmentator integration for out-of-the-box segmentation
- Digital pathology: whole-slide image conversion to DICOM
- Digital pathology viewing with SLIM Viewer and OHIF
- Automatically generated pathology thumbnails for large slide datasets
- Interactive JupyterLab workspaces
- RStudio workspaces
- Collabora collaborative spreadsheets and reports
Our long-form take written Jul 26, 2026 is more than 90 days old, so it is being re-reviewed against current data and is not shown.
Models under the hood
as of 2026-10-24
Limitations
- Kaapana is research software, not a certified medical device, so it is not a drop-in for regulated clinical production.
- It is a toolkit of specialized services rather than a fixed application, so you assemble it for your use case.
- Deployment means running Kubernetes and Helm on your own hardware or cloud: the documented starting point is one Ubuntu or AlmaLinux machine with 8+ cores, 64 GB RAM and 200 GB storage, deployed by an install script, with multi-node cluster operation requiring real engineering effort.
- Extending the platform means containerizing methods and packaging them as Helm charts.
- Digital pathology and imaging support are built around DICOM rather than non-imaging data domains.
- There is no managed hosting option described in the vendor material, so operational responsibility stays with your team.
as of 2026-10-30
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Is Kaapana still active?
Kaapana is live · free · last verified 30 Oct 2026 · viability monitor.
Viability score
How well maintained and how widely used is Kaapana? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →What do users say about Kaapana?
What people actually say about Kaapana — is it worth it?
Our community sentiment research written Jul 3, 2026 is more than 90 days old, so it is being re-reviewed against current data and is not shown.
How we verified this
Every listed fact about Kaapana— pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 30 Oct 2026. Pages mix model-assisted synthesis with human review; see how we verify.
Verification history
We have re-verified Kaapana 11 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — changed pricing fit, limitations, description, pricing tiers, switching in, who should skip it, models, switching out, features, our verdict, setup time, hidden costs, summary
- — changed domain expiry
- — changed use cases, description, our verdict, features, hidden costs, integrations, limitations, switching in, switching out, who should skip it, pricing fit, setup time, summary, who it suits
- — changed limitations
- — changed SSL certificate, domain expiry
- — changed setup time, our verdict, switching out, switching in, who should skip it, hidden costs, pricing fit, features, pricing tiers
Showing the 6 most recent of 11 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does Kaapana cost in 2026?
Kaapana: Free to use. Pricing last verified 30 Oct 2026.
Plans compared
Every published Kaapana tier, its price as the vendor publishes it, who it fits, and what it adds over the tier below. The 12-month cost projection follows.
| Plan | Price | Billing | Who it's for |
|---|---|---|---|
| Open Source | Free | — | Research institutions and multi-center study teams with IT capacity that will self-host on their own hardware or cloud.Adds: Single open-source entry point under AGPL-3.0, with all platform features, the extension marketplace and community support via Slack and GitHub. |
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Where the pricing makes sense
The company stage and team size where Kaapana's pricing actually pencils out — and where peers do it cheaper.
Kaapana is AGPL-3.0 open source: no licence fee at any stage, and cost is entirely infrastructure plus the people to run it. A single 8-core, 64 GB, 200 GB machine covers a pilot; multi-center federated studies push you toward a multi-node Kubernetes cluster with real engineering time. Compare that against general federated learning frameworks, which are lighter to install but leave you to assemble DICOM handling, a PACS and search index yourself.
Has Kaapana's pricing changed?
No plan price for Kaapana has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick Kaapana?
Pick Kaapana if you are Research institutions with dedicated IT infrastructure, Multi-center study coordinators needing privacy-preserving analysis, Medical imaging researchers building AI models on decentralized data, Clinical data scientists running reproducible containerized pipelines.
Look elsewhere if you are Teams wanting a fully managed SaaS platform, Groups needing a certified medical device for clinical use, Single-site studies where centralizing data is simpler, Organizations without anyone able to run Kubernetes deployment.
Skip it if Skip Kaapana if you need a certified medical device for clinical deployment or want a managed service, since you would run Kubernetes and Helm yourself.
GeneratedReal-world workflow fit
Concrete scenarios for the personas Kaapana actually fits — and what changes day-one when you adopt it.
Three hospitals each install Kaapana on their own hardware, keep patient images local, and connect through federated analysis; Airflow DAGs run training at each site and only models and aggregated results travel back.
Outcome: The study runs across institutions with patient data never leaving the site, satisfying the privacy mandate that blocked a centralized pool.
You send a study's DICOM data to the platform, filter and tag series by metadata in the datasets view to carve out a cohort, then trigger TotalSegmentator or nnU-Net across the whole set with a few clicks.
Outcome: Segmentation results land back on the platform, where you open them in JupyterLab or RStudio without copying data anywhere.
Whole-slide images are converted to DICOM, reviewed in SLIM Viewer or OHIF, and large slide collections are browsed via automatically generated thumbnails; an in-house algorithm is packaged as a Helm-chart extension and installed from the marketplace.
Outcome: Pathology slides sit in the same governed platform as radiology data, and your method is shareable with other Kaapana instances.
Use cases
- Deploy a federated learning platform across hospitals so models train on local data and only parameters are shared.
- Run TotalSegmentator or nnU-Net across a whole DICOM dataset with a few clicks instead of scripting each case.
- Build study cohorts by filtering and tagging DICOM metadata in the gallery-style dataset browser.
- Convert whole-slide pathology images to DICOM and review them in SLIM Viewer or OHIF.
- Keep several multi-center studies separated on one platform with project-based data and access isolation.
- Analyze segmentation results in JupyterLab or RStudio without moving data off the platform.
- Stream MITK Workbench or 3D Slicer into the browser to process images where they reside.
- Package an in-house algorithm as a Helm-chart extension and share it with other Kaapana instances.
Setup time & first value
How long it actually takes to get something useful out of Kaapana — broken out by persona, not the marketing-page minute.
On the documented starting point, a single Ubuntu or AlmaLinux machine with 8+ cores, 64 GB RAM and 200 GB: allow an afternoon for provisioning and the install script that sets up Kubernetes and deploys the platform, then a day to load DICOM data and run a first workflow. Multi-node cluster deployment is a project, not an afternoon.
Switching to or from Kaapana
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From a standalone dcm4chee archive: send your DICOM data to the Kaapana platform, which uses dcm4chee as its internal PACS, and keep working from the same archive format.
- →From scripted nnU-Net or TotalSegmentator runs: containerize the method or use the built-in ones, then trigger it across datasets through Airflow DAGs instead of per-case scripting.
- →From manual cohort queries: send data to the platform and rebuild cohorts in the gallery-style datasets view, where OpenSearch indexes the metadata of every image.
- ↗To another DICOM archive: Kaapana stores images in its dcm4chee PACS, so export via standard DICOM rather than a proprietary format.
- ↗To a managed analysis service: pull results from JupyterLab or RStudio workspaces, but expect to rebuild curation and workflow logic externally since Kaapana's value is in the assembled platform.
Integrations
Resources & guides
Tutorials & Learning
YouTube returned 6 videos for “Kaapana”, and we withheld 6: 6 could not be judged, because “Kaapana” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Kaapana.
Official links
What are the best Kaapana alternatives?
The closest alternatives to Kaapana we list are CUREBench, Quibim and Mecha Health. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| CUREBench | Free | Free | — |
| Quibim | Contact Sales | Custom pricing | — |
| Mecha Health | Contact Sales | Custom pricing | — |
Alternatives to Kaapana
View all
CUREBench
CURE-Bench is an open-source benchmark grading AI therapeutic decision-making and software-engineering reasoning at scale.
Quibim
Quibim turns MRI, CT and PET scans into cleared quantitative imaging biomarkers for prostate, brain, liver, breast and lung.
Mecha Health
Radiology foundation models that turn medical images into complete draft reports for physician review.
Tools that pair well with Kaapana
Common stack mates teams adopt alongside Kaapana, with the specific reason each pairing earns its keep.
CUREBench
CURE-Bench is an open-source benchmark grading AI therapeutic decision-making and software-engineering reasoning at scale.
Quibim
Quibim turns MRI, CT and PET scans into cleared quantitative imaging biomarkers for prostate, brain, liver, breast and lung.
Mecha Health
Radiology foundation models that turn medical images into complete draft reports for physician review.
Kaapana vs its closest rival
Kaapana's closest head-to-head is with Rapidsos — Kaapana and RapidSOS serve completely different domains: Kaapana is a free, open-source toolkit for federated medical imaging AI in multi-center studies, while RapidSOS is a commercial emergency intelligence platform for 911 and public safety. Choose Kaapana if you need decentralized medical imaging analytics; choose RapidSOS if you need AI-powered emergency response integration with public safety networks. Verdict generated
Head-to-head comparisons
Kaapana vs Rapidsos
Kaapana and RapidSOS serve completely different domains: Kaapana is a free, open-source toolkit for federated medical imaging AI in multi-center studies, while RapidSOS is a commercial emergency intelligence platform for 911 and public safety. Choose Kaapana if you need decentralized medical imaging analytics; choose RapidSOS if you need AI-powered emergency response integration with public safety networks.
GeneratedKaapana vs Codametrix
Choose Kaapana if you need a free, open-source platform for federated medical imaging AI across multiple centers. Choose CodaMetrix if you're a large health system aiming to slash coding costs and denials with an enterprise-grade, KLAS #1 solution. They serve completely different needs—imaging research vs. revenue cycle automation.
GeneratedKaapana vs Isomorphic Labs
Kaapana is a free, open-source toolkit for federated medical imaging AI, best for multi-center research with on-premise deployment. Isomorphic Labs is a high-budget AI drug discovery platform for pharma partnerships, unavailable to individual researchers. Choose based on domain: medical imaging vs drug discovery, and budget: free self-hosted vs enterprise partnership.
GeneratedFrequently asked questions
Best-of guides
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