Clinical systems · Open-source health data API
Metriport
Metriport is an open-source health data API that pulls a patient's records from exchanges, EHRs, labs, pharmacies, and ADT feeds into one normalized FHIR record.
Strong fit
- Digital health companies and startups that want outside records through one API
- Value-based care teams that want record summaries, suspected conditions, and care gaps from the full record
- Engineering teams that want to read the open-source code before they sign
Weak fit
- Health systems that want a long-established vendor with many years of references
- Practices that need fax and Direct messaging inboxes
- Teams without engineers to work with an API
Bottom line
Metriport is a San Francisco company that sells healthcare data infrastructure built on an open-source API. Care teams and software companies use it to pull a patient's records from health information exchanges, EHRs, labs, pharmacies, and admission and discharge alerts, normalized into one FHIR record. It suits engineering-led teams that want one integration and want to read the code.
Score breakdown
Weights: Outcomes 35% · Product 30% · Implementation 20% · Pricing clarity 15%.
Metriport was founded in 2021 by Dima Goncharov and Colin Elsinga and went through Y Combinator in summer 2022. Its platform connects to health information exchanges, ADT networks, pharmacies, labs, and EHRs, then delivers the data through an API, a data warehouse, EHR apps, or an embedded dashboard. Newer features summarize records with AI and flag suspected conditions and HEDIS care gaps.
Metriport announced a $26 million round in August 2026, led by TJ Parker of Matrix, and named Amazon One Medical, Sollis Health, and Color Health as customers.
Compare Zus Health when the team wants a shared record maintained by the vendor, Health Gorilla or Kno2 when QHIN status is required, and Particle Health when a similar national records API from an older vendor fits better.
Competitor landscape
| Vendor | Overall | Ease of implementation |
|---|---|---|
| Particle Health | 7.4 | 7.0 |
| Kno2 | 7.3 | 7.3 |
| Zus Health | 7.2 | 7.0 |
| Metriport | 7.2 | 7.2 |
| Redox | 7.1 | 6.8 |
| Health Gorilla | 6.9 | 6.8 |
| 1upHealth | 6.7 | 6.5 |
Pricing
| Item | Detail |
|---|---|
| Model | Usage-based platform pricing quoted by Metriport, scaled by patients and data services. |
| What usually drives cost | Number of patients queried, ADT alert volume, and add-ons such as AI summaries, care gaps, and EHR apps. |
| What to ask in diligence | Price per patient or per month at your volume, what each add-on costs, and support terms for production. |
| Published pricing | No public list price. |
Prerequisites for purchase
| Need | Why it matters |
|---|---|
| What you need to get Metriport to function | |
| Engineers assigned to the integration | The product is an API first. |
| Patient demographics quality | Matching depends on clean data. |
| Permitted purpose for queries | Network rules apply. |
| Data warehouse or EHR target chosen | Records need a home. |
| Security review of open-source components | Reviewers will ask about the code. |
| What will maximize your value | |
| Turn on ADT alerts early | Admission alerts are the fastest win for care teams. |
| Use the data warehouse for analytics | Avoids building another pipeline. |
| Measure time to first record | Shows clinicians the data is current. |
| Deal-breakers | |
| No engineers | |
| Needs fax inbox | |
| Requires many years of enterprise references | |
Value creation time frame
| # | Stage | Typical range |
|---|---|---|
| 1 | Sandbox and security review | 2-4 weeks |
| 2 | API integration | 4-8 weeks |
| 3 | Production launch | 2-4 weeks |
Methodology
| Weight | Factor | What it measures |
|---|---|---|
| 35% | Customer outcomes | Whether buyers get measurable operational or clinical-workflow results after go-live |
| 30% | Product | Capability depth, reliability, and fit for the job the category actually buys |
| 20% | Implementation | How hard it is to stand up, integrate, train, and stabilize |
| 15% | Pricing clarity | Whether a buyer can model total cost without a mystery quote |
| Label | Meaning |
|---|---|
| Highly recommend | Strong outcomes and product with manageable caveats |
| Recommend | Solid fit for the right buyer; know the tradeoffs |
| Conditional | Only with a specific use case or heavy caveats |
| Not recommended | Avoid for most buyers in this category |
Read our full methodology for how we weight scores and assign recommend labels.