Number of Seats/Users: Zegami 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 Zegami 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 Zegami. Zegami reserves discounts for new customers or those not currently under promotional pricing.

Volume or Usage Commitment: Zegami 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 Zegami. Such companies are often in a position to negotiate for better pricing.

Early Renewal or Prepayment: Zegami 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 Zegami is very positively received by Spendbase clients, especially for visual data exploration and faster insight generation.

Overall sentiment
Most Spendbase clients highlight that Zegami:

• Speeds up pattern and outlier detection
• Is visually intuitive and pleasant to work with
• Makes it easier to spot data quality issues that would otherwise be missed

Key strengths our clients talk about:

• Powerful visual views
• Graph and scatter views are mentioned as standout features
• Flexible axes configuration helps teams explore data from multiple angles

• Grid-based image browsing makes it easy to compare many images at once

• Ease of use and setup

• Creating and editing collections is described as “easy” and “smooth”

• Non-technical users can interact with complex image and structured data without heavy training

• Real impact on data quality and experiments

• One client working on wheat growth and nitrogen use detected a serious imaging timing issue (3‑hour system stop) only after using Zegami’s visual exploration
• This prevented misleading analytical results caused by changes in ambient light

• Clients see Zegami as a practical tool for validating experiments and cleaning data before building models

• Support for better machine learning

• By surfacing outliers and inconsistencies visually, teams can improve training data
• This leads to more accurate models and clearer understanding of what’s happening in production datasets

Main pain points our internal data highlights:

• Zoom behavior
• Some clients report that zooming into tiles can be inconsistent
• Examples include zooming too far into a single tile or landing on partial tiles
• This doesn’t block usage but can slow down detailed inspection work

Summary for Spendbase clients considering Zegami:

• Strong fit if you work heavily with images or mixed data and need to:
• Validate experiments and detect anomalies quickly
• Improve data quality before ML modeling

• Give domain experts a visual, low-friction way to explore complex datasets

• Primary trade-off:

• A mostly smooth UI with occasional quirks around zoom and tile navigation, which clients would like to see refined.