ClinicFrame vs Experiential Labs: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of ClinicFrame and Experiential Labs — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
ClinicFrame
ClinicFrame
Ambient AI medical scribe that transcribes visits in real time and delivers a structured SOAP note within seconds.
Key features
- 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.
Best for
- 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.
Experiential Labs
Experiential Labs
Open-source AI gateway that routes every model through one endpoint at provider cost, then improves that traffic with caching, routing and fine-tuning.
Key features
- Unified Model Endpoint: One OpenAI-compatible POST endpoint fronts every hosted provider, your own bring-your-own keys and your own GPUs, so switching models is a parameter change rather than an integration.
- Zero-Markup Routed Tokens: Routed traffic bills at the provider's list price with 0% added on top, with the company earning on hosted inference and the Pro plan instead of on your tokens.
- Model Recommendation from Traffic: The intelligence layer watches real request patterns and tells you when switching models would win, including newly released models on the day they ship, with optional per-prompt optimization.
- Caching Opportunity Detection: Identifies where cache hit rate could improve and shows the projected savings, with repeated tokens returning at 90% off once enabled.
- Traffic-Trained Custom Models: Fine-tunes a model on your own traffic and proves it in closed-loop simulation before it ever serves, then exposes it through the same endpoint you already call.
- Spend Attribution Console: Breaks requests and dollars down by agent, person, model, provider and day across the whole organization, alongside catalog, usage and limits.
- Live Request Logs and Metrics: Streams per-request time-to-first-token, token counts, provider, status and cost, with dashboard rollups for requests, spend, p50 TTFT and cache hit rate.
- Governance Controls: Budgets, provider allowlists and attribution are available from the free tier upward for controlling who can spend what on which models.
Best for
- Consolidating Multi-Provider Access: Replace separate SDKs and keys for OpenAI, Anthropic, Google and others with a single endpoint and key across every application.
- Cutting Inference Spend: Use caching recommendations and model-switch suggestions to lower the cost of an existing production workload without changing application code.
- Replacing a Frontier Model with a Small One: Distill or fine-tune a small model on your own traffic for a narrow repetitive task and serve it at a fraction of frontier-model cost and latency.
- Chargeback and Budgeting: Attribute AI spend to individual agents, teams or people for internal cost allocation and to enforce per-key budget caps.
- Evaluating New Model Releases: Compare a newly shipped model against your current one on your own traffic before committing to a migration.
- Hybrid Local and Hosted Serving: Route some workloads to self-hosted GPUs at zero marginal cost while sending the rest to hosted providers through the same interface.
- Self-Hosting the Gateway: Run the open-source gateway inside your own infrastructure when hosted routing is not an option.