Asimily vs Ordr
The two KLAS scored challengers, 96.6 against 89.4, and close enough that the axis you weight decides it. Both grade A on interoperability and setting coverage. Asimily leads on the independent rating and on pre purchase device evaluation, assessing risk before a device is even bought. Ordr leads on the machine learning and the enforcement model: behavioural fingerprinting on a stated 100 million plus devices generates segmentation policy from learned behaviour, earning an A on AI centrality where Asimily sits at B, and its autonomy design is the best in the lane, simulating enforcement impact and showing the blast radius before any rule changes with a human gate. That autonomy edge is the substantive one, because automatically isolating a misidentified infusion pump is a patient safety event. If your failure mode is the top independent rating and upstream procurement risk, start with Asimily. If your failure mode is segmentation that stalls on distrust of asset data, start with Ordr.
- The higher independent rating: ranked first in the KLAS 2026 Healthcare IoT Security report at 96.6, against Ordr's reported 89.4 in the same report, the most direct comparison available in this lane.
- Assessment upstream of procurement: pre purchase device evaluation assesses risk before a device is bought, moving security ahead of installation rather than inheriting it afterward, a capability Ordr does not match.
- Cross industry reach with healthcare depth retained, graded A on setting, spanning IoMT, IoT, OT, and IT across medical, laboratory, and general connected fleets.
- The stronger AI centrality case: behavioural fingerprinting on a stated 100 million plus devices generates segmentation policy from learned behaviour rather than templates, graded A against Asimily at B, whose moat is a curated knowledge base.
- The better autonomy design, and it is the axis that matters most here: policies are simulated and the blast radius shown before enforcement with a human gate, graded A against Asimily at B, because isolating a misidentified clinical device automatically is a patient safety event.
- Falsifiable deployment: initial discovery within 48 to 72 hours and enforcement in weeks, with segmentation policy generated and validated against real traffic rather than authored from templates.
Side-by-Side
| Axis | A Asimily |
O Ordr |
|---|---|---|
| AI Centrality | ||
| Autonomy and Oversight Model | ||
| Model and Technology Transparency | ||
| Clinical and Operational Evidence | ||
| AI Safety and PHI Stewardship | ||
| HIPAA and BAA Posture | ||
| Security Certifications and Trust Center | ||
| FDA and Regulatory Status | ||
| AI Governance and Bias Disclosure | ||
| EHR and Interoperability Depth | ||
| Deployment Model and Data Residency | ||
| Commercial Transparency | ||
| Setting and Specialty Coverage |
This is the closest pairing in the lane, since both grade A on interoperability and setting coverage and both are KLAS scored, 96.6 against 89.4. The splits are specific: Asimily leads on independent rating and pre purchase evaluation, Ordr on AI centrality and autonomy design, where Ordr's A rests on documented enforcement simulation and a human approval gate. A source caution on Ordr: it publishes its own comparative rankings of competing platforms, so favourable comparative material from the vendor is self interested and this index relies on it only for Ordr's own product claims. Both are cloud based, both report passive agentless monitoring, and neither publishes a security attestation or pricing.