Last EHR vs NonBioS.ai

Last EHR and NonBioS.ai are both ai agent apps tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

Last EHR

The tool wraps four FHIR operations — patient search, chart read, note creation, and observation recording — into a chat agent that runs as the signed-in user, bounded by whatever Medplum AccessPolicy you already have. Every write surfaces as an approval card showing exactly what will be saved before it touches the chart. The same four tools expose as an MCP server for Claude Desktop or Claude Code, read-only by default. The ceiling is visible early: four tools, one backend, one EHR platform. Teams that need more than that are writing their own.

NonBioS.ai

NonBioS positions itself as an agentic full-stack builder: you describe what you want, and it plans, codes, installs dependencies, and deploys — operating inside a Linux VM with minimal hand-holding from you. The workflow is closer to delegating to a junior engineer than dragging components onto a canvas. For solo founders building booking systems, internal dashboards, or early SaaS MVPs, the promise is a production-ready app without a DevOps setup. The ceiling appears when your product logic grows beyond what a single high-level instruction can specify cleanly — at that point, the agent's planning assumptions and yours start to diverge.

Attribute	Last EHR	NonBioS.ai
Pricing	Free	Paid
Price	—	$9/mo to $199/mo
Free trial	No	No
Open source	Yes	No
Has API	Yes	No
Self-hosted option	Yes	No
Platforms	Web, self-hosted	Web-based SaaS; agent executes in Linux VM environment
Released	—	2024
Pros	Approval-gated writes — the agent proposes, you confirm, nothing saves silently — so you can prototype AI chart edits without a runaway write reaching a patient record. Runs as the signed-in Medplum user scoped by your existing AccessPolicy, which means you get access control for free instead of rebuilding it in the agent layer. Stores no patient data of its own, so there is no second compliance surface to audit or secure beyond what Medplum already covers. Provider-agnostic model key (OpenAI or Anthropic), so switching models is a single environment variable change rather than a code change when costs or output quality shift. MCP server mode exposes the same four tools to Claude Desktop or Claude Code read-only by default, so teams can wire AI chart access into existing developer tooling without building a separate integration.	Full-stack deployment handled autonomously — including dependency installation and service startup — so you skip the DevOps setup that typically blocks a solo founder's first production deploy. Agentic debugging loop means the tool attempts to resolve build failures on its own rather than surfacing a stack trace and stopping, which means fewer interruptions during a build session. Freemium entry point lets you validate whether the agent's output matches your mental model of the app before committing budget, reducing the risk of paying for a tool whose defaults don't fit your use case. Linux VM runtime means the agent is executing real code in a real environment rather than simulating behavior in a sandboxed preview, so what you see is closer to what actually runs in production.
Cons	The agent covers exactly four FHIR tools. Any clinical workflow that requires a fifth operation — scheduling, prior auth, document retrieval — hits a hard stop, and teams extend by forking the codebase, at which point they are maintaining their own agent layer. Medplum is the only supported FHIR backend. Teams running Azure Health Data Services, Google Cloud Healthcare API, or a self-hosted HAPI instance cannot use this without replacing the backend integration entirely — at which point they are evaluating tools built for backend-agnostic FHIR. A managed tier with BAA coverage is listed as in development and not yet available. Teams under HIPAA that cannot self-host and sign their own BAA with Medplum have no production-ready path here, and will move to a vendor with signed agreements in place.	Ambiguous requirements produce unpredictable output: when your product spec contains branching logic or multi-step user flows that are hard to express in a single instruction, the agent makes assumptions — and correcting those assumptions through repeated re-prompting takes longer than writing the feature directly. Teams with complex data models hit this within the first two or three build iterations. No API access and no self-hosted option mean the generated application and its runtime are locked inside NonBioS infrastructure. Teams that need to plug the output into an existing deployment pipeline, enforce data residency, or own the execution environment cannot do so — and this is the condition under which teams move to a self-hosted agent framework like Cursor or a code-generation layer they can run locally. Credit-based usage on the free tier creates unpredictable build costs: longer agent loops — triggered by complex requirements or repeated debugging cycles — consume credits faster than a simple one-shot build, making it difficult to estimate how far a free allocation stretches before a paid tier is required.

Bottom line

Last EHR is free while NonBioS.ai is paid; Last EHR is open source; only Last EHR exposes a public API. Choose based on which difference matters most for your workflow.

Frequently asked questions

What is the difference between Last EHR and NonBioS.ai?

Last EHR is Free and open source, while NonBioS.ai is Paid. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is Last EHR better than NonBioS.ai?

It depends on your workflow. Use the side-by-side attributes (pricing, open source, API, self-hosted, platforms) to decide. AIDiveForge does not rank a universal winner — we publish verified facts so you can choose.