For developers, the main quest is building and scaling their applications—not struggling with complex monitoring setups. Yet, observability in cloud-native environments is essential, and configuring monitoring for AWS services has traditionally been a complex and manual process. Developers had to set up Firehose streams, CloudWatch metric streams, and log subscriptions, all while ensuring continuous maintenance for new instances, turning observability into an unwelcome side quest.

Debugging distributed systems can often feel like searching for a needle in a haystack. When issues arise, engineers need faster ways to pinpoint critical spans within their traces. With our latest Deeper Trace Analytics update, SigNoz now enables powerful filtering for root and entry spans—making it significantly easier to analyze and debug distributed traces.

Messaging queues power modern distributed systems, handling background tasks, event-driven architectures, and real-time data streaming. However, debugging issues in Kafka and Celery queues has traditionally been a black box, with limited correlation between message producers, consumers, and broker metrics. With OpenTelemetry-powered Kafka & Celery monitoring, SigNoz introduces the industry's first fully integrated observability solution for messaging queues powered by OpenTelemetry.

Observability at scale is challenging—especially when dealing with high-volume distributed traces. Traditional tracing tools struggle with large traces containing thousands of spans, often leading to sluggish UIs and an unmanageable debugging experience. Most tracing tools we checked have a limit on the maximum spans they can load for a single trace. But with SigNoz, we’ve redefined what’s possible.

Today, we’re excited to announce a much-awaited feature in SigNoz: Infrastructure Monitoring, built natively on OpenTelemetry. Infrastructure monitoring is a critical aspect of modern observability. Without proper visibility into your infrastructure resources, troubleshooting issues, optimizing costs, and maintaining performance become challenging.

Answer a few questions to get personalized recommendations in 30 seconds Start Now The rising costs and complexities of monitoring cloud infrastructure are pushing many organizations to explore alternatives to Datadog. With monthly bills sometimes reaching thousands of dollars and feature sets that can be overwhelming, teams are looking for practical, cost-effective solutions that better fit their needs.

Both DataDog and Prometheus are application monitoring tools aimed to improve application performance. While Datadog is a cloud-based SaaS solution, meaning there's no need to install or maintain any infrastructure, Prometheus is an open-source tool that requires manual download and installation on your infrastructure. Let us compare DataDog and Prometheus to see which tool suits The biggest difference between Datadog and Prometheus is that while Prometheus is open-source, Datadog is proprietary.

Both Kibana and Grafana are data visualization tools providing users capabilities to explore, analyze and visualize data with dashboards. The difference between Kibana and Grafana lies in their genesis. Kibana was built on top of the Elasticsearch stack, famous for log analysis and management. In comparison, Grafana was created mainly for metrics monitoring supporting visualization for time-series databases.

ELK is the acronym Elasticsearch, Logstash, and Kibana, and combined together, it is one of the most popular log analytics tools. Elastic changed the license of Elasticsearch and Kibana from the fully open Apache 2 license to a proprietary dual license. The ELK stack is also hard to manage at scale. In this article, we will discuss 14 ELK alternatives that you can consider using.

Are you looking for Grafana alternatives? Then you have come to the right place. Grafana started as a data visualization tool. It slowly evolved into a tool that can take data from multiple data sources for visualization. For observability, Grafana offers the LGTM stack (Loki for logs, Grafana for visualization, Tempo for traces, and Mimir for metrics). You need to configure and maintain multiple configurations for a full-stack observability setup.