An honest comparison
monō vs Hebbia
Hebbia reads the documents. monō acts on them.
Our honest position
Hebbia is excellent at document-heavy retrieval and analysis for finance and the professions. monō uses that same capability as one tile in a broader harness that takes action, drafting, deciding, shipping, inside your perimeter on your models.
| Dimension | Hebbia | monō |
|---|---|---|
| Core job | Read + analyse documents | Read + act + ship |
| Breadth | Document intelligence | Whole-business operations |
| Model choice | Managed | Model-agnostic |
| Data residency | Vendor-managed | Your cloud, inference-only |
| Action | Surfaces findings | Agents execute downstream |
| Cost to run | Per-seat enterprise licence | Harness efficiency + BYO keys |
| Compounds over time? | No memory that compounds across your tools and teams | Yes, one memory across your tiles, people and time, inside your tenant |
Core job
Hebbia: Read + analyse documents
monō: Read + act + ship
Breadth
Hebbia: Document intelligence
monō: Whole-business operations
Model choice
Hebbia: Managed
monō: Model-agnostic
Data residency
Hebbia: Vendor-managed
monō: Your cloud, inference-only
Action
Hebbia: Surfaces findings
monō: Agents execute downstream
Cost to run
Hebbia: Per-seat enterprise licence
monō: Harness efficiency + BYO keys
Compounds over time?
Hebbia: No memory that compounds across your tools and teams
monō: Yes, one memory across your tiles, people and time, inside your tenant
Cost-to-run is illustrative, same outcome, materially fewer tokens; with your own API keys, your rates, no markup. All marks belong to their owners.
No FUD
When they're the right call.
- Your core need is deep document search and analysis.
- You want a focused retrieval product, not autonomous execution.
- You’re comfortable with a vendor-managed model approach.
For deep document search and analysis specifically, Hebbia is a powerful focused tool.
The architecture difference
Three decisions they can't retrofit.
Sovereign by architecture
Inference-only. Your cloud, your keys, the data never builds anyone else's product.
Model-agnostic
Frontier, open-source, fine-tuned, swap models without rewrites.
Your own API keys
Your lab accounts, your negotiated rates. We never mark up your tokens.
In depth
The fuller picture.
What Hebbia is built for
Hebbia is excellent at document-heavy retrieval and analysis for finance and the professions. For deep document search and analysis, it is a powerful, focused tool. Its job is to read and analyse.
From reading to acting
monō uses that same reading capability as one tile in a broader harness that acts: drafting, deciding and shipping, inside your perimeter on your own models. Hebbia surfaces findings; monō acts on them downstream with governance on every step. When the work moves from analysis to execution, that is the line between the two.
FAQ
Questions, answered straight.
- Is monō a document-intelligence tool like Hebbia?
- Reading is one tile, not the whole job. monō acts on documents by drafting, deciding and shipping downstream. Hebbia is focused on document search and analysis.
- Can they work together?
- Yes. Analysis can feed agent work. monō turns a finding into a completed, governed task.
- Where does the work run?
- In your own cloud, inference-only, on your own models, with data inside your perimeter.
- When should I choose Hebbia?
- When deep document search and analysis is specifically your need, Hebbia is a powerful focused tool.
See it on your own use case.
Your use case, live on the platform, beside whatever you're evaluating.