Petabyte Scale, Gigabyte Costs: Mezmo’s ElasticSearch to Quickwit Evolution

Many engineering teams rely on ElasticSearch for search and analytics, but as data volumes grow, so do the challenges of scale, cost, and performance. At Mezmo, we faced this reality head-on, recognizing the need for a more efficient and scalable solution to support our multi-cluster, multi-petabyte telemetry data backend.

After extensive evaluation, we made the leap to Quickwit, an open-source, cloud-native search engine for logs. But making such a fundamental architectural shift—without disrupting customers—was no small feat.

Join this webinar to hear how we navigated the transition, the key decision-making frameworks we used, and the architectural challenges we overcame. Whether you're exploring alternatives to ElasticSearch or preparing for your own backend transformation, this session will provide practical insights from a real-world migration.

What You’ll Learn:

• Signs it was time for Mezmo to move from ElasticSearch to Quickwit
• The decision framework that guided our selection process
• Key engineering and architectural hurdles—and how we tackled them
• Best practices for major infrastructure changes with minimal customer impact
• Lessons learned and what we’d do differently next time
• If you're facing scaling challenges, cost pressures, or considering a move to open-source solutions, this session is for you.

Recognizing When to Move On – Learn the signs that indicated it was time for Mezmo to transition from ElasticSearch to Quickwit, including scalability limits, cost concerns, and performance challenges.

Making the Right Choice – Get insights into the structured decision-making framework Mezmo used to evaluate and select Quickwit as the best-fit solution for their multi-petabyte telemetry backend.

Executing a Seamless Migration – Understand the key engineering and architectural hurdles Mezmo faced, how they tackled them, and best practices for making large-scale infrastructure changes without disrupting customers.