Granola vs Omi
Granola vs Omi: side-by-side comparison of two voice-first / wearable ai memory systems — architecture, taxonomy, license, pricing, MCP/A2A support, and direct edges.
Cost & capability
| Granola | Omi | |
|---|---|---|
| Capability band | entry | competent |
| Capability composite | 40 | 42 |
| Cost tier | free | mid |
| $/Mtok input | 0 | — |
| $/Mtok output | 0 | — |
| Use cases | Analytical Summarization, Memory Augmented Chat | Latency Sensitive, Offline Capable, Memory Augmented Chat, Long Running Session |
Where they differ (12)
Rows where both sides have data and the values disagree — the shortlist of dimensions that actually distinguish these two systems.
| Granola | Omi | |
|---|---|---|
| Capability band | entry | competent |
| Capability composite | 40 | 42 |
| Cost tier | free | mid |
| Use cases | Analytical Summarization, Memory Augmented Chat | Latency Sensitive, Offline Capable, Memory Augmented Chat, Long Running Session |
| Type | Mac/iOS meeting notepad — system-audio tap | Open-source wearable orb + memory graph |
| Created | 2023-05 | 2024-03 |
| Pricing | Free + paid | Hardware-bundled |
| Funding | $192M total $1.5B val Series C · 2026-03 | $2M seed (Jan 2025; Draper Associates, 468 Capital; prior $700K bootstrapped) |
| Backend storage | custom | searched not found |
| Deployment | Managed-only | Both |
| Embedding | locked | searched not found |
| Multi-tenancy | hard-isolation | searched not found |
At a glance
| Granola | Omi | |
|---|---|---|
| Section | Voice-first / wearable AI memory | Voice-first / wearable AI memory |
| Tier | T1 | T1 |
| Type | Mac/iOS meeting notepad — system-audio tap | Open-source wearable orb + memory graph |
| Created | 2023-05 | 2024-03 |
| Latest release | — | v0.11.378+11378… 2026-05-06 |
| License | — | MIT |
| GitHub | — | 12.4k★ +104/mo Dart |
| Pricing | Free + paid | Hardware-bundled |
| Funding | $192M total $1.5B val Series C · 2026-03 | $2M seed (Jan 2025; Draper Associates, 468 Capital; prior $700K bootstrapped) |
| Backend storage | custom | searched not found |
| Deployment | Managed-only | Both |
| API surface | searched not found | searched not found |
| Embedding | locked | searched not found |
| Multi-tenancy | hard-isolation | searched not found |
| MCP | searched not found | searched not found |
| A2A | searched not found | searched not found |
| OpenTelemetry | no — consumer product | no — consumer product |
| Optimised for | always-on capture + lifelog timeline + voice UX | always-on capture + lifelog timeline + voice UX |
| Anti-fit | not for enterprise compliance-regulated environments without explicit consent flows | not for enterprise compliance-regulated environments without explicit consent flows |
Taxonomy
| Axis | Granola | Omi |
|---|---|---|
| storage | file | graph |
| retrieval | extraction-pull | graph-traversal |
| persistence | long-term | lifelong |
| update | append-only | append-only |
| unit | episode | episode |
| governance | user-controllable | user-controllable |
| conflict | append | append |
Pros & cons
Granola
Pros: Captures system audio without joining as a meeting bot — works for any meeting tool without admin rules; enterprise tier opts out of model training.
Cons: Mac/iOS only — no Windows/Linux story; cloud processing despite local-audio claim.
Omi
Pros: Open-source hardware + firmware + cloud — only wearable lifelog with a fully inspectable stack; users can pick their LLM provider.
Cons: DIY-leaning — less polished than vendor-managed competitors; community support is the support model.