Choosing the right monitoring tools: Guide for Tech Teams

89% of IT leaders say making systems easier to watch is a top goal for 2025. But picking the right tool from hundreds of options for your tech setup can be tough.

Teams often use scattered tools that don't show a complete, up-to-date picture of their microservices. This causes outages and problems when rolling out new versions.

What poor monitoring costs?

• 70% of engineering teams have downtimes because they don't watch their systems well enough.

  
  

• $300K per hour is what big companies lose on average when their systems go down.

  
  

• 58% of teams say their current monitoring stack doesn't give them real-time visibility into dependencies.

  
  

The best monitoring tools help you find problems before they affect users, make your system run better, and ensure smooth rollouts. This guide will show you the top tools you can use today.

In this guide👇

1. What makes a good monitoring tool?

2. 10 Best Monitoring Tools for tech teams

3. How HyperTest improves distributed tracing?

4. Picking the right tool for your team

What makes a Good Monitoring Tool?

A strong monitoring tool should provide:

1. Complete Sight: A robust monitoring tool must offer visibility into every aspect of the system—applications, infrastructure (network, servers, databases), and services (APIs, microservices).

• Example: If an e-commerce app experiences slowdowns, monitoring should help pinpoint whether it's due to a database bottleneck, an overloaded backend service, or a failure in a third-party API.

2. Rapid Notifications: The tool should send alerts before users start to notice issues, allowing teams to address problems proactively.

• Example: If a payment gateway API begins to respond slowly, the system should alert the team before customers experience transaction failures.

3. Workflow Understanding: It should map and track dependencies between microservices, enabling teams to see how failures affect various parts of the system.

• Example: If a user authentication service goes down, the system should indicate which services (like checkout or user dashboards) are impacted.

4. Intuitive Issue Detection: The tool should efficiently identify problems without necessitating extensive manual investigation.

• Example: Rather than simply displaying high CPU usage, a smart monitoring tool would link it to a specific failing API request or a sudden surge in traffic.

5. Adaptive Compatibility: It should function seamlessly across various environments—on-premises, cloud, or hybrid setups.

• Example: If a company shifts part of its workload to AWS while retaining some services on private servers, the monitoring tool should still deliver a cohesive view of both.

10 Top Monitoring Tools for Tech Teams

➡️ Datadog

This tool watches everything from top to bottom. It combines logs, measurements, and traces.

• Best for: Cloud-native settings and teams that need a single monitoring tool.

• Why it stands out: Anomaly detection driven by AI, dashboards that update in real time, and monitoring for security.

• Key Features:

  • Monitoring of infrastructure and applications.

  • Alerts you can customize, and insights based on AI.

  • Integration with AWS GCP, and Azure for cloud-native systems.

➡️ HyperTest

A tool to trace distribution and test APIs designed for microservices.

• Best for: Making sure upstream services stay stable during deployments.

• Why it stands out: It lets developers know when they're pushing a PR that might break upstream services, both direct and indirect.

• Key Features:

  • Tracks APIs across microservices.

  • Gives a clear view of distributed systems leaving no blind spots.

  • Stops broken deployments by spotting possible failures.

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➡️ Prometheus + Grafana

Open-source tool to monitor and visualize made for Kubernetes and microservices.

• Best for: Teams that run applications in containers.

• Why it stands out: You can customize it a lot and it has advanced alerting based on queries.

• Key Features:

  • Database that stores time-series data to collect and query metrics.

• Grafana integration to visualize details.

  • Scales and remains lightweight to suit modern DevOps workflows.

➡️ New Relic

An APM and infrastructure monitoring tool that provides deep insights into applications.

• Best for: Debugging and troubleshooting in real time.

• Why it stands out: It uses AI to detect anomalies and trace distribution.

• Key Features:

  • Insights at code level to tune performance.

  • Visibility from end to end across applications and what they depend on.

  • Supports OpenTelemetry to monitor extensively.

➡️ Elastic Observability (ELK Stack)

A tool that brings together logs, metrics, and performance tracking in one place.

• Best for: Groups wanting a solution they can host themselves and scale up.

• Why it catches your eye: It's open source, so you can tweak it to your heart's content.

• Key Features:

  • You can analyze and visualize logs in depth.

  • It spots unusual patterns using AI.

  • It works well with cloud-native setups.

➡️ Splunk

This is a top-notch tool for keeping an eye on things and analyzing security data for big companies.

• Works best for: Big organizations that need machine learning to watch over their systems.

• What makes it special: It gives real-time insights into how things are running, with deep analysis.

• Main features:

  • It uses AI to predict and watch for issues.

  • You can make your own dashboards to see what's happening right now.

  • It works well with many cloud services and tools that developers use.

➡️ Jaeger

This is a free tool that tracks how information moves in systems with lots of small, connected parts.

• Works best for: Finding out why things are slow or not working in systems with many parts.

• What makes it special: It works well with OpenTelemetry right out of the box.

• Main features:

  • It can see how different services depend on each other.

• In-depth root cause analysis.

  • Visual display of request flows.

• Excels at tracing microservices and finding latency issues.

• Why it's unique: Built-in support for OpenTelemetry.

➡️ AppDynamics

Software for tracking application performance and business data.

• Ideal for: Big enterprise applications.

• What makes it special: AI-driven monitoring of business effects.

• Main Features:

  • Detailed look at code-level performance.

  • Tracking of end-user experience.

  • Works with business intelligence tools.

➡️ Sentry

Tool for tracking errors and monitoring performance in real time.

• Perfect for: Developers who need to see everything from start to finish.

• Why it shines: spots code-level problems.

• Main Features:

  • Instant bug alerts with in-depth stack traces.

  • Speed checks with latency breakdowns.

  • Works with major dev workflows.

➡️ Zabbix

Free tool to keep an eye on infrastructure and networks.

• Ideal for: IT and DevOps groups watching over big setups.

• What makes it special: Lets you monitor with or without agents.

• Main Features:

  • Custom dashboard to see live stats.

  • Watches crucial systems to ensure they're always up.

  • Can grow to fit big company needs.

How does HyperTest make distributed tracing better?

Traditional monitoring tools track logs and metrics but do not provide real-time visibility into service dependencies. HyperTest goes beyond plain tracing, providing developers immediate, actionable insight into microservices interactions—before issues hit production.

✅ Proactive Failure Detection: Identifies direct and indirect service failures prior to deployment, preventing cascading failures.

✅ End-to-End Microservices Visibility: Traces API calls between services, dbs., 3rd-party APIs etc. revealing hidden dependencies in real-time.

✅ Zero Manual Setup: Auto-traces services without additional configurations or code changes.

✅ Seamless Kubernetes & Cloud-Native Support: Ensures high availability across dynamic, container-based environments.

HyperTest is not just a tracing solution—it's your microservices stability safety net.

Picking the right tool for your team

📌 Questions to Consider:

• Do you need to monitor in real-time or look at past data?

• How important is it to trace requests across your microservices?

• Does your team want to spot potential failures before they happen?

• Will the tool work well with the tech you already use?

For large-scale microservices teams, a tool like HyperTest can add to existing observability platforms helping debug faster, deploy safer, and keep services running.

Monitoring is now essential—it forms the core of system reliability. Whether you pick Datadog Prometheus, or HyperTest putting money into the right tool will:

✅ Stop outages before they affect users.

✅ Cut down debug time from hours to minutes.

✅ Boost developer output with instant insights.