7.5 Overall
MEDPLUM

Medplum

Recommend Scored Oct 2026

Medplum is an open-source developer platform for building custom EHRs, patient apps and care tools on FHIR data, free to self-host or offered as a paid hosted service.

medplum.com

Strong fit

  • Digital health companies with engineers who want to build a custom EHR
  • Teams that want open-source code and no vendor lock-in
  • Buyers who want posted prices for hosting

Weak fit

  • Clinics that want a ready-made EHR without developers
  • Teams without engineers to build and maintain screens
  • Buyers who need a vendor to run clinical workflows for them
Medplum product interface

Bottom line

Medplum is a strong choice for digital health companies with engineers who want to build their own EHR or care app on open-source, FHIR-based tools. It suits teams that want to avoid lock-in and see prices up front. Clinics that want a ready-made EHR should look elsewhere.

Score breakdown

6.9
Speed to a working product
8.0
Developer platform
6.8
Building on the platform
9.0
Knowing what you will pay

Weights: Outcomes 35% · Product 30% · Implementation 20% · Pricing clarity 15%.

Reshma Khilnani, Cody Ebberson and Rahul Agarwal started Medplum, and Khilnani is chief executive. The founders had earlier built MedXT, a medical imaging startup that Box bought, and held engineering roles at Box and One Medical. Medplum went through Y Combinator in 2022.

Medplum is a headless EHR, which means it stores clinical data and handles logins, permissions and connections, while each customer builds its own screens. It stores data in FHIR, the standard format for exchanging health data, and its core code is free under the Apache 2.0 license.

It includes a clinical data repository, an API, React components for building screens, and server-side automations called Bots. Its paid plans add lab, e-prescribing and HL7 interface connections. Medplum says it is SOC 2 and HIPAA compliant, and its Enterprise plan supports ONC certification.

Medplum says companies on its platform care for more than 20 million patients and have run more than 120 million workflow actions. It is built for software developers, so most buyers need engineers to build and maintain their app.

Canvas Medical fits care teams that want a ready-made EHR they can extend with code. Healthie fits virtual care and nutrition practices that want a configurable EHR, Metriport fits teams that mainly need patient records from national networks, and Zus Health fits teams that want a shared patient record service.

Competitor landscape

VendorOverallEase of implementation
Healthie7.77.4
Canvas Medical7.57.2
Medplum7.56.8
Metriport7.27.2
Zus Health7.27.0

Pricing

ItemDetail
ModelMedplum is free to self-host. Its hosted service has a free tier and monthly plans, and enterprise plans are priced by quote.
What usually drives costFor Medplum, cost depends on the hosted plan, your usage tier, and enterprise needs such as dedicated hosting, audit support and ONC certification.
What to ask before you buyAsk Medplum which usage tier fits your traffic, what support each plan includes, and how much your own team must build.
NoteMedplum's core code is free under the Apache 2.0 license, so you can host it yourself at no license cost.
Published pricingMedplum's hosted plans are free for testing, $2,000 a month for Production and $6,000 a month for Premium. Enterprise pricing is by quote.

Leadership

Methodology
WeightFactorWhat it measures
35%Customer outcomesWhether buyers get measurable operational or clinical-workflow results after go-live
30%ProductCapability depth, reliability, and fit for the job the category buys
20%ImplementationHow hard it is to stand up, integrate, train, and stabilize
15%Pricing clarityWhether a buyer can model total cost without a mystery quote
LabelMeaning
Highly recommendStrong outcomes and product with manageable caveats
RecommendSolid fit for the right buyer; know the tradeoffs
ConditionalOnly with a specific use case or heavy caveats
Not recommendedAvoid for most buyers in this category

Read our full methodology for how we weight scores and assign recommend labels.