Number of Seats/Users: Deepnote offers discounts based on the number of users or seats. A higher volume typically leads to a better discount structure, especially for enterprise-level usage.

Commitment to a Long-Term Contract: Discounts are often provided to companies that commit to using the service for a longer period (e.g., 1-year or 3-year contracts). A longer commitment shows Deepnote that the company is serious and willing to invest in the platform long-term.

No Active Discounts: Companies that are not already receiving other active discounts may have a better chance of qualifying for additional discounts from Deepnote. Deepnote reserves discounts for new customers or those not currently under promotional pricing.

Volume or Usage Commitment: Deepnote offers discounts if you agree to a minimum amount of usage or transaction volume. This could be based on data storage, transactions, or other metrics tied to how much you will use the platform.

Enterprise-Level Negotiation: Companies that qualify as “enterprise” due to their size or complexity (e.g., having large teams, multiple departments, or geographical presence) may be eligible for additional discounts from Deepnote. Such companies are often in a position to negotiate for better pricing.

Early Renewal or Prepayment: Deepnote often offers discounts if you renew early or prepay for the entire contract period. This is a common approach to securing a lower price.

Here at Spendbase our internal data shows that Deepnote is seen as a strong, team-focused data science workspace, especially for groups that live in notebooks every day.

From what Spendbase clients tell us, the main strengths cluster around:

• Real-time collaboration
• Described as “hands-down the best” for working together in the same notebook.
• Multiple people can edit, comment, and discuss work without heavy setup.

• Very popular for group projects (both in academia and in teams).

• Ease of use and onboarding

• Clients highlight that it is “easy to use” and feels intuitive.
• The browser-based UI and cloud execution mean less friction with local setups.

• Setting up environments is seen as simple and fast.

• Productivity for data science

• Optimized for statistics and data science workflows.
• Organizing notebooks and projects is straightforward, which helps teams keep things tidy.
• Sharing is flexible: visual outputs and code can both be shared in a clean way, making stakeholder updates smoother.

At the same time, our internal feedback also surfaces some consistent friction points:

• UX and stability quirks
• When several people work in the same project, the page can jump or “skip to random places,” which interrupts focus.

• Some clients need to reload the page to see outputs or displays correctly, which can be frustrating.

• Data import/export

• Importing and exporting data is sometimes described as “hard” or not as smooth as the rest of the experience.
• This can slow down workflows where data must move frequently between tools.

From a Spendbase discount and optimization standpoint, the pattern we see is:

• Deepnote delivers high value for:
• Data teams that collaborate heavily on notebooks.
• Organizations wanting to reduce friction of environment setup and sharing.
• Potential negotiation and optimization opportunities:
• Teams that rely heavily on complex data pipelines might want to balance Deepnote with other data tooling and avoid overbuying seats that are not collaboration-heavy.
• Highlighting the UX issues (page reloads, scrolling jumps, data import/export friction) can be useful leverage when discussing pricing tiers and renewals.

Overall, our internal data shows that Spendbase clients feel Deepnote brings strong collaborative benefits and a modern workflow for data science teams, with some clear, manageable areas where the product could be smoother.