Granola vs Basil AI: Why 'Bot-Free' Isn't the Same as On-Device (2026 Privacy Architecture Breakdown)

If you've been searching is Granola AI private or is Granola on-device, the short answer is: Granola is bot-free, but it is not on-device. Granola captures audio locally on your Mac or PC instead of joining your meeting as a visible bot, but the audio is then transmitted to four cloud subprocessors — Deepgram and AssemblyAI for transcription, OpenAI and Anthropic for summarization — before transcripts are stored in an AWS Virtual Private Cloud. That architectural choice has real privacy consequences that the marketing language around "no bots" tends to gloss over.

This article compares Granola's actual data flow, as documented on its own security pages and in independent reviews, with a fully on-device alternative like Basil AI. We'll cover what changed when Granola raised $125M at a $1.5B valuation in March 2026, what "bot-free" actually means at the network layer, where the privacy gaps remain for lawyers, clinicians, and regulated industries, and how a truly on-device design eliminates the subprocessor question entirely.

What Granola Actually Is in 2026

Granola was founded in 2023 by Chris Pedregal (a former Gmail PM) and Sam Stephenson, headquartered in London. In TechCrunch's coverage of its March 2026 funding round, the company secured $125 million in Series C funding led by Danny Rimer at Index Ventures, with participation from Mamoon Hamid at Kleiner Perkins, reportedly at a $1.5B valuation. The pitch resonated because users might not like bots in meetings visibly taking notes, but a lot of them don't mind if an app on someone's computer is doing the transcription.

That single design choice — capture audio on the device instead of dialing a bot into the call — drives Granola's entire UX advantage. Independent reviewers have noted the same thing. A tl;dv hands-on review across 20+ real calls describes Granola as a prosumer app that sits on your computer, transcribes meetings without a bot joining the call, and generates Notion-style notes afterward. Alongside the Series C, Granola also launched Spaces — team workspaces with granular access controls — alongside a personal API (available on Business and Enterprise plans) and an enterprise API (Enterprise only) for integrating meeting context into broader AI workflows.

But "no bot in the call" is a statement about the meeting platform, not about where your audio is processed. Those are two very different privacy questions.

Where Granola's Audio Actually Goes

This is the part the marketing doesn't lead with. Granola's own security page states that Granola uses best-in-class transcription providers (like Deepgram and Assembly) and AI providers (like OpenAI and Anthropic) to summarize your meeting. In other words, every Granola transcript passes through at least one third-party ASR vendor and at least one third-party LLM vendor before it lands in your notes panel.

An independent strategy breakdown of Granola's stack on Michael Goitein's Substack confirms the architecture: AI Models: Granola selectively calls OpenAI, Anthropic, and Google models instead of building its own model. Transcription Services: Granola partners with Deepgram and Assembly AI for its automatic speech recognition ("ASR") needs. Cloud Infrastructure: Granola uses standard cloud providers to give it optionality and scalability for its backend services.

Granola's security page also clarifies storage: Granola doesn't store the audio from meetings - it transcribes in real time on macOS/Windows, or after your meeting using temporarily cached audio on iOS. It only stores the transcript and any notes you provide from a call, and notes are stored in our US-hosted AWS Virtual Private Cloud. They are encrypted at rest and in transit. The audio is discarded after transcription, which is a meaningful improvement over services like Otter that keep recordings indefinitely — but it is not the same as audio that never leaves the device.

Comparison Table: Granola vs Basil AI vs Otter

Here's the actual architectural difference, side by side:

The 'Bot-Free' Trick — What It Solves and What It Doesn't

Bot-free capture solves a real problem. The Otter litigation has made the costs of bot-based capture impossible to ignore. EPIC Insurance Brokers' January 2026 analysis notes that in the federal class action complaint in California, Brewer v. Otter.ai, Inc., Brewer alleges various federal and state law violations by Otter.ai, Inc. (Otter) related to its AI-powered meeting assistant, Otter Notetaker, which joins Google Meet, Zoom, and Microsoft Teams meetings as a participant with Otter Notebook accountholders and transmits data directly to Otter in real time for processing and transcription purposes.

By removing the visible bot, Granola sidesteps the most obvious form of the consent problem. But the underlying wiretap-statute concerns don't fully evaporate. A Bloomberg Law analysis published January 5, 2026 warns that the law governing these "notes" is lagging. Companies face a patchwork of state and federal laws, evolving court decisions, and real uncertainty. This backdrop raises questions about how a beneficial technology could be viewed as wiretapping, and notes that California's Invasion of Privacy Act is among the country's strictest wiretapping statutes. Within that scheme, vendor classification is a key unsettled issue.

If your bot-free tool still ships audio to a vendor for processing, vendor classification questions still apply. Bot-free helps the etiquette and consent optics; it doesn't change the fact that a third party (Deepgram, AssemblyAI, OpenAI, or Anthropic) is now technically able to read the contents of a privileged or sensitive conversation.

For a deeper look at why this matters for attorneys specifically, see our buyer's guide for lawyers on privilege-waiver risk.

SOC 2 Type 2 Is Not the Same as 'Your Data Stays With You'

Granola is rightly proud of its compliance posture. The company's SOC 2 Type 2 announcement notes that companies needed the peace of mind of an independent SOC2 Type 2 audit. So we've been busy working on it, and now it's here. A separate independent review at efficient.app confirms Granola earned SOC 2 Type 2 certification in July 2025, and independent auditors confirmed it meets the standards for customer data privacy and confidentiality. From the practical side, no bot joins your meeting, and audio is temporarily cached only for transcription, and once that's done the audio is deleted.

SOC 2 Type 2 is meaningful — it verifies that the controls a vendor claims to have are operating over time. But it is a statement about a vendor's internal handling of your data, not a guarantee that your data is unreadable to that vendor or its subprocessors. The single most private architecture is the one where no data is transmitted at all, because there is nothing for a SOC 2 audit to be about.

HIPAA Status

Healthcare buyers should pay close attention here. A January 2026 Feisworld review notes that if you are in healthcare (HIPAA), note that they are not HIPAA compliant yet (as of early 2026), but they mention it is on the roadmap. Without a Business Associate Agreement (BAA), Granola cannot lawfully be used to capture conversations that contain protected health information under HHS HIPAA rules on business associates. An on-device tool sidesteps this entirely because no PHI is transmitted to a business associate in the first place.

For a deeper dive on therapist-specific workflows, see our guide to HIPAA-compliant notetaking for therapists.

The AI-Training Default Most Buyers Miss

One of the more surprising details in Granola's policy: training defaults vary by plan. Per Granola's own documentation, by default on Free and Business plans, anonymised data may be used for Granola's own model improvements. You can opt out at any time: go to Settings → Preferences → Data & sharing and turn off "Use my data to improve models for everyone." Third parties like OpenAI and Anthropic are never allowed to train on your data — we have enterprise agreements preventing this.

On the Enterprise tier, admins can configure org-wide opt-out in Settings → Workspace > General under Data security. On the Business plan, each user needs to opt out individually. And if you opt out late, I opted out — was my historical data already used for training? We cannot guarantee that anonymised data wasn't used before you changed the setting. That's an honest disclosure, and it's worth respecting. But it also illustrates the inherent friction of cloud architectures: the user has to actively manage settings to achieve a baseline that an on-device architecture provides by default.

An itsconvo review from May 2026 flags this directly, noting real gaps for team meetings (weak speaker ID at 3+ people), high-volume users (25-note free cap), privacy-conscious teams (AI-training opt-out gated to Enterprise at $35/user/month), and anyone wanting help during the call rather than better notes after.

What 'On-Device' Actually Means

To understand why this matters, it helps to look at how Apple itself describes the privacy trade-off. Apple Security Research's Private Cloud Compute paper spells out the engineering reality: one of the key reasons such designs can assure privacy is specifically because they prevent the service from performing computations on user data. Since Private Cloud Compute needs to be able to access the data in the user's request to allow a large foundation model to fulfill it, complete end-to-end encryption is not an option. That is true of every cloud AI architecture, no matter how well-engineered: if the cloud has to compute on your data, the cloud has to be able to read it.

Apple's strategy explicitly favors keeping computation on-device whenever possible. Apple has long championed on-device processing as the cornerstone for the security and privacy of user data. A May 2026 MacDailyNews analysis highlighted the systemic benefit: no centralized data troves: Unlike cloud AI services that aggregate vast amounts of user data in data centers, on-device processing keeps information disaggregated and under the user's control.

And a 2026 technical overview from Stackviv on on-device vs cloud AI makes the compliance point bluntly: for businesses operating under GDPR, HIPAA, or CCPA regulations, on-device processing often simplifies compliance automatically. There's no need to audit what happens to data on remote servers if that data never goes there. This is the difference between hoping a vendor's controls hold and structurally not needing them to.

How Basil AI Solves This

Basil AI is built around a single architectural commitment: meeting audio, transcripts, and summaries never leave your device. Concretely:

• Audio capture: Mic + system audio captured locally on macOS or iOS — like Granola, with no bot in the meeting.
• Transcription: Apple's on-device Speech Recognition framework runs on the Apple Neural Engine. No Deepgram, no AssemblyAI. Documented at Apple Developer documentation for the Speech framework.
• Summarization and action items: Apple's on-device Foundation Models, plus Basil's own local processing — no OpenAI, no Anthropic, no Google.
• Storage: Your device, optionally synced to your own iCloud, never to a vendor cloud.
• Subprocessors: None for meeting content. There is no AWS VPC of yours we host.
• Training: N/A. Your audio is not in our infrastructure, so it cannot train anyone's model.

This is a more constrained product in some dimensions — there is no "enterprise context layer" pulling meeting data into Claude across your team, and there's no server-side admin console aggregating everyone's notes. For some teams that's a feature gap. For lawyers handling privileged communications, clinicians handling PHI, and executives handling M&A discussions, it's the whole point.

If you want a head-to-head against the cloud-bot leaders, see our bot vs bot-free comparison for Google Meet and Teams and our broader roundup of bot-free Mac notetaker alternatives.

A Buyer's Checklist for 2026

If you're evaluating notetakers this quarter, here are seven concrete questions that cut through marketing language:

1. Where is audio transcribed? Get a named answer — "on-device," "Deepgram," "AssemblyAI," "our own model in AWS."
2. Which LLM provider generates summaries? If the answer is OpenAI, Anthropic, or Google, your transcript is being sent to a third party in plaintext.
3. What is the list of subprocessors? Every vendor with SOC 2 publishes one. Count the parties with technical access.
4. What is the default training setting on each plan? Free, Pro, and Enterprise often differ.
5. Is there a BAA available? If no, the tool is off-limits for PHI.
6. What happens to historical data when I opt out? Most vendors cannot retroactively scrub training sets.
7. Does it work offline? If not, it is not on-device.

Granola will answer most of these honestly — it has surprisingly transparent documentation. The point is not that Granola is hiding something; it's that even with its strong disclosures, the answers reveal a cloud-processing architecture. An on-device tool gives a shorter, simpler answer to every question: "the data is on the user's device."

The Bottom Line

Granola earned its $1.5B valuation by solving the social-friction problem of bots in meetings. That is a real product win, and the SOC 2 Type 2 certification, GDPR posture, and contractual prohibitions on third-party training are meaningfully better than the Otter-era default. If your threat model is mostly "I don't want a Zoom bot embarrassing me in front of a client," Granola is a perfectly reasonable choice.

But if your threat model includes regulators, opposing counsel, biometric privacy plaintiffs, or simply "I don't want my words on someone else's GPU," the bot-free model still has gaps that only an on-device architecture closes. Basil AI is built for the second group: the cleanest answer to "where does my audio go?" is "nowhere."

Try the only meeting notetaker that never sees your audio

Basil AI runs Apple's Speech Recognition and Foundation Models entirely on your iPhone or Mac. No Deepgram, no AssemblyAI, no OpenAI, no Anthropic, no AWS. Just your meeting, transcribed and summarized on your device.