Question: How can I reduce mean time to resolution (MTTR) in my complex and distributed tech estate?

To lower mean time to resolution (MTTR) in your complex and distributed tech estate, ServiceNow Cloud Observability provides an AI-powered system to monitor and respond to changes in cloud-native and monolithic applications. It provides visibility into cloud-native applications, boosts productivity by providing dev teams with visibility into dependencies, and removes guesswork so you can lower MTTR. This package can be accelerated with ServiceNow Impact packages for faster value.

Another great option is Datadog, an all-in-one monitoring and security platform that provides real-time insights into performance, security, and user experience. It includes infrastructure monitoring, APM, synthetic monitoring, real user monitoring, security monitoring, and serverless monitoring, so you can quickly identify and optimize system performance. Datadog monitors a wide range of cloud providers and includes tools for digital experience optimization and software delivery.

Logz.io is another great option, combining open-source monitoring tools like OpenSearch, Prometheus, and OpenTelemetry to provide logs, metrics, and trace data for identifying and resolving production issues. With high-performance log analytics, centralized metrics monitoring, and AI-powered features like anomaly detection and alert recommendations, Logz.io can automate and accelerate work in lowering MTTR. It monitors over 300 cloud platforms and offers flexible pricing based on the amount of data ingested.

Last, Honeycomb is designed to help teams quickly identify the root cause of problems in distributed services. It offers distributed tracing, debuggable Service Level Objectives (SLOs), and smart data sampling, which together provide a powerful observability platform. Honeycomb's flexible pricing model, based on event volume, means it can accommodate teams of any size without sacrificing visibility or capabilities.