Top 15 infrastructure monitoring tools in 2026: a performance and cost-based comparison

TL;DR #

• The Problem: Exploding telemetry data from cloud-native apps makes infrastructure monitoring a massive data challenge. Teams face either unsustainable costs from vendors like Datadog or the high operational burden of scaling open-source tools like Prometheus.
• The real Criteria: Choosing a tool isn't about UI features; it's an architectural decision. The key factors are the underlying data platform's ability to handle high-cardinality data, deliver sub-second query performance, and offer a low total cost of ownership (TCO).
• The Solution: Modern monitoring tools built on high-performance analytical databases provide the best path forward. They offer superior data compression (reducing storage costs) and fast SQL-based queries, enabling deep, ad-hoc analysis without performance penalties or vendor lock-in.
• ClickStack's Edge: As a solution built on ClickHouse, ClickStack is purpose-built for this challenge, delivering unmatched performance and cost-efficiency for teams operating at petabyte scale.

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In today's cloud-native landscape, infrastructure monitoring is a data problem. As telemetry volumes explode, engineering teams are caught between Datadog's unpredictable bills and the operational burden of scaling open-source tools like Prometheus. The result is the same: spiraling costs and slow queries during a critical outage. Most tool comparisons miss this fundamental point, focusing on feature checklists instead of the underlying architecture. This guide cuts through the noise to provide a clear, performance-based analysis, evaluating the top 15 tools on what truly matters: the ability of their data platform to deliver speed, scale, and cost-efficiency.

What really matters in modern infrastructure monitoring? #

Fast aggregations over high-cardinality data #

High-cardinality data is the reality of cloud-native systems. This includes metrics tagged with unique container IDs, user IDs, or request traces. While essential for pinpointing issues, it brings traditional monitoring tools to their knees, causing slow dashboards and sluggish queries. A modern solution must perform fast aggregations across millions of unique time series without performance penalties.

Cost-efficiency at scale: the architectural advantage #

As infrastructure scales, pricing models that penalize data volume can become unsustainable, forcing teams to choose between budget and visibility. A cost-effective solution must provide predictable costs tied to the resources you actually use. The key to achieving this is an architecture that separates storage from compute and uses object storage. This design is a strategic advantage, enabling:

• Massive Cost Reduction: Superior data compression combined with low-cost object storage dramatically reduces TCO.
• Infinite Data Retention: It makes long-term or even infinite data retention economically viable, removing the anxiety of restrictive data retention policies.
• Architectural Flexibility: It allows for the independent scaling of read (query) and write (ingest) workloads, ensuring queries remain fast even under heavy ingestion load.

Query performance and analytical depth: beyond dashboards #

Dashboards track known issues. Real observability is about investigating "unknown unknowns" during an incident. This requires the power to ask ad-hoc questions and get answers in seconds. While proprietary languages have their place, the analytical depth of SQL is unmatched, allowing teams to perform complex joins and aggregations on raw, unsampled data. The best tools are built on real-time analytical databases that prioritize this capability, offering both simple, search-based querying for common workflows and the full analytical power of SQL for deep, ad-hoc investigations.

A comparison of the top 15 infrastructure monitoring tools #

1. ClickStack (ClickHouse Observability) #

• What's good: ClickStack’s primary advantage is its architecture, which is purpose-built for the scale and complexity of modern telemetry data. It uses ClickHouse, a columnar database for real-time analytics, to solve the core challenges of modern observability:
  • Unmatched Performance at Scale: Delivers sub-second query performance on petabytes of high-cardinality data, as demonstrated by customers like OpenAI and Anthropic. The columnar architecture excels at large-scale aggregations needed for trend analysis over wide time periods, while features like inverted indices and bloom filters enable fast, discovery-based text searches.
  • Radical Cost-Efficiency via Object Storage: The architecture is built on the strategic principle of separating storage from compute and using object storage. This, combined with superior data compression (>15x), can reduce storage costs by over 90%, making long-term retention a reality. Teams can finally "send everything" without worrying about the bill.
  • Unified Data & Deep Analytics: Unlike fragmented solutions, it provides a unified data store where logs, metrics, and traces are correlated efficiently at the database level, not the application level. All data is accessible with the power of standard SQL, enabling 'observability science.' This is the ability to join telemetry data with business data (e.g., user activity, revenue) for powerful, cross-domain insights that are impossible to get in siloed platforms.
  • Open and Flexible: It is OpenTelemetry-native, preventing vendor lock-in, but flexible enough to integrate with existing agents like Fluentd or Vector.
• The catch: ClickStack is not a turnkey, consumption-based SaaS like Datadog. It requires engineering effort to configure and manage data ingestion pipelines. While powerful, the user experience is purpose-built for engineering workflows, prioritizing deep analytical capabilities through both full SQL and natural language querying, while continuously evolving its user experience to include more guided workflows and dashboards.
• The verdict: The definitive choice for mature, cost-conscious teams operating at a scale where performance bottlenecks and cost overruns are primary concerns. It is the perfect migration path for users feeling the pain of high data costs from vendors like Elastic or Splunk, or for those who have hit the architectural limits of complex, self-hosted Prometheus and Thanos/Mimir setups.

2. Datadog #

• What's good: Datadog is a market-leading, all-in-one observability platform that offers a massive feature set covering infrastructure monitoring, APM, log management, and more. Its key strength lies in its ease of use and rapid time-to-value, enabled by over 700 pre-built integrations that allow teams to light up monitoring for their entire stack with minimal configuration. The user interface is highly polished and considered one of the best in the industry, which, combined with strong incident response and collaboration features, makes it an attractive tool for teams who want a turnkey solution that just works.
• The catch: This convenience comes at a significant cost. Datadog is notoriously expensive, with a complex and opaque pricing model that includes a bewildering array of SKUs for hosts, ingested data, custom metrics, and indexed logs. This makes cost forecasting nearly impossible and often leads to vendor lock-in due to the proprietary agent and data ecosystem. For teams operating at scale, the trade-offs become more apparent: Architecturally, its performance degrades on high-cardinality or long-term data queries, and retaining high-fidelity data for more than a few weeks becomes prohibitively expensive.
• The verdict: Datadog is the go-to choice for enterprises that prioritize a single, comprehensive suite and are not sensitive to the high costs. However, for cost-conscious teams managing data at scale, the model often proves to be unsustainable.

3. New Relic #

• What's good: New Relic provides a compelling entry point into observability with one of the most generous free tiers available, including 100GB of monthly data ingest and one full-platform user at no cost. This strategy allows smaller teams to adopt a powerful, all-in-one platform without an initial investment. The company has simplified its pricing to a usage-based model focused on data volume and users, which is a welcome change from older, more complex host-based pricing. Its core strength remains its robust Application Performance Monitoring (APM) capabilities, which are complemented by first-class support for OpenTelemetry, ensuring alignment with modern, open standards for instrumentation.
• The catch: The platform's primary drawback is its user-based pricing model for teams that need to scale. "Full Platform" user seats are expensive, with Pro plans costing around $349 per user. This creates a significant cost barrier to providing broad access to engineering teams, often turning into a prohibitive expense that discourages collaborative, team-wide observability.
• The verdict: New Relic is a solid, feature-rich platform ideal for teams that can comfortably operate within the generous free tier or for organizations with a small number of core users who will have full access. However, enterprises planning for wider team access must carefully model the total cost of ownership, as the per-user fees can become a significant financial trap at scale.

4. Dynatrace #

• What's good: Dynatrace is a highly automated platform excelling in root cause analysis through its powerful causal AI engine, "Davis". Its "OneAgent" technology offers zero-touch, automated instrumentation, enabling rapid, full-stack data collection from hosts, virtual machines, containers, and cloud services. This allows for the automatic creation of a complete dependency map, called "Smartscape," which visualizes the entire application topology. The platform's AI-driven approach provides automated anomaly detection and prioritizes alerts, reducing manual effort and alert fatigue. Davis AI helps identify the precise root causes of issues, moving beyond simple correlation to provide actionable answers.
• The catch: This is a premium-grade, expensive solution with a complex, usage-based pricing model that can be difficult to predict. The high degree of automation can create a "black box" experience, which may frustrate hands-on engineering teams who prefer direct, query-driven investigation and control over their data. Furthermore, the platform has a steep learning curve, and its user interface can be confusing for new users.
• The verdict: Dynatrace is best suited for large enterprises that are willing to pay a premium for a highly automated, AI-driven platform that delivers "answers, not data." It is ideal for organizations seeking to reduce the operational burden on their teams and wanting a solution that automatically identifies and explains performance problems without requiring deep manual analysis.

5. Splunk Observability Cloud #

• What's good: Splunk’s primary strength lies in its powerful log search, powered by the proprietary Search Processing Language (SPL), which allows for deep analysis of log data. The platform is battle-tested for petabyte-scale workloads and is backed by robust security and compliance features, making it a staple in large, security-conscious enterprises.
• The catch: This power, however, comes at a significant cost. Splunk is notoriously expensive, with pricing models that make costs difficult to control and predict as data volumes expand. The proprietary nature of SPL introduces a steep learning curve and creates vendor lock-in. Architecturally, its biggest weakness is the siloed backends for logs, metrics, and traces. This separation prevents a truly unified query experience and often leads to poor query performance at scale. This architecture, combined with its pricing, makes long-term data retention prohibitively expensive, complicating the process of correlating signals and undermining the core promise of observability.
• The verdict: For large organizations already heavily committed to the Splunk platform, the Observability Cloud is a logical, but extremely expensive, choice.

6. Grafana Stack (Loki, Mimir, Tempo) #

• What's good: The Grafana Stack provides a powerful, open-source, and composable observability solution, pairing the best-in-class, highly customizable dashboards of Grafana with dedicated backends for logs (Loki), metrics (Mimir/Prometheus), and traces (Tempo). This composable, API-driven approach avoids vendor lock-in and is backed by a massive and active open-source community.
• The catch: This flexibility comes at a significant cost. The self-hosted version carries an immense operational burden, requiring expertise to deploy, scale, and maintain three separate, stateful systems. The siloed backends create the architecture's fatal flaw: cross-signal correlation happens at the application level, not the database level. While Grafana mitigates the need to manually copy-paste IDs, it forces users into a rigid, opinionated "metrics to traces to logs" workflow. This completely prevents the kind of exploratory analysis that is possible with a unified database, where an engineer might start an investigation from a log pattern and join it across all signals. This limitation is architectural:
  • Loki Prevents Exploration: By design, Loki only indexes metadata labels, not full log content. This makes it cost-effective for targeted lookups (e.g., "find logs for this trace ID") but prevents the discovery-based, "Google-like" search workflows that engineers rely on for root cause analysis of unknown issues.
  • Prometheus Loses Fidelity: Prometheus encourages the use of pre-aggregation via recording rules to manage performance. This destroys the raw data fidelity required for deep, exploratory root cause analysis. Furthermore, its data model struggles fundamentally with high-cardinality data, leading to a massive memory footprint and slow queries as the number of unique time series explodes.
• The verdict: The Grafana stack is ideal for teams that prioritize best-in-class visualization and dashboarding and possess the deep engineering expertise required to manage a complex, multi-faceted, self-hosted stack, or for those whose scale and budget can accommodate the high potential costs of Grafana Cloud's usage-based pricing.

7. Prometheus #

• What's good: As the second project to graduate from the Cloud Native Computing Foundation (CNCF) after Kubernetes, Prometheus is the de-facto open-source standard for cloud-native metrics monitoring. It is 100% free, preventing vendor lock-in and is trusted by countless organizations. Its power lies in a flexible, multi-dimensional data model where metrics are identified by a name and key-value pairs, combined with the expressive PromQL query language that allows for powerful slicing and dicing of time-series data. Its pull-based model and native service discovery mechanisms make it a perfect fit for the dynamic and ephemeral nature of Kubernetes environments, allowing it to automatically find and monitor new services as they appear.
• The catch: Prometheus is a powerful component, but it is not a complete observability solution. It handles metrics only, forcing teams to integrate separate tools for logging and tracing. Its single-server architecture struggles at scale with long-term storage and high-cardinality data. Architecturally, its biggest limitation is that it encourages pre-aggregation via recording rules to manage performance. This forces teams to anticipate their problems upfront and destroys the raw data fidelity required for deep, exploratory root cause analysis when unexpected issues arise. Scaling Prometheus beyond a single node requires adopting entirely separate, complex distributed implementations like Thanos or Mimir to achieve high availability and long-term storage, dramatically increasing operational complexity and TCO.
• The verdict: For teams operating at a smaller scale, Prometheus is an excellent and often default choice for metrics. For those building a custom, best-of-breed metrics stack at scale, it remains a foundational technology. It offers immense power and control for those willing to accept the significant and ongoing operational investment required to run it reliably at scale. For teams without the dedicated resources to manage its complexity, it can become a source of operational toil rather than a solution.

8. SigNoz #

• What's good: SigNoz stands out as an open-source, all-in-one observability platform, serving as a direct alternative to costly solutions like Datadog. Its greatest strength is its native integration with OpenTelemetry, an open-source standard that prevents vendor lock-in. It provides a unified experience for metrics, traces, and logs in a single application, which simplifies troubleshooting. Built on the high-performance ClickHouse database, SigNoz excels at ingesting and aggregating data quickly, making it a powerful tool for monitoring cloud-native environments, including microservices and Kubernetes. Its transparent pricing and self-hosting option appeal to privacy-conscious companies and those looking to control their data.
• The catch: As a newer entrant, SigNoz lacks the polished UI and extensive feature set (e.g., mature RUM, synthetics) of its proprietary competitors. The self-hosted version, while offering control, also carries a significant operational burden, requiring engineering resources to deploy, manage, and scale the infrastructure. It's important to note that while SigNoz is built on ClickHouse, its self-hosted nature means it lacks the key architectural advantages of a managed platform like ClickHouse Cloud, such as the separation of storage and compute, which is critical for cost-efficient, long-term data retention at scale.
• The verdict: SigNoz is an excellent choice for startups and cost-conscious engineering teams who are committed to open-source and OpenTelemetry standards. It offers a robust, unified monitoring experience for those who prioritize data ownership and are willing to trade some of the bells and whistles of market leaders for a more affordable and extensible solution. It is particularly well-suited for teams with the technical expertise to manage their own infrastructure.

9. Open Observe #

• What's good: OpenObserve is an open-source, all-in-one observability platform engineered for massive cost reduction and high performance. Written in Rust, it provides a unified solution for logs, metrics, and traces, often positioned as a more efficient alternative to Elasticsearch. Its primary architectural advantage comes from using Parquet files on object storage, which, combined with advanced compression, can lead to dramatic (up to 140x) reductions in storage costs. The platform's stateless node architecture simplifies scaling, and its DataFusion query engine allows for high-performance SQL queries directly on Parquet files. It embraces open standards, offering full compatibility with OpenTelemetry and existing tools like Prometheus and Fluentd.
• The catch: OpenObserve's primary downside is its relative immaturity. As a newer project, it lacks the decade-long, battle-tested history of its competitors. This results in a much smaller community and ecosystem compared to established players, making it harder to find tutorials, third-party integrations, and experienced engineers. Its dashboarding and alerting capabilities, while functional, are still evolving and may not be as feature-rich as those of market leaders. For large enterprises, its track record at extreme, petabyte-scale is not as publicly proven as solutions like ClickHouse, which can make it a higher-risk choice for mission-critical deployments. Architecturally, its reliance on Parquet files presents a critical trade-off. While the format provides excellent compression and vendor neutrality, it is poorly suited for the fast point-read queries essential for observability (e.g., fetching a specific trace by ID). It lacks the sophisticated skipping indexes of a purpose-built database like ClickHouse, which can lead to slower performance for these common, needle-in-a-haystack query patterns.
• The verdict: OpenObserve is a compelling choice for teams whose primary goal is to store observability data in an open, vendor-neutral format like Parquet. It is best suited for organizations prioritizing long-term data ownership in a lakehouse format and who are comfortable with the trade-offs of a rapidly developing, less mature tool.

10. Better Stack #

• What's good: Better Stack excels at unifying the core components of incident management into a single, cohesive platform. It integrates log management, uptime monitoring, well-designed status pages, and on-call scheduling, making it a cohesive solution for the entire incident lifecycle. Users frequently praise its clean, intuitive user interface and simple setup, which provides a fast time-to-value, especially for smaller teams. Its SQL-based log querying is powerful, and its approach to uptime monitoring is robust, with checks as frequent as every 30 seconds and detailed error reporting. For startups and small to mid-sized businesses, Better Stack offers a generous free tier and transparent pricing, providing exceptional value by bundling services that often require multiple subscriptions.
• The catch: The platform's primary limitation is its lack of depth for complex, large-scale environments. It does not offer advanced observability features like Application Performance Monitoring (APM) or distributed tracing, which are critical for debugging microservices architectures. Furthermore, its Kubernetes monitoring capabilities are not as deep or feature-rich as those of specialized competitors, meaning engineering teams in sophisticated, cloud-native environments will likely outgrow the platform as their needs for deep-dive analysis and performance monitoring mature. Notably, while it is built on ClickHouse, it does not offer the architectural benefits of a managed solution like ClickHouse Cloud, such as the separation of storage and compute, which can limit its cost-efficiency and scalability for users with demanding, long-term retention needs.
• The verdict: Better Stack is the perfect solution for small to mid-sized teams, particularly startups, who need a simple, user-friendly, and cost-effective tool to manage the core incident lifecycle. It masterfully combines the essentials of logging, uptime, and on-call, but teams with complex infrastructures will eventually need to graduate to a more comprehensive observability platform.

11. Chronosphere #

• What's good: Chronosphere is a purpose-built solution for taming cloud-native complexity, specifically engineered to solve high-cardinality metrics problems at a massive scale. Its standout feature is the "Control Plane," which provides granular, real-time control over telemetry data before ingestion. This allows teams to analyze data usage, shape traffic, and apply rules to drop or aggregate low-value metrics, directly addressing the data growth and cost explosion common with containerized workloads. Born out of Uber's highly-scaled M3 monitoring system, the platform is architected for reliability and performance, giving DevOps teams the tools to manage observability spend as a deliberate, value-based decision rather than an uncontrollable expense.
• The catch: Chronosphere is a premium, enterprise-grade tool with non-public pricing, making it inaccessible and likely overkill for teams that do not have a massive, business-critical cardinality problem. While its metrics capabilities are best-in-class, its logging and tracing functionalities are considered less mature than those of other all-in-one platforms, potentially requiring supplementary tools.
• The verdict: This is the platform you graduate to when your self-hosted Prometheus, Mimir, or Thanos stack is buckling under the weight of its own data and operational cost. It is the definitive choice for large-scale enterprises where the primary pain point is the untenable cost and complexity of high-cardinality metrics.

12. Honeycomb #

• What's good: Honeycomb is purpose-built for developer-centric observability, excelling at debugging "unknown unknowns" through the exploration of high-cardinality trace data. Its standout feature, "BubbleUp," allows engineers to intuitively find patterns and correlations by simply selecting a group of unusual data points on a heatmap. The tool then automatically compares the characteristics of that selection against the baseline, quickly surfacing the dimensions that explain the anomalous behavior across billions of events. This significantly shortens the typical debugging loop. Honeycomb’s pricing model is developer-friendly, based on event volume rather than hosts or users, which encourages sending rich, high-cardinality data without fear of surprise bills.
• The catch: Adopting Honeycomb often requires a significant cultural and technical shift. Teams must move away from a traditional reliance on metrics and dashboards towards a mindset of event-based investigation and deep tracing. While powerful for debugging complex application logic in microservices environments, its capabilities for classic infrastructure health dashboards and unstructured log management are less mature than tools specifically designed for those tasks.
• The verdict: An excellent tool for developer-centric teams focused on observability-driven development and debugging complex production issues. It is ideal for organizations that have embraced a culture of proactive, deep-system analysis and want to empower engineers to quickly understand and resolve incidents by asking questions of their data, rather than relying on pre-configured dashboards.

13. VictoriaMetrics #

• What's good: VictoriaMetrics is a high-performance, resource-efficient time-series database engineered as a powerful, drop-in replacement for Prometheus long-term storage. Its key advantage lies in its operational simplicity. It deploys as a single binary, which is significantly easier to manage than the complex, multi-component architectures of alternatives like Thanos or Mimir. It is highly resource-efficient, consistently using less RAM and disk space due to better data compression. This translates to lower storage costs, even when using high-performance block storage. For querying, its MetricsQL is backward-compatible with PromQL but extends it with additional features for more powerful analytical capabilities.
• The catch: VictoriaMetrics is a specialized tool primarily focused on metrics. While it has recently introduced a logging solution (VictoriaLogs), it is still in its early stages and the platform does not offer the same unified experience for logs, metrics, and traces as all-in-one solutions. Teams will likely need to integrate other tools for a complete observability picture. As a self-hosted, DIY solution, it demands operational expertise to deploy, scale, and maintain. While it is simpler than its direct competitors, it still represents a significant management overhead compared to a fully managed SaaS platform.
• The verdict: VictoriaMetrics is a fantastic, high-performance alternative for teams hitting the performance and cost bottlenecks of a scaled Prometheus setup. It is the ideal choice for organizations that want to avoid the architectural complexity and operational burden of Thanos or Mimir while retaining control over their monitoring stack.

14. Elastic Observability #

• What's good: Elastic Observability's core strength is its foundation in Elasticsearch, providing powerful, search-driven log analytics. It excels at ingesting, indexing, and analyzing large volumes of unstructured and semi-structured log data with high speed, making it a go-to for deep forensic analysis. Its open and flexible nature, coupled with the powerful visualization capabilities of its unified Kibana interface, allows teams to build custom dashboards for comprehensive insights. For organizations already invested in the ELK stack for logging, adding APM and metrics can feel like a natural and cost-effective extension.
• The catch: The ELK stack’s architecture creates significant challenges for modern observability workloads:
  • Extremely High TCO: Whether self-hosted or on their cloud, the cost is a major pain point. The Lucene inverted index is notoriously inefficient and creates massive storage overhead. It’s common for the index to be multiple times the size of the original data. Combined with poor data compression (typically only 2x), this leads to budget-breaking infrastructure costs.
  • High Operational Complexity: Self-hosting requires a dedicated team of experts. The architecture is brittle. When a node fails, the major operational overhead of rebalancing data across the cluster becomes a significant, time-consuming pain point for SREs.
  • Fails on Analytical Queries: Architecturally, it's a text-search engine retrofitted for analytics. It performs poorly on the aggregation queries over wide time periods that are essential for trend analysis, causing high JVM memory pressure, slow queries, and even node crashes.
• The verdict: Elastic is an excellent choice for teams whose primary use case is powerful, search-driven log analytics and who are already familiar with the ELK stack. It is a formidable tool for log-centric troubleshooting, but teams seeking a more balanced, all-in-one observability platform with predictable costs and top-tier APM may find it less suitable.

15. Nagios & Zabbix #

• What's good: As veterans of IT monitoring, both Nagios and Zabbix are free, open-source, and battle-tested over decades. Their primary strength lies in an extensive ecosystem of plugins that allows them to monitor almost any traditional hardware imaginable, from servers and switches to printers. This makes them highly customizable for specific, predictable environments. They are renowned for their stability in these static contexts, providing reliable, check-based monitoring for legacy systems.
• The catch: Their age reveals itself in their architecture, which is poorly suited for modern, dynamic infrastructure. Their host-centric model, reliant on manual configuration via text files or cumbersome UI elements, cannot keep pace with ephemeral environments like Kubernetes where containers and nodes are constantly changing. Compared to modern platforms, their UIs feel dated, and features like auto-discovery and data visualization are either less sophisticated or require extra plugins to implement. This makes them an "architectural fossil" when measured against the needs of cloud-native observability.
• The verdict: Still a viable choice for monitoring static, on-premise data centers with predictable hardware. However, they are the wrong tool for any team running applications in the cloud or Kubernetes, as their foundational design philosophy predates the dynamic, scalable nature of these systems.

Infrastructure monitoring tools: at-a-glance comparison table #

This table summarizes our findings, focusing on the architectural and financial realities of monitoring at scale. We evaluate each tool on its ability to handle high-cardinality data, its core cost model, and its query language—the factors that determine TCO and your team's ability to resolve incidents.

Conclusion #

The central argument of this analysis is that infrastructure monitoring is now an architectural decision, dictated by the performance of the underlying data engine. As telemetry data grows in scale and cardinality, the ability to run fast, complex queries without incurring crippling costs becomes the only metric that matters. Platforms that fail on this front are not just expensive; they are an obstacle to resolving outages.

Solutions built on high-performance analytical databases like ClickHouse represent the necessary architectural shift. They address the core problems of scale, performance, and cost head-on, providing a foundation not just for monitoring, but for deep, unrestricted analysis.