Comparison
ChatGPT Enterprise signs a BAA. Sanolith signs a BAA. Both qualify as "HIPAA-compliant." The differences are in what actually happens to PHI between the clinician typing and the model responding. This page documents those differences row-by-row.
Last updated: June 9, 2026. We update this comparison when OpenAI changes their offering.
HIPAA-aligned LLM workspace built ground-up for healthcare. Fail-closed redaction, per-tenant fine-tuning, clinical tools, model-agnostic.
Pick when
OpenAI's enterprise tier of ChatGPT. BAA available, broad model breadth for general productivity, polished UX. No automatic redaction.
Pick when
Each row links a capability to how Sanolith and ChatGPT Enterprise actually deliver it. Verified against published terms + product documentation as of the update date above.
| Capability | Sanolith | ChatGPT Enterprise |
|---|---|---|
| PHI auto-redaction before inference | Fail-closed redactor 40+ identifier categories. Request fails if redactor fails. | None User is the redactor. PHI in prompt = PHI to OpenAI servers. |
| Per-tenant fine-tuned model | Sano adapter per team Your data trains an adapter only you serve. ~$15 per training run. | Custom GPT GPT-4o base only. No fine-tuning at the weights level; system prompts + retrieved files. |
| Append-only tamper-evident audit ledger | Hash-chained Hourly Merkle checkpoints in immutable object storage. Subpoena-grade. | Workspace logs Activity logs of who chatted when. Not per-prompt-redaction-event audit. |
| Tenant-isolated RAG index | Yes Postgres + pgvector with row-level security per tenant. | Workspace files Files attached to a workspace. Embedding model is shared infrastructure. |
| Clinical tool catalog | Built-in PubMed, DailyMed (SETID), RxNorm, FAERS, web fetch. Citations on every answer. | GPT Actions Third-party plugins. No clinical tools shipped; you build them. |
| Model choice | GPT, Claude, Llama, Qwen, your own Switch per tenant without rewriting integrations. | OpenAI models only GPT-4o, GPT-4-turbo, etc. No Claude. No Llama. No bring-your-own. |
| Bring your own GPUs / on-prem | Yes, Enterprise tier Point at your vLLM cluster, AWS Bedrock account, or air-gapped inference. | No Inference is on OpenAI's infrastructure. No on-prem deployment. |
| BAA available | Yes (Team + Enterprise) Standard BAA template. Annual third-party HIPAA risk assessment. | Yes (Enterprise only) Enterprise tier required for BAA. Team/Plus tiers do not include BAA. |
| Pricing transparency | Public per-seat $499 / $1,990 / Custom. Per-team flat. No per-token overages. | Sales-quote only Enterprise pricing requires a sales conversation. Reported $60+/user/month. |
| Time to first chat | 15 minutes (Starter) Self-serve trial. Team-tier BAA in ~5 business days. | Days to weeks Enterprise onboarding via sales. SAML config + BAA paperwork. |
| Data deletion on churn | 60-day SLA Full export within 30 days, full deletion within 60. Certified destruction report on request. | 30-day SLA Standard OpenAI Enterprise terms. Some retention for legal hold. |
| Open-source / auditable inference stack | Yes vLLM + open-weight models. Source auditable. | Closed GPT-4o weights are proprietary. No source-level audit. |
Sources: OpenAI Enterprise privacy policy + BAA terms; ChatGPT Enterprise documentation; Sanolith product documentation. Where ChatGPT Enterprise behavior is ambiguous in public docs, we err toward their published position rather than speculation.
We genuinely think ChatGPT Enterprise is a fine tool for some healthcare workflows. Here's where each wins.
Pick Sanolith if
Clinicians type prompts that contain MRN + DOB without thinking about it. Sanolith catches them before inference. ChatGPT Enterprise doesn't.
Pick ChatGPT Enterprise if
If clinicians are only using LLMs for non-PHI workflows (research summaries, policy drafting against public sources), ChatGPT Enterprise's broader model breadth is enough.
Pick Sanolith if
DailyMed SETID citations on every drug-label answer + tenant-scoped RAG over the label library. Built-in. ChatGPT Enterprise: you build it.
Pick ChatGPT Enterprise if
If your team is mostly drafting general medical-information templates and doesn't need cited drug labels, GPT-4o is a more general model and fine.
Pick Sanolith if
Per-team fine-tuning means each therapeutic-area team trains a model on their own protocols. ChatGPT Enterprise: shared GPT-4o, no fine-tuning at weights.
Pick ChatGPT Enterprise if
For early-stage CROs with one protocol team and broad model needs (drafting non-clinical docs, lit review, etc.), ChatGPT Enterprise's UX polish matters.
Pick Sanolith if
Bring-your-own-model on Enterprise tier. Point Sanolith at your own vLLM cluster. ChatGPT Enterprise: inference must happen on OpenAI's infrastructure.
Pick ChatGPT Enterprise if
(Not viable for this use case. See Sanolith.)
Run through these. If three or more push toward Sanolith, the decision is probably clear.
1Do clinicians ever type PHI into prompts?
If yes, Sanolith's fail-closed redactor is the relevant feature. ChatGPT Enterprise has no automatic redaction; PHI typed into a prompt reaches OpenAI's servers in plaintext.
2Does your team need a model tuned on YOUR institution's data?
If yes, Sanolith's per-tenant Sano adapter fine-tuning gives you that without sharing weights across customers. ChatGPT Enterprise's 'Custom GPT' is system-prompt + file-attachment level, not weights-level.
3Do you need to use Claude, Llama, or a self-hosted model?
If yes, Sanolith routes to any of them. ChatGPT Enterprise is OpenAI-only.
4Does your privacy officer need to subpoena-grade audit specific prompts months later?
If yes, Sanolith's hash-chained append-only ledger is built for that. ChatGPT Enterprise's workspace logs are access-level, not per-redaction-event.
5Is your team committed to OpenAI as a vendor?
If yes, ChatGPT Enterprise is the path of least resistance. Sanolith is the choice for teams that want model flexibility, on-prem options, or vendor independence.
Self-serve trial. Walk through the redactor, the audit ledger, and the clinical tool catalog with a real prompt. Decide for yourself.