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

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

Early Renewal or Prepayment: Monument 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 Monument is consistently seen as a high-value, low-friction tool for predictive analytics, especially for non-technical teams that still want powerful models.

From what Spendbase clients share, a few themes stand out:

1) Onboarding and setup
– Quick purchase-to-setup journey
– Minimal configuration needed
– Smooth install on standard laptops, no heavy infra required

2) Ease of use / no-code experience
– Intuitive UI that feels natural even for non-engineers
– Drag-and-drop or point-and-click style workflows to import data and run algorithms
– Very short time to first insight: tasks that used to take hours or days in code now completed in under a minute

3) Performance and capabilities
– Strong performance directly on local machines, with fast algorithm execution
– Good variety of predictive algorithms out of the box
– Interactive plots and visualizations that make it easier to share findings across teams

4) Business impact we observe for clients
– Faster experimentation cycles for data science and analytics stakeholders
– Lower dependence on engineering resources, which helps keep internal costs down
– More teams (ops, finance, retail, etc.) using predictive insights day-to-day

5) Gaps and feature wishes
Our internal feedback highlights a recurring, but positive, pattern:
– Spendbase clients are satisfied with the current functionality but are actively asking for:
– More algorithms and advanced features
– Deeper integrations with existing data stacks
– Expanded monitoring and governance capabilities for production use

From a Spendbase discount perspective, this pattern usually means:
– Monument already replaces a fair amount of manual coding and consulting spend
– The main lever for additional savings is aligning license tiers with how many teams will actually build and run predictive apps
– It can be cost-effective to start with core users, then scale licenses as new features and integrations are released, instead of over-licensing upfront

In short, our data suggests Monument offers strong value in ease, speed, and power, with an active product roadmap that clients are eager to see grow.