Pricing & User Upload Strategy

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As the next generation of our analytics platform matured, users increasingly asked for the ability to upload and manage their own geospatial assets—not just consume platform-provided data. This shift created a new challenge: user uploads were technically feasible, but we lacked a pricing structure to support ongoing storage and management at scale.

At the same time, storage footprints were growing rapidly due to higher data volumes and more advanced processing outputs.

 

UP42 2025

Product Research

TL;DR

Users increasingly wanted to upload and manage their own geospatial data, but the platform had no pricing model to support large-scale storage. At the same time, storage usage was growing rapidly due to higher data volumes and processing outputs.

Through competitive research and user interviews, we found that most users download data quickly, rarely revisit it, and see platform storage as a temporary staging area. They value visualization and metadata access but don’t want to pay for long-term storage they don’t actively use.

Problem Statements

Problem 1 — User uploads require a sustainable pricing model.

Users wanted to bring their own data, process it, and organize it using existing data management tools. But without a pricing model, large-scale storage would quickly become costly and unsustainable.

Problem 2 — Storage growth needed a predictable cost structure.

As platform usage increased, storage consumption grew significantly—both from user activity and automated processing outputs. A scalable pricing model was required to ensure long-term viability.

Motivation

This was the right moment to define a holistic data management offering that would:

  • Open a new, recurring revenue stream

  • Enable user uploads at scale

  • Give users clarity around storage usage

  • Allow the platform to manage storage costs more proactively

Scope

The exploration focused on three dimensions:

1. Pricing Model

Potential directions included:

  • Margin-based pricing tied to data ordering

  • Subscription-based storage tiers

  • Usage-based, pay-as-you-go storage

  • Hybrid models

Key considerations included:

  • Free allowances

  • Time-to-live (TTL) for data

  • Credit vs. direct billing

  • Handling of expired or unpaid storage

2. Data Handling Rules

To price storage accurately, we needed clarity on what “counts”:

  • Raw uploaded files

  • Processed derivatives

  • Analytics outputs

  • Temporary or cached products

We also explored TTL options to encourage active data management and reduce long-term storage bloat.

3. Payment Logic

Once the model was selected, the pricing logic needed definition:

  • Unit costs (e.g., per GB, per asset)

  • Billing intervals

  • Consumption measurement

  • Handling overdue payments

Research

Competitive Landscape

Evaluated storage and data management pricing across major players in geospatial, cloud, and analytics platforms.

Identified four primary pricing archetypes:

  1. Usage-based / metered storage

  2. Subscription tiers

  3. Free tier → paid expansion

  4. Custom enterprise agreements

Most competitors offered some version of free storage to onboard users, followed by predictable or metered pricing.

User Behavior Insights

Across interviews and data analysis, we found:

  • Users download most data within days or weeks of receiving it.

  • Re-access is rare, but the option to re-access is highly valued.

  • Visualization tools were heavily used as a “source of truth.”

  • Many teams exported data to external cloud providers for long-term retention.

  • Storage was often seen as a temporary staging area, not a primary archive.

Users expressed:

  • Positive reactions to visualization features

  • Frustration with inconsistent metadata and tagging

  • Interest in lighter-weight processing tools

  • Concerns around paying for long-term storage they rarely use

User Pricing Perceptions

Feedback themes:

  • Reluctance to pay for data they immediately export

  • Desire for guaranteed access to data they purchased

  • Interest in pricing models aligned with frequency of access

  • Expectation that data metadata remains accessible, even if storage expires

Working Solution

A free baseline storage allowance became the central concept.

Users would receive a set amount of free space. Once full, they could either:

  1. Delete older assets, or

  2. Enable automatic deletion, or

  3. Upgrade for additional storage

Benefits:

  • Supports zero-friction onboarding

  • Keeps short-term “in/out” workflows free

  • Enables uploads and new feature adoption

  • Encourages strategic use of storage

This also sets the stage for more advanced pricing models as the platform evolves.

Recommendations

As a phased approach:

Phase 1 — Free Allowance + Paid Expansion

  • A fixed amount of free GB to support early workflows

  • Additional storage billed monthly

  • Automatic cleanup available for cost-conscious users

Phase 2 — Behavior-Aligned Pricing

Evolve pricing as features mature:

For short-term users:

  • Free short-term storage with auto-deletion (e.g., 6-month TTL)

For processing-focused users:

  • Pay for processing only; store inputs/outputs locally

For visualization-heavy teams:

  • Paid long-term storage with guaranteed access

Phase 3 — Platform-wide Pricing Review

Recommend establishing a cross-functional group to review pricing, account structure, and platform value holistically, ensuring a consistent user and business experience.