ClinicFrame vs Kastra: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of ClinicFrame and Kastra — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

ClinicFrame

Paid

Ambient AI medical scribe that transcribes visits in real time and delivers a structured SOAP note within seconds.

Ambient Voice Capture: Runs on the clinician's laptop and records the patient visit in real time without requiring extra hardware, in person or on telehealth.
Specialty-Trained Transcription: Reaches 96% transcription accuracy across 15+ medical specialties using models trained on specialty-specific clinical language.
Structured Note Generation: Produces SOAP notes for medicine and DAP/BIRP notes for behavioral health seconds after the visit ends, plus configurable custom templates.
EHR Copy-In: Notes can be pasted into Epic, Athenahealth, Cerner and Healthie in under 30 seconds, so it slots into existing charting workflows.
HIPAA-Aware Handling: Audio is discarded after the note is generated and a signed Business Associate Agreement is available on request.
Flat Monthly Pricing: Priced per clinician per month with no per-hour surcharges, replacing traditional human scribing services.

Primary Care Documentation: A family physician sees back-to-back patients and finishes the day with SOAP notes already drafted, cutting after-hours charting time.
Behavioral Health Notes: A therapist runs 50-minute sessions and gets DAP or BIRP notes generated automatically instead of writing them up between clients.
Telehealth Visits: A remote clinician on a video call has the conversation transcribed and structured without a separate device or human scribe on the line.
Multi-Specialty Clinics: A clinic covering cardiology, dermatology and psychiatry gets specialty-appropriate note formatting from a single tool.
Scribe Cost Reduction: A practice that has been paying hourly for human scribes switches to a flat monthly software subscription.

Kastra Labs Inc

Freemium

Kastra is the runtime authorization layer for AI: it decides in sub-milliseconds what agents, models, and tools are allowed to do before they act.

Sub-Millisecond Policy Decisions: Every prompt, tool call, and API request is evaluated in under a millisecond so enforcement never becomes the bottleneck.
Deterministic Policy Engine: Rules are written and evaluated deterministically, not by an LLM judge, so the same input always produces the same allow/deny.
Kastra Edge (Local Enforcement): A local enforcement component that runs next to the agent so decisions happen even without a network round-trip.
Cryptographic Audit Trails: Signed logs of every decision give security and compliance teams tamper-evident evidence of agent behavior.
Coding Agent Integrations: First-class hooks for Claude Code, Cursor, Codex, and OpenClaw let policies wrap the tool calls those agents already make.
Kastra Recon: Discovers what actions an agent actually attempts in a codebase or environment, so policies can be authored from observed behavior instead of guesses.
Zero Implicit Trust Model: Nothing an agent asks to do runs until it is explicitly allowed by policy, aligning agent access with zero-trust principles.

Guardrails for Coding Agents: Prevent an autonomous coding agent from dropping tables, force-pushing to main, or leaking secrets during long runs.
Enterprise Rollout Approvals: Give security teams a control plane before letting employee-facing AI agents access internal APIs.
Regulated-Industry Agent Deployments: Provide the auditable authorization trail required in finance, healthcare, or government agent pilots.
Multi-Agent Systems: Enforce per-agent scopes so a research agent can read data but only a deploy agent can trigger production changes.
Incident Forensics: Reconstruct exactly what an AI agent was allowed or blocked from doing after a suspicious action.