Datadog

Datadog is our first point of access for developers to review logs and monitoring of key services and architecture. Primarily, we were drowning in trying to find useful logs in AWS Cloudwatch and Datadog's log discovery capabilities are far and away better. We have setup up several key alarms for bad log patterns but have yet to find full utility in monitoring other metrics - largely because we do not have a core platform development team.

• Highly improved distributed compute debugging
• Still waiting for Bits AI access - negative experience on new product rollouts
• Spreadsheets have made reporting to external teams significantly easier

ClickHouse, Grafana and Sentry

AWS CloudTrail, Amazon CloudWatch, Google Workspace

We use Datadog for monitoring and observability across our org. It gives us visibility into what's really happening when something goes wrong via tracing, and its monitors have alerted us to issues countless times before a customer could complain. We monitor performance via metrics and can dig into specific calls using traces and flame graphs to see where bottlenecks are.

• By using monitors for new errors, we've reduced the errors to near zero for a growing consumer-facing application. A meaningful percentage of visits were previously ending in failure, but we've been able to work through the errors with the increased visibility
• Custom metrics have allowed us to provide excellent customer service to our business customers by analyzing usage patterns over time and helping them use our application more effectively. They really appreciate when we proactively reach out to them about ways to improve their success rate

In our environment, Datadog is a core part of our observability stack for application performance and infrastructure. We are using RUM for monitoring customer performance in web applications and identifying issues. For API, we have an APM monitor to check latency, track all endpoints and status codes, and the success rate. We have comprehensive AWS infrastructure monitoring, including EC2 instances, RDS MySQL Clusters, NLB/ALB, VPC flows, and Lambda functions. Additionally, APM for Java Microservices includes tracing, JVM Heap, GC, and thread pools. We are also utilizing it for log analysis and on-call services, including custom logs for settlement jobs, credit applications, normalization for fast searching, resolving deadlocks, and handling 5xx bursts. We are also using WAF for application endpoint security.

• Datadog has had a very positive ROI for us because it direclty reduced downtime, improved customer experience, and helped our team to delete the issues early, and operate more efficienlty.
• Engineers can quickly correlate logs, metrics, and traces in one places instead of spending hours seraching across servers.
• infrastructure side, visibility into CPU, memory, and RDS usage helped us right-size several EC2 instances, resulting in ~10–15% cloud cost savings.

Grafana, Prometheus, Amazon CloudWatch and Dynatrace

Grafana Loki, Dynatrace, Prometheus

We use Datadog to monitor our application using monitors and alerts, create dashboards to report system performance, and general tracking of logs and traces to make debugging easier. I've also used Datadog workflows to create automated workflows for creating Jira tickets, triaging bugs, and even creating automated bug fixes by pushing errors through to AI coding tools, which can diagnose issues and suggest solutions.

• Enables faster response to critical issues using monitors and alerts
• Enhances understanding of system performance, making it easier to track progress towards performance improvements

<div>We use Datadog as the main Observability and Appication Monitoring Tool in our organization.</div><div>It excel at our :</div><div>1 Incident Detection and Response -Wr are able to have real time monitoring of apps and service health ,with alerting configured via integrations like PagerDuty.This help us to detect Incidents quickly ,reduces downtime and ensures we meet our SLA/SLO targets.</div><div>2.End -to- End Observability - we are able to trace requests,analyse logs and monitor metrics to spot issues particulary in our Envoy mesh architecture and coroutine-driven workloads.</div>

• Helps us to to identify and resolve our systems and applications bugs quickly and effectively.
• The highly customizable dashboards allow us to tailor the analytics to fit our exact needs that leads to better decision-making
• The ability to customize which metrics and logs we ingest helps us to manage o control the cost effectively.

New Relic

IBM Cloudability, Slack, Jira Service Management