Operations | Monitoring | ITSM | DevOps | Cloud

It’s 3 AM. Your phone screams. You stumble to your laptop, eyes half-closed, wondering the same question every SRE has asked mid-incident.

Log volume is exploding, costs are rising, and most teams are stuck duct-taping together short-term fixes. During our webinar, "Optimizing Log Management in Datadog: Cut Costs Without Losing Insights," we discuss how DevOps and engineering leaders are navigating the growing pains of observability, especially in environments where tools like Datadog are mission-critical but challenging to manage. Here’s a recap of the key takeaways.

Datadog may be the default choice for all-in-one observability, but its logging experience takes a back seat to the broader platform. Logs are primarily designed to feed into metrics and traces, which leads to tradeoffs such as slower search, complex workflows, and a UI that isn’t optimized for log investigations. As a result, Datadog doesn’t align with how developers actually troubleshoot.

By Sara Miteva Sr. Product Marketing Manager, Checkly ‍ Modern applications are complex. Microservices, third-party dependencies, and continuous deployments all contribute to a flood of telemetry data—logs, metrics, traces—flying in from every direction.

At Mezmo, we’ve always believed that observability should empower innovation, not hold it back with complexity and unpredictable costs. However, as organizations scale and data volumes continue to explode, the old ways of managing telemetry data aren’t sustainable.

Everyone in your organization needs logs to perform the critical functions of their job. Developers need them to debug their applications, security engineers need them to respond to incidents, and support engineers need them to help customers troubleshoot issues. These various use cases create general requirements for enriched log data, often including accessing insights from outside typical retention windows.

When things break, logs are often the first place you turn to figure out what's going on, which is why Datadog makes it easy to find them. The ability to pivot between traces, metrics, and logs in one place speeds up investigations and helps teams move faster during incidents. That level of correlation is a big reason so many teams rely on Datadog. ‍

We’re thrilled to share that Mezmo has been recognized by G2 with 25 badges across four key categories: Enterprise Monitoring, Log Monitoring, Log Analysis, and Cloud Infrastructure Monitoring. These awards are more than just a celebration of our platform—they’re a reflection of you, our customers. Your feedback, support, and insights push us to build better solutions and deliver the highest standards of performance and service.

Intellyx BrainBlog by Jason English for Mezmo ‍ “Bubble bubble, toil and trouble” describes the mysterious process of mixing together log data and metrics from multiple sources as they enter an observability data pipeline. ‍ Customers demand high performance, functionality-rich digital experiences with near-instantaneous response times.

You've deployed a new feature into production. You've done your unit testing, fixed lots of bugs, your code is awesome. Now it's time for hundreds/thousands/millions of users to break...err...use your feature. You're diligent about tracking usage in real-time, and getting customer feedback when something goes wrong. You track the performance and response time impacts on the server. All is good...except...that feature isn't quite working for a specific group of users. Now what?