FHIR Data Stores Compared (HAPI / HealthLake / Azure / Google)
Choose a managed FHIR store (AWS HealthLake, Azure Health Data Services, or Google Cloud Healthcare API) when you want a compliant, scalable FHIR server without running infrastructure — ideal if you're already on that cloud and value analytics integrations. Choose self-hosted HAPI FHIR (the open-source Java server) when you need full control, no per-store cloud fees, cloud portability, or deep customization. The trade is the classic one: managed removes operational burden and lock-in-frees you less; self-hosted maximizes control and portability at the cost of running it yourself.
Quick answer
Want it managed, scalable, cloud-integrated → HealthLake / Azure / Google. Want control, portability, no per-store fees → self-hosted HAPI FHIR.
Decision table at a glance
| Criterion | HAPI (self-host) | Managed cloud |
|---|---|---|
| Operational burden | You run it | Provider runs it |
| Cost model | Infra + your team | Managed service fees |
| Control / customization | Full | Within service limits |
| Cloud portability | High (any host) | Locked to that cloud |
| Scaling & HA | Your responsibility | Handled by provider |
| Analytics / ML integration | Build it | Native to the cloud |
| Compliance (infra) | You own more of it | Shared with provider |
What they are
What is HAPI (self-host)
HAPI FHIR is the leading open-source FHIR server (Java), which you deploy and operate on your own infrastructure or containers. You control the version, storage, extensions, and scaling, with no managed-service fees — in exchange for owning deployment, upgrades, security, and operations.
What is Managed cloud
Managed FHIR stores — AWS HealthLake, Azure Health Data Services, and Google Cloud Healthcare API — are fully-hosted, compliant FHIR servers run by the cloud provider. They handle scaling, availability, and much of the compliance surface, and integrate with each cloud's analytics, ML, and data services out of the box.
Key differences
Operational burden is the core trade
A managed store means the provider handles scaling, high availability, patching, and infrastructure compliance — you consume an endpoint. HAPI self-hosted means you own all of that. For a small team without deep DevOps, managed removes a large, ongoing responsibility; for a team that wants control, self-hosting is that control.
Portability vs lock-in
HAPI runs anywhere — on-prem, any cloud, hybrid — so you're not tied to a vendor. Managed stores bind you to AWS, Azure, or Google, and migrating off later is non-trivial. If multi-cloud or exit-optionality matters, self-hosting keeps doors open; if you're committed to one cloud, lock-in is a smaller concern.
Analytics and ecosystem integration
Managed stores shine when you want to do more with the data: HealthLake integrates NLP and analytics, Azure and Google tie into their data/ML stacks and warehouses. Recreating those pipelines on self-hosted HAPI is possible but is engineering you'd own. If analytics/ML on FHIR data is central, managed can be a big accelerator.
Cost shape
Self-hosting trades service fees for infrastructure plus the people to run it — potentially cheaper at scale, but only if you have the team. Managed pricing is predictable and includes operations, but per-store/per-operation fees grow with usage. Model both against your data volume and team reality rather than assuming open-source is automatically cheaper.
When to use each
When to use Self-Hosted (HAPI FHIR)
- You need full control over configuration, extensions, and FHIR version.
- You want cloud portability and to avoid managed-service lock-in.
- Per-store or per-operation cloud fees don't fit your cost model.
- You have the DevOps capacity to run, scale, and secure a stateful server.
- You're building on-prem or in a multi/hybrid-cloud environment.
When to use Managed (HealthLake / Azure / Google)
- You're already on AWS, Azure, or GCP and want native FHIR without running servers.
- You want the provider to own scaling, availability, and infra-level compliance.
- You value built-in analytics/ML integrations (e.g. HealthLake's NLP, BigQuery export).
- You'd rather trade some control and cloud lock-in for lower operational burden.
- You need to stand up a compliant FHIR store quickly with a small team.
Frequently Asked Questions
Not sure which fits your build?
Tell us about your product, integration targets, and timeline. We'll map the trade-offs to your context and recommend the path that gets you to a compliant launch fastest.