Cassandra Alternative

Looking for a Cassandra alternative? Decide whether you want to build the model or just use it

Cassandra is a no-code builder for Marketing Mix Modelling - powerful for teams who want to configure and own their model, but it still requires someone to build, calibrate and interpret it. Brands looking for a Cassandra alternative are often after the output of MMM - a budget recommendation - without the model-building step in between.

MANUAL MODEL CONFIGURATION Requires continuous technical ownership. Your team must handle model calibration, Robyn syntax parameters, and subjective scenarios. Robyn/Open-Source OverheadCalibration Duty

➔automated

AUTONOMOUS DIRECT DECISIONS Pangolin automates the data science pipeline end-to-end, serving human-approved, profit-optimized recommendations directly. Managed Profit Allocation

See how Pangolin compares using your own data

The Drivers

Why businesses look for a Cassandra alternative

Teams typically seek a managed path over pure no-code modelling platforms for a few core reasons:

01 Someone still has to build the model Cassandra's no-code UI removes the need to write custom scripts, but configuring, training and calibrating the model remains your internal responsibility.

02 DTC & ecommerce aren't the sole focus Cassandra serves a wide operational net including B2B, universities, and charities. Its standard presets and configurations aren't tuned explicitly for high-growth DTC marketing mixes.

03 No profit-based optimisation target Cassandra's Budget Allocator calculates scenarios using revenue-based ROI projections rather than optimizing directly for contribution profit metrics.

04 No autonomous execution Cassandra surfaces scenarios and allocations for your team to act on, so putting a recommended change live stays a manual step rather than an approve-and-execute one.

Landscape Overview

Different types of alternatives

TYPE 1 Attribution-first platforms Platforms like Triple Whale and Northbeam, centred on multi-touch attribution and real-time operational dashboarding.

TYPE 2 Enterprise measurement Platforms like Rockerbox and Sellforte, offering deeper omnichannel support and multi-market enterprise integrations.

TYPE 3 Incrementality platforms Tools like Measured, proving incremental lift through controlled, geo-matched and platform experimentation tracks.

TYPE 4 Self-serve MMM toolkits Platforms like Forvio, offering similar custom model-building capabilities for teams with in-house analytic resources.

TYPE 5 Continuos, decision-first MMM Pangolin's category: a managed Bayesian MMM system that maintains the model for you, optimized directly for contribution profit decisioning.

Pangolin Position

What is Pangolin?

Pangolin is a continuos marketing measurement platform built around Bayesian Marketing Mix Modelling. Rather than providing a builder for you to configure, Pangolin models channel contribution, saturation and contribution profit automatically once your data is connected, then generates a specific AI-recommended budget reallocation that a human approves before it goes live. It's built specifically for DTC and ecommerce brands.

Direct Comparison

How Pangolin compares to Cassandra

Capability	Pangolin	Cassandra
Marketing Mix Modelling	Core, Bayesian, managed	Core, Bayesian (built on Meta's Robyn), self-configured
Model ownership	Modelled and maintained by Pangolin	Built and configured by the user via a no-code UI
Contribution profit optimisation	Core feature	Not a primary focus
Autonomous execution (human-approved)	Yes, core to the product	Not a core feature
Vertical focus	DTC and ecommerce specifically	Broad - ecommerce, B2B, universities, charity, fintech
Agency support	Not a current focus	Dedicated agency workspaces and pricing

The Core Difference

Pangolin vs Cassandra

Cassandra's no-code builder gives teams direct control over their MMM, which suits marketers or agencies who want to own the modelling process and inspect how it works, built as it is on Meta's open-source Robyn framework. Pangolin takes the opposite approach: no building or configuration step at all, just a managed model that produces a contribution-profit-optimised recommendation automatically. If what you want is control and ownership of the model, Cassandra fits; if you want the recommendation without the build, Pangolin does.

When Pangolin is the right choice ✓You want a managed system that generates the recommendation, not a model you configure yourself ✓Contribution profit needs to drive the decision, not just revenue-based ROI scenarios ✓You're a DTC or ecommerce brand and want a platform built specifically around that channel mix ✓You'd rather approve a specific recommendation than build and run your own allocation scenarios

When another solution may be better ○If you or your agency want direct, no-code control over building and adjusting the MMM, Cassandra's builder is designed for that ○If you're operating outside DTC/ecommerce, in B2B, charity or another vertical, Cassandra's broader focus may suit you better ○If you're an agency managing MMM across multiple clients, Cassandra's dedicated workspace tooling is built for that use case ○If your team already has the statistical familiarity to configure and calibrate a model directly, that ownership may be worth the extra setup

Platform Interface

From data to a better budget decision

Data➔ Measurement➔ Insights➔ Recommendations➔ Better budget decisions

Live Bayesian MMM Output A live view of channel contribution, saturation and contribution profit, with AI-generated budget recommendations ready for approval.

Meta AdsActive saturation limitsStable

Google SearchOver-claiming creditReduce

TikTok VideoHigh marginal profit curveScale

Live Action Recommendations · No dedicated analyst required · Optimised for contribution profit · One click to approve

Common Inquiries

Frequently Asked Questions

Why would a brand look for a Cassandra alternative?

Common reasons include not wanting to build and configure a model themselves, wanting a platform built specifically for DTC and ecommerce, and wanting a contribution-profit optimisation target rather than revenue-based scenarios.

Is Pangolin a direct replacement for Cassandra?

For DTC and ecommerce brands wanting a managed, no-configuration approach, yes. For agencies managing multiple clients across varied verticals, or teams wanting hands-on control of their model, Cassandra's builder serves a different need.

What type of alternative should I choose?

It depends on whether you want to build the model or just use its output. Brands wanting a managed recommendation tend to fit Pangolin; teams or agencies wanting direct control over configuration may prefer Cassandra.

Does Pangolin let me configure my own model like Cassandra?

No. Pangolin's model is built and maintained for you once your data is connected, with no configuration step required from your end.

How does pricing compare?

Cassandra's pricing is structured around self-serve and agency use cases. Pangolin is priced transparently around growth-stage DTC and ecommerce brands specifically.

Skip the model building. Get straight to the budget decision.

See what an automated, profit-optimised recommendation looks like for your own brand.

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