Effective monitoring of deployed apps and services is crucial for DevOps teams. Unless systems are observable, you can’t identify the causes of errors and performance problems.
Setting up comprehensive monitoring can be daunting, but it gets easier with the right tools. In this article, we’ll share over 20 of the best options for instrumenting your apps, observing your infrastructure, and analyzing collected data.
The DevOps monitoring tools on this list have been selected because they’re popular ecosystem choices that can be integrated with each other and have good community support. However, remember that this is just a small slice of the broader DevOps monitoring landscape.
DevOps monitoring is the process of collecting data from your infrastructure and applications. A robust monitoring strategy provides actionable real-time data that allows you to understand how your DevOps process is performing.
A successful DevOps monitoring implementation should answer specific questions about your operations, such as the average response time, rate of failure, and why a slowdown occurred on a particular day. You can use this data to inform future improvements to workflows and systems, and then assess whether changes are producing their intended effects.
The three main monitoring strands are:
- Metrics — Metrics are numerical values such as CPU usage, latency, and error rates; when tracked over time, they reveal changes in a system’s performance.
- Logs — Logs provide a chronological description of system activity, such as incoming requests and error messages. Apps engineered to write detailed log files are easier to observe.
- Traces — Traces augment logs by capturing the full sequence of events preceding a particular point. They let you see the code paths a particular transaction took, providing vital context to inform root cause analysis.
Capturing and utilizing data spanning all three areas requires dedicated tools designed to accommodate the large number of data points you’ll accumulate. Continuous monitoring tools in DevOps also need to support efficient patterns for data querying and analysis, including integration with other systems that enable broader trends to be identified.
Types of monitoring in DevOps
Here are the primary types of monitoring in DevOps:
- Infrastructure monitoring – Focuses on the health and performance of servers, networks, databases, and other infrastructure components
- Application performance monitoring (APM) – Monitors the performance and behavior of applications to ensure they are running smoothly
- Log monitoring – Analyzes logs generated by applications, servers, and network devices to identify issues and trends
- Network monitoring – Monitors data traffic across networks to detect bottlenecks, uptime issues, and overall network health, ensuring smooth data flow and connectivity
- Security monitoring – Focuses on detecting security threats and vulnerabilities within the infrastructure and applications
- User Experience monitoring – Simulates user interactions with the application to monitor performance from the end-user perspective
- End-to-end monitoring – Provides a holistic view of the application’s performance, user experience, and infrastructure health, allowing teams to detect subtle issues that may arise at any stage of the process
- Container and orchestration monitoring – Focuses on monitoring containerized environments and orchestration platforms like Kubernetes.
- Cost monitoring – Focuses on tracking resource usage and associated costs, allowing teams to forecast expenses and optimize resource allocation effectively
- Database monitoring – Ensures databases are functioning optimally and efficiently
Let’s dive into our tools round-up. The top DevOps monitoring tools include:
- Spacelift
- Prometheus
- Grafana
- Elasticsearch
- Logstash
- Kibana
- InfluxDB
- New Relic
- Kubecost
- Splunk
- Sensu
- Datadog
- Pagerduty
- Dynatrace
- Sysdig
- Zabbix
- Collectd
- Perses
- Netdata
- Sentry
- SolarWinds
- Nagios
- AppDynamics
The tools span all major monitoring themes and are not listed in order of preference. As there are plenty of other great tools available, this is a guide to what’s available, not a head-to-head comparison.
Spacelift is not exactly a DevOps monitoring tool, but it allows you to connect to and orchestrate all of your infrastructure tooling, including infrastructure as code, version control systems, observability tools, control and governance solutions, and cloud providers.
Spacelift enables powerful CI/CD workflows for OpenTofu, Terraform, Pulumi, Kubernetes, and more. It also supports observability integrations with Prometheus and Datadog, letting you monitor the activity in your Spacelift stacks precisely.
Key features
- Multi-IaC workflow
- Stack dependencies: You can create dependencies between stacks and pass outputs from one to another to build an environment promotion pipeline more easily.
- Unlimited policies and integrations: Spacelift allows you to implement any type of guardrails and integrate with any tool you want. You can control how many approvals you need for a run, which resources can be created, which parameters those resources can have, what happens when a pull request is open, and where to send your notifications data.
- High flexibility: You can customize what happens before and after runner phases, bring your own image, and even modify the default workflow commands.
- Self-service infrastructure via Blueprints: You can define infrastructure templates that are easily deployed. These templates can have policies/integrations/contexts/drift detection embedded inside them for reliable deployment.
- Drift detection & remediation: Ensure the reliability of your infrastructure by detecting and remediating drift.
Pro: Seamlessly integrates with popular tools
Con: Can have a steep learning curve for new users
Website: https://spacelift.io
Prometheus is a time series database that’s specifically designed as a metrics monitoring solution. You can use it to store metrics values collected from your infrastructure and apps, then query them using a powerful expressive language.
Prometheus has become a key component of the observability ecosystem. It integrates well with many other tools, apps, and platforms and offers official instrumentation support for ten different programming languages. An alerting system is also included to ensure you’re informed when metrics change.
Key features
- Time-series database for metrics
- Powerful query language (PromQL)
- Built-in alerting
Pro: Highly scalable and open-source
Con: Can be complex to manage at scale without additional tools
Website: https://prometheus.io
See example: Prometheus Monitoring for Kubernetes Cluster
Grafana is an observability solution focused on creating visual dashboards that display metrics from your data sources. Grafana supports a wide array of connectors to link your metrics and logs, but it’s most commonly used alongside Prometheus.
Dashboards are accessed via a web app. They can include charts, graphs, and other customizable panels that display the results of querying your data sources. Grafana can also produce PDF reports and governance insights that are ideal for periodically informing stakeholders of changes to KPIs.
Key features
- Visualizes metrics from various sources
- Customizable dashboards
- Extensive plugin ecosystem
Pro: Versatile and integrates with numerous data sources
Con: May require additional configuration for advanced use cases
Website: https://grafana.com
Elasticsearch is a search engine and query API that’s optimized for deep analysis of textual data. In the context of observability, it’s most commonly used to index logs and traces. Elasticsearch is fast, scalable, and capable of ingesting large amounts of data in real time, allowing you to efficiently query your logs and identify meaningful content.
Key features
- Distributed and scalable search capabilities that can operate across a cluster of servers
- Full-text search support for multiple languages
- Real-time data analysis
Pro: Highly scalable and fast search capabilities
Con: Resource-intensive, especially with large datasets
Website: https://www.elastic.co/elasticsearch
Logstash is part of the Elastic Stack and is used in conjunction with Elasticsearch. Whereas Elasticsearch indexes data and makes it searchable, Logstash implements a processing pipeline that ingests, transforms, and filters data before it’s sent to its final storage location.
As the name implies, Logstash is commonly used for logs and traces. It can extract key details such as severity, timestamp, and IP address from incoming messages, making logs more useful and accessible. The output data can then be saved in an Elasticsearch cluster, ready for long-term retention.
Key features
- Data processing pipeline for logging
- Supports various input and output plugins
- Real-time data transformation
Pro: Flexible data ingestion from multiple sources
Con: High memory usage under heavy loads
Website: https://www.elastic.co/logstash
Kibana is the analytics visualization solution within the Elastic Stack. Similarly to Grafana, it focuses on enabling the creation of detailed visual dashboards that reveal the meaning within your data. Although it’s commonly used in conjunction with Elasticsearch and Logstash, Kibana can be connected to any data source to aggregate insights from across your entire application inventory.
Key features
- Data visualization tool for Elasticsearch
- Interactive dashboards and reports
- Real-time search and filtering
Pro: Powerful visualization for Elasticsearch data
Con: Does not natively support direct integration with other databases or data sources outside Elasticsearch
Website: https://www.elastic.co/kibana
InfluxDB is a time series database that emphasizes event-logging. It’s designed to capture records of real-time events with high storage efficiency, fast writes, and low latency querying. These qualities make it particularly well-suited to monitoring edge devices, such as IoT workloads that generate large event volumes.
InfluxDB also supports SQL queries, potentially making it more approachable for database developers who don’t want to learn a new language.
Key features
- Time-series database for high-write loads
- SQL-like query language (InfluxQL)
- Built-in support for downsampling and data retention
Pro: Optimized for time-series data with high performance
Con: Can become costly with large-scale data storage
Website: https://www.influxdata.com
New Relic is a DevOps monitoring tool that provides a comprehensive suite of observability solutions designed to fulfill all your monitoring requirements. Its platform incorporates metrics, logs, and trace analysis alongside error tracking, performance profiling, and automated anomaly detection, allowing you to monitor your entire stack in one place. New Relic is a commercial service where you pay for what you use.
Key features
- Application performance monitoring (APM)
- Distributed tracing
- Incident alerting
Pro: Comprehensive monitoring across multiple environments.
Con: Pricing can be high for extensive usage.
Website: https://newrelic.com
It’s important to monitor cloud costs to detect waste and identify savings opportunities. Kubecost and the open-source OpenCost platform it’s built upon provide automated cost monitoring for Kubernetes clusters, letting you track the spending associated with their resources. The tool includes alerts, multicloud data aggregation, and automated recommendations on how to reduce costs by optimizing your infrastructure.
Key features
- Cost monitoring for Kubernetes
- Resource usage analysis
- Real-time cost allocation and alerts
Pro: Helps optimize Kubernetes resource spending
Con: Limited functionality outside Kubernetes environments
Website: https://www.kubecost.com
Learn more: What is Kubecost & How to Use It?
Splunk, owned by Cisco, is a DevOps monitoring platform that focuses on providing the data to enable resilient incident response. You can track metrics with real-time alerts, and then take action to resolve problems as they occur.
Splunk incorporates AI-powered tools capable of immediately spotting anomalies and security vulnerabilities, providing more support to DevOps teams by exposing the broader context surrounding problems.
Key features
- Log management and analysis
- Real-time event monitoring
- Search Processing Language (SPL) and visualization of machine data
Pro: Powerful for large-scale log analysis
Con: High cost, especially for large data volumes
Website: https://www.splunk.com
Sensu is an “observability pipeline” that aims to deliver robust monitoring via an as-code strategy. It consolidates your other observability tools and augments them with service-based auto-discoverable agents that can be deployed to any endpoint. Sensu is also self-healing, supports custom integrations, and works with a wide selection of alerting systems and incident management platforms.
Key features
- Monitoring and observability pipeline
- Scalable event processing
- Extensive plugin support
Pro: Flexible and scalable with a strong community
Con: Can be complex to configure for large environments
Website: https://sensu.io
Datadog is a complete DevOps observability solution that supports infrastructure, application metrics, security analysis, and log auditing. A commercial SaaS solution, it emphasizes real-time monitoring and the ability to create customized dashboards that clearly show critical values.
Datadog also includes integrated container and serverless monitoring capabilities, making it a compelling option for teams building cloud-native systems. The platform is supported by a comprehensive API, a catalog of third-party integrations, and IDE plugins that give developers vital performance data as they work.
Key features
- Cloud monitoring and security
- Real-time dashboards and alerts
- Integrated APM and log management
Pro: Unified monitoring solution for cloud-native environments
Con: Pricing can escalate with increased usage and features
Website: https://www.datadoghq.com
See example: How to manage Datadog with Terraform
PagerDuty is an operations management platform that focuses on incident response. It provides contextually relevant information about incidents in real time, helping IT teams build resolutions quickly. It’s most commonly used by operations teams managing production applications where downtime is critical.
PagerDuty lets you observe unplanned events with a high degree of automation. The platform can notify on-call team members, update a public status page, and utilize AI to highlight the most meaningful events and associated actions. In addition to an open API, it includes over 700 native integrations with other services, including other monitoring tools.
Key features
- Incident response and on-call management.
- Real-time alerting and escalation
- Integration with multiple monitoring tools
Pro: Reliable for managing critical incidents
Con: Can be costly for smaller teams
Website: https://www.pagerduty.com
Dynatrace is a cloud observability and security platform that relies heavily on AI to provide precise answers about the state of your systems.
In addition to features designed to offer end-to-end visibility of apps and infrastructure, Dynatrace also supports business leaders by providing detailed analytics and user experience session profiling. The platform can deeply integrate with your other cloud ecosystem components, via a process automation system that lets you automate key workflows.
Key features
- AI-driven application performance monitoring
- Full-stack observability
- Automatic discovery and dependency mapping
Pro: Highly automated with AI insights
Con: Complex to configure for non-standard environments
Website: https://www.dynatrace.com
Sysdig is oriented around security. It’s a cloud-native app protection platform (CNAPP) that delivers real-time visibility into threat activity in your cloud environments. Sysdig supports security and operations teams in detecting vulnerabilities, narrowing down the risk, and applying effective mitigations in response. This includes a detailed analysis of attack pathways and suspicious events with correlation across your cloud inventory.
Key features
- Container and cloud-native security
- Performance monitoring and troubleshooting
- Real-time threat detection
Pro: Strong focus on Kubernetes security
Con: Limited support for non-containerized environments
Website: https://sysdig.com
Zabbix is positioned as an all-in-one open-source DevOps monitoring tool that provides “single pane of glass” visibility for your entire stack. This extends from infrastructure components such as cloud resources right through to the operation of your APIs, web services, and IoT devices.
The suite offers high availability, strong scalability, and pre-built integrations with popular alerting, ticketing, and incident response solutions.
Key features
- Open-source network monitoring
- Customizable alerting
- Scalable to large environments
Pro: Free and highly configurable.
Con: The user interface can feel outdated and less intuitive
Website: https://sysdig.com
Collectd is a small daemon that collects performance metrics data from your systems and running apps. It’s lightweight and simple to configure but uses a powerful modular architecture that permits robust extensibility. Once metrics have been collected, they can either be stored on the system or made available over the network, ready for other platforms to consume.
Collectd is a good option for teams that plan to develop their own observability tooling and don’t want to deploy heavier agents to their endpoints.
Key features
- System and application performance metrics collection.
- Extensible with plugins.
- Supports a wide variety of output formats.
Pro: Lightweight and efficient
Con: Requires manual configuration for complex setups
Website: https://www.collectd.org
Perses is a young project being developed as part of the CoreDash community — an effort to standardize how observability dashboards and other visualizations are defined. The Perses workflow revolves heavily around GitOps and declarative as-code configuration, with dashboards primarily created using either Go or the CUE templating language.
Although it’s still maturing, Perses is usable today as a lightweight alternative to Grafana. It can surface data natively from Prometheus clusters and supports plugins that let you add support for other data sources.
The project might not be ready for prime time just yet, but it’s worth tracking if you’re fed up with having to recreate your dashboards each time you switch observability suite. If Perses achieves its aims, then its model could be the future standard in the visualization space.
Key features
- Open-source dashboarding tool
- Supports Prometheus and other time-series databases
- Focuses on scalability and ease of use
Pro: Scalable dashboarding for large datasets
Con: Limited to specific use cases, mainly time-series data
Website: https://perses.dev
Netdata is an open-source observability suite designed as an alternative to platforms including Datadog and Prometheus/Grafana. Supported by the CNCF, it offers hundreds of integrations with other monitoring platforms, cloud providers, container technologies, and popular applications.
Netdata also promises sub-second monitoring latency, low resource consumption, and high resolution. It’s a compelling option for engineering teams seeking an open all-in-one solution.
Key features
- Real-time performance monitoring
- Detailed visualization with minimal setup
- Distributed monitoring support
Pro: Highly detailed and real-time insights
Con: Can be overwhelming with too much data displayed
Website: https://www.netdata.cloud
Sentry is an error-tracking platform. It provides the error messages, stack traces, and surrounding context for problems happening in your apps in production. This allows you to efficiently respond to errors using relevant data, without having to wait for reports to come in from users.
Sentry has client libraries for all major programming languages, enabling straightforward integration with your apps. It also supports performance profiling, letting you investigate why operations are running slow. It integrates directly with code platforms such as GitHub and GitLab to map issues back to their source, making it an ideal tool for developers.
Key features
- Error tracking and monitoring
- Real-time crash reporting
- User context for error events
Pro: Excellent for tracking and resolving application errors
Con: Limited to error monitoring, not full-stack observability
Website: https://sentry.io
SolarWinds is a stalwart in the observability space. It provides a full-stack monitoring platform that’s most commonly used in large enterprises needing visibility of multiple endpoints, including cloud and on-premises environments.
SolarWinds also includes performance analysis capabilities for databases, networks, and applications, facilitating detailed investigations into user experience problems.
Key features
- Network and infrastructure monitoring.
- Automated performance management.
- Scalability for large environments.
Pro: Comprehensive monitoring suite with a wide range of tools
Con: High cost and complexity in large deployments
Website: https://www.solarwinds.com
Nagios is a widely used open-source monitoring tool. Its suite of projects includes enterprise server and network monitoring, log aggregation, and centralized visibility functions. Nagios has risen to prominence as one of the leading open observability options, as well as for its ease of configuration and library of over 4,000 community plugins.
Key features
- Server and network monitoring.
- Alerting and incident management.
- Extensible with plugins.
Pro: Highly customizable and open-source
Con: Configuration can be cumbersome and time-consuming
Website: https://www.nagios.org
Cisco’s AppDynamics is an integrated suite of observability tools designed to span the full IT stack. It includes capabilities for monitoring apps, infrastructure, networks, and security issues, with automatic correlation back to events observed by users and business leaders. This makes it an ideal option for enterprise teams requiring robust analytical capabilities that span their entire service inventory.
Key features
- End-to-end application performance monitoring.
- Real-time business transaction insights.
- AI-powered analytics.
Pro: Strong focus on business impact analysis
Con: Expensive and complex to deploy fully
Website: https://www.appdynamics.com
We’ve introduced some of the top tools in the DevOps continuous monitoring arena. Hopefully, you’ve found an observability solution that meets all your requirements.
However, this might not necessarily be a single DevOps monitoring tool because so many on this list work best when used together. Whether you need Prometheus and Grafana for app metrics instrumentation or the ELK stack for log indexing, it’s likely that combining multiple options will give you the most success.
These monitoring tools for DevOps are a great way to learn what’s happening in your apps and infrastructure, but you still need a platform like Spacelift to manage your deployments. Create a free account today or book a demo with one of our engineers.
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