How Zyte unified multi-cloud logging chaos into a single, lightning-fast platform

Business context

Zyte delivers reliable, compliant web data at enterprise scale, handling the full complexity of extraction, anti-bot resilience, and data delivery so customers can focus on their core business. Backed by 15+ years of web-data engineering expertise, Zyte is the trusted partner companies rely on when web data must work under scale, scrutiny, and SLA.

As an API-first company serving enterprise customers worldwide, their infrastructure spans multiple clouds and on-premise systems to deliver reliable web scraping services. With logging data scattered across Google Cloud, Oracle Cloud, and on-premise ELK stacks, their engineering teams struggled to get the unified visibility needed for effective troubleshooting and customer support.

The challenge: three logging systems, zero unified visibility

Before Bronto, Zyte's logging infrastructure was a complex patchwork of disparate systems that created a heavy management overhead. Developers reported that queries would frequently time out or take too long when analyzing large volumes of data, which was a major blocker for troubleshooting.

How Bronto helped

Zyte implemented Bronto as their unified logging layer, consolidating their fragmented multi-cloud and on-premise logging infrastructure into a single platform. The migration eliminated the complexity of managing separate systems while providing the fast, flexible search capabilities their developers and support teams needed for effective troubleshooting.

Zyte evaluated several established observability vendors and found their pricing models prohibitively complex and expensive — Bronto was 10x more cost effective than one specific vendor. With legacy players, costs tended to compound unpredictably across ingestion, parsing, and add-on modules, making it difficult to forecast spend or budget with any confidence.

Implementation details

Implementation was relatively straightforward thanks to Zyte's existing Kubernetes infrastructure. Bronto's native container support meant log forwarding required minimal configuration changes — essentially just redirecting their existing log streams to Bronto's endpoints. The team took a low-risk coexist-then-replace approach, running Bronto in parallel initially before switching over and disabling GCP logging for most clusters. Teams across the organization — from engineering and support to data and billing — could quickly start using Bronto directly without training or complex onboarding. The migration from their legacy on-premise Elasticsearch is progressing as applications naturally move to the new unified platform.

Operational and business impact

The transformation in search performance has been dramatic. Where Oracle Cloud searches previously took over 7 minutes or timed out entirely, Zyte now gets instant results across their entire logging infrastructure. This performance improvement scales seamlessly with their 35TB monthly log ingestion volume, enabling fast search across large data volumes without any performance degradation. The ability to correlate logs across all infrastructure components means complete request tracking is now effortless rather than a multi-system investigation.

Operationally, the shift to unified troubleshooting has transformed how Zyte's customer support team works. When customers report API issues, support can now track complete request journeys with a single search, eliminating the previous need to manually correlate data across GCP, Oracle Cloud, and Elasticsearch. The JSON field parsing capability enables flexible search and filtering on any structured log field, making complex investigations straightforward. Engineering, support, data, and billing teams all access the same unified platform, with integration into their existing Grafana dashboards providing familiar visualization while centralizing all log analysis in one place.

From a business perspective, Zyte has achieved significant cost savings by reducing their dependency on expensive GCP logging while gaining access to a transparent, predictable pricing model. The team is actively consolidating their infrastructure by eliminating their on-premise Elasticsearch deployment and reducing their GCP logging footprint, which will deliver additional operational savings as the migration completes.