How Munch Centralized Data on BigQuery and Cut Costs by 50% with Mitzu

Company

Munch is the Central Europe’s leading food-saving marketplace, helping users rescue surplus meals and groceries from restaurants and retailers at a discounted price. With more than 4 million downloads, 500k MAU, and operations across several countries, Munch connects eco-conscious customers with thousands of partners through its mobile and web apps, as well as its B2B platform for partners.

Behind the scenes, Munch runs a complex ecosystem of user, partner, and operational data. As the business expanded rapidly, so did the volume of analytics questions coming from product, marketing, sales, and operations teams.

Challenge

Before switching to Mitzu, Munch relied heavily on Mixpanel and Looker for product and marketing analytics. But as the company scaled, the setup became fragile:

• Events broke often, dashboards became unreliable, and teams were unable to trust the numbers.
• Combining Mixpanel with Looker gave us only a limited view of the customer journey.
• Pricing based on event volume pushed costs up as Munch reached hundreds of millions of monthly events.

“We simply couldn’t rely on the data anymore. Even basic decisions became risky. And the event-based pricing model made it impossible to scale affordably.”

Research and selection process

During the selection process, the team evaluated a wide range of solutions. Like most fast-growing tech companies, they benchmarked major players, tested demos, checked integrations and reporting flexibility, and assessed how each tool would handle future data volumes. The goal was to find a platform that could support not just analysts, but the entire organization.

They also explored Amplitude, but quickly ran into the same issues they’d experienced before: high costs, limited flexibility, and a black-box data model, great for getting data in, much harder for doing anything outside the constraints of the product.

What Munch truly needed was:

• a reliable, centralized data model,
• a tool that teams could use confidently without relying on analysts,
• and pricing that actually made sense for a multi-country, rapidly scaling marketplace.

Final Solution Setup and Implementation

Munch chose Mitzu based on recommendations and for its ability to connect directly to their BigQuery data warehouse. By doing this, they kept all data within their own infrastructure instead of relying on third-party systems. The move also brought consistency: events and business logic now matched across every tool they use. This setup fit naturally into Munch’s existing stack and worked smoothly alongside their tools: BigQuery, AVO, Segment, Adjust, and Looker.

Onboarding was smooth. They quickly established the new data model, migrated the key events, and within a few weeks the entire organization was operating inside the new system. The team repeatedly highlighted the level of hands-on help they received.

“More people use analytics now than ever before. The transition was smooth, and the insights we started uncovering genuinely helped us make better decisions.”

Today, Munch uses Mitzu daily across:

• Product & UX
• Marketing & CRM
• Sales & Partner Success
• Customer Support

“Mitzu finally gave us a real self-service BI layer. We keep full control of the modeling.”

Results

1. Data literacy skyrocketed up by 3x

Teams across product, sales, marketing, and customer success now understand and use data daily.

More people use analytics. More questions get answered. Fewer bottlenecks.

2. Analytics costs dropped ~50%

Predictable license-based pricing eliminated event-based fees. Munch now scales without financial problems.

3. Insights arrived 2x faster, even complex ones

PMs saw value within weeks. Teams quickly uncovered issues such as:

• They investigated rising subscription churn and found that some subscription decisions had previously been made without any data insight
• How users behaved around new features

4. Data engineering workload decreased

Mitzu minimised ad-hoc support. Most questions are solved through self-service by product marketers and managers instead of data team tickets.

It reduced the operational burden immediately. We no longer spend time fixing broken events or answering repetitive questions, most non-technical teams can now explore data independently. The data model lives in BigQuery, so engineering keeps full control, but without being dragged into daily troubleshooting. We can go even deeper, exporting the underlying SQL, adjusting logic, and building advanced models directly in BigQuery.

Who should use Mitzu?

Albert (CMO) and Réka (PM) recommend Mitzu to:

• companies with large event volumes,
• events from multiple resources such as app and website,
• organizations looking for a more affordable, scalable alternative to Mixpanel or Amplitude.

“Anyone who wants a powerful, flexible analytics tool at a fair price will love Mitzu. It’s perfect if you want to scale without breaking the bank.”