10 Best Infrastructure Monitoring Tools for 2026: A Complete Comparison

The infrastructure monitoring tool you pick today will shape your incident response, your observability budget, and your migration options for years to come. Architecture and pricing model deserve more weight than the feature list because they determine what your team can actually do with the tool once the contract is signed.

This guide covers five evaluation criteria, ten tools worth shortlisting in 2026, a side-by-side comparison table, and answers to the questions practitioners most often run into when picking a tool.

What Is Infrastructure Monitoring?

Infrastructure monitoring is the practice of collecting, analyzing, and alerting on telemetry data from your servers, containers, networks, databases, and cloud services. The data falls into three signal categories: metrics, logs, and traces. A good monitoring tool pulls these signals into one query layer so your team can catch degraded performance, diagnose failures, and track capacity trends before they turn into outages.

Infrastructure monitoring sits underneath application performance monitoring (APM) and log management, covering the compute, storage, network, and orchestration layers your application runs on. Most production teams now evaluate all three together rather than as separate purchases because the boundaries between these categories have blurred over the last few years. Modern setups also fold in distributed tracing, extended Berkeley Packet Filter (eBPF) collection, and security telemetry, which is what most vendors call full-stack observability.

How to Choose an Infrastructure Monitoring Tool

A solid evaluation looks past feature checklists and digs into how a tool charges, where data lives, and how well it supports the technology direction your organization has set. The five criteria below separate tools that fit your architecture and budget from tools that surface unexpected costs and make migrations harder once your environment changes:

• Data architecture and storage model: Index-first architectures write data to expensive storage before your team can query it, which shows up as a retention wall the first time an on-call engineer investigates an incident older than the hot-tier window. The durable fix is an in-stream or archive-first model that analyzes data while it’s still in flight and routes low-priority logs to cheaper tiers without giving up alerting.
• OpenTelemetry support and open standards: Most engineering teams now invest in OpenTelemetry across at least one signal type, which makes proprietary agents a slow-burn migration liability. The cost surfaces on day one of a vendor switch, when every service with a proprietary agent has to be reinstrumented before the new backend can ingest a single telemetry packet.
• Pricing transparency and total cost of ownership: Per-host pricing fits poorly in dynamic environments where container counts fluctuate hourly under Kubernetes autoscaling, and per-GB models drift past forecast the first time a traffic spike hits. The durable fix is a pricing model that decouples cost from infrastructure scale and lets leadership forecast next quarter without guessing.
• AI and automation capabilities: Many vendors ship AI-assisted investigation, but most teams can’t put it into practice because the underlying retention window is too short to give the model a meaningful baseline. The capabilities worth weighing are plain-language querying, reproducible diagnoses, and a storage model that actually holds enough history to train on.
• Kubernetes and cloud-native readiness: 82 percent of container users now run Kubernetes in production, which makes pod-level metrics, Horizontal Pod Autoscaler (HPA) monitoring, and graphics processing unit (GPU) telemetry non-negotiable. Cardinality explosions under autoscaling surface any tool that treats Kubernetes coverage as a generic dashboard bolted onto a host-based product.

Working through these areas early saves your team from surprises around retention, query performance, and pricing behavior 12 months into a contract.

10 Infrastructure Monitoring Tools to Evaluate in 2026

The profiles below cover ten infrastructure monitoring tools that show up most often in 2026 evaluations. The list spans cloud-native platforms, enterprise SIEM and APM suites, open-source backends, and developer-focused tracing tools.

Tool	Pricing Model	OTel Support	Kubernetes Readiness	AI/AIOps	Data Ownership
Coralogix	Logs $0.42/GB, traces $0.16/GB, metrics $0.05/GB ingested, no per-host, per-user, per-query, or per-feature fees	OTel-native across logs, metrics, traces, and security	Purpose-built Kubernetes and Serverless dashboards, eBPF DaemonSet, Fleet Management via OpAMP, PromQL-compatible	Olly, Coralogix’s autonomous observability agent, with Git cross-reference	Customer-owned S3 or GCS in open Parquet, no reindexing to query
Datadog	From $15/host/month, plus per GB and per event	Accepted, Datadog Agent primary	DaemonSet, autodiscovery, included dashboards	Watchdog anomaly detection	Vendor-owned, proprietary format
Dynatrace	From $29/host/month under Dynatrace Platform Subscription, plus per GiB and per pod	Complementary, OneAgent primary	OneAgent DaemonSet, per-pod fees	Davis AI root cause analysis	Vendor-owned, Grail lakehouse
Splunk	Quoted (Workload, Ingest, Entity, or Activity-based)	Native, Splunk OTel Collector distro	Generic dashboards	ITSI ML-driven correlation	Vendor SaaS or self-managed, S3 archive needs reindex
New Relic	Free tier (100 GB/month and free Basic users); paid Full Platform users on Standard, Pro, and Enterprise plans, plus per-GB ingestion above the free allowance	Native, first-class path	Pixie eBPF, included integrations	New Relic AI assistant, Proactive AIOps	Vendor-owned database, archiving enterprise-only
Elastic	Compute-capacity-based with named tiers (Standard, Gold, Platinum, Enterprise) on Elastic Cloud Hosted; consumption-based on Elastic Cloud Serverless	Accepted, Elastic Agent and Beats	Elastic Cloud on Kubernetes operator, prebuilt Kubernetes dashboards via integrations	Limited ML, search-focused	Managed (Elastic Cloud Hosted or Serverless) or self-hosted; searchable snapshots on S3 restricted to enterprise tier
Grafana Cloud	From $0.50/GB logs and $6.50 per 1,000 active series of metrics, plus per-user fees on enterprise	Native, Grafana Alloy collector	Grafana dashboards	Adaptive Metrics, limited root cause analysis	Vendor SaaS, separate Loki, Mimir, and Tempo backends
Sumo Logic	Quoted, credit-based Flex Licensing on ingestion and analytics scans	Accepted	Helm-based, included dashboards	LogReduce fuzzy-logic clustering	Vendor-owned, S3 archive needs re-ingestion
Honeycomb	From $130/month for the Pro tier (1.5B events/month), plus $0.10/GB telemetry pipeline	Native, OTel-first	Generic K8s coverage	BubbleUp	Vendor-owned, no remote archiving
Prometheus	Free (Apache 2.0)	Experimental OTLP receiver	Native service discovery, requires sharding past a single node	None built-in	Self-managed local time series database

1. Coralogix

Coralogix processes telemetry in-stream through its Streama architecture and writes it to your own cloud object storage in open Parquet format, which puts it in a different category from vendors that index first and hold the data in proprietary storage. The architecture lines up against each of the five evaluation criteria above: Streama plus customer-owned Amazon Simple Storage Service (S3) or Google Cloud Storage (GCS) covers the storage model, full OpenTelemetry ingestion covers open standards, ingestion-based pricing with TCO Optimizer tiering covers total cost, Olly covers AI and automation, and purpose-built Kubernetes dashboards with an extended Berkeley Packet Filter (eBPF) DaemonSet cover cloud-native readiness.

Olly, Coralogix’s autonomous observability agent, sits on top of that data layer and investigates incidents across every telemetry type using plain-language queries, cross-referencing Git to surface the affected service, blast radius, and the exact line of code to fix.

Key features:

• Streama in-stream architecture: Coralogix analyzes data while it’s still in flight, then writes it to the customer’s own Amazon S3, Google Cloud Storage, or other object store in open Parquet format, which decouples retention cost from indexing cost and keeps historical data queryable without rehydration.
• Ingestion-based pricing with TCO Optimizer: Logs at $0.42 per GB, traces at $0.16 per GB, and metrics at $0.05 per GB ingested, with no per-host, per-user, per-query, or per-feature charges, plus a TCO Optimizer that routes data into the Frequent Search, Monitoring, Compliance, and Blocked pipelines based on policies you define for each data stream (DPXL filters across application, subsystem, and severity). Logs routed to the Compliance pipeline drop as low as $0.17 per GB, and customers report 40 to 70 percent cost reductions through that tiering.
• 100 percent OpenTelemetry ingestion: OTel-native across logs, metrics, traces, and security telemetry, with Fleet Management handling OpenTelemetry collector configuration through the Open Agent Management Protocol (OpAMP), plus Prometheus-compatible metrics and Prometheus Query Language (PromQL) support.
• Purpose-built Kubernetes and Serverless dashboards: Dedicated Kubernetes and Serverless views rather than generic dashboards, with an eBPF DaemonSet for zero application instrumentation and GPU telemetry for artificial intelligence (AI) workloads.
• Olly, the autonomous observability agent: Plain-language querying and automated root cause analysis across every telemetry type, with Git cross-reference that surfaces the affected code path and the exact line of code to fix, alongside an AI Center that monitors large language model (LLM) interactions through evaluators and guardrails.

Pros:

• Archived data stays queryable directly in the Coralogix interface in seconds, without rehydration fees or re-ingestion steps, because the data never leaves the customer’s Parquet archive
• Cost stays flat as infrastructure scales, with no per-host, per-user, per-query, or per-feature line items that compound under autoscaling
• 24/7 support with a 17-second median response time, included on every plan regardless of spend, staffed by tier 3 engineers rather than frontline script readers
• Olly cross-references telemetry with Git to pinpoint root cause, blast radius, and the exact line of code causing an incident, which compresses investigations that previously took two to four hours into minutes

Cons:

• Takes a brief ramp-up for teams used to index-first query models
• Less brand recognition than long-tenured vendors in some procurement pipelines

Best for: Cloud-native engineering teams that want predictable observability spend, full data ownership in open Parquet on their own cloud, and AI-assisted incident investigation across logs, metrics, traces, and security in one product.

2. Datadog

Datadog is a broad-coverage SaaS built around per-host pricing, with modules billed separately. Logs, metrics, traces, APM, real user monitoring, and security all ship as separate modules under one interface.

Key features:

• Per-host infrastructure monitoring at $15 to $23 per host per month, with separate billing per module
• Logs, traces, APM, real user monitoring, and security as separate line items
• Kubernetes coverage through DaemonSet-based collection, autodiscovery, and out-of-the-box dashboards
• Watchdog handles automatic anomaly detection across logs, metrics, and traces
• OpenTelemetry data accepted, with the proprietary Datadog Agent and the Datadog distribution of OTel Collector (DDOT) as the recommended collection paths

Pros:

• One of the broadest integration libraries in the category, with hundreds of supported sources
• Mature dashboards, alerts, and workflow integrations from over a decade in the market
• APM and synthetic monitoring sit alongside infrastructure metrics in one product

Cons:

• Each module bills separately, so total spend gets harder to forecast as the environment grows
• Data lives in proprietary format, which makes provider switches a migration project rather than a configuration change
• Flex Logs bills compute separately from Flex storage, so historical search workloads add a second line item on top of the existing per-host and per-GB charges

Best for: Established teams that want broad integration coverage and have the budget headroom to absorb separately billed module growth.

3. Dynatrace

Dynatrace is an application performance monitoring (APM)-led platform that pairs deep auto-instrumentation through OneAgent with consumption-based pricing across multiple line items. OneAgent installs once per host and handles auto-discovery, topology mapping, and code-level visibility across supported runtimes.

Key features:

• Dynatrace Platform Subscription lists $29 per host per month for Infrastructure Monitoring and $58 per host per month for Full-Stack Monitoring, with separate fees for log ingestion, queries, and Kubernetes pod monitoring at $1.40 per pod
• OneAgent provides auto-instrumentation and topology discovery across the stack
• Davis AI runs root cause analysis and topology-aware anomaly detection
• Grail data lakehouse handles log analytics across large data volumes
• OpenTelemetry accepted as a complementary path alongside OneAgent

Pros:

• APM and end-user experience monitoring sit at the core of the product
• Automated root cause findings map to the live service topology, which shortens investigation for known failure modes
• OneAgent auto-instrumentation reduces manual instrumentation work for supported runtimes

Cons:

• Per-pod Kubernetes pricing scales poorly as microservices counts grow, since every additional pod adds a line item to the monthly bill
• Log management is a less mature capability than the APM core
• Top-down operational model fits operations teams better than developer-led DevOps workflows
• OneAgent is a proprietary agent, which creates migration work when teams later standardize on OpenTelemetry

Best for: Enterprise operations teams with APM and end-user experience as primary use cases, where deep auto-instrumentation matters more than developer-centric workflows.

4. Splunk

Splunk is an enterprise platform anchored on log and security analytics, with several pricing models customers can pick from per deployment. The observability side lives in Splunk Observability Cloud while security lives in Splunk Enterprise Security, and the product has a long history in security operations centers and regulated industries.

Key features:

• Workload, Ingest, Entity, and Activity-Based pricing options for different workload patterns
• Splunk Observability Cloud built on OpenTelemetry, with a Splunk-distributed OTel Collector
• IT Service Intelligence (ITSI) layers ML-driven event correlation across the data set
• Built-in security information and event management (SIEM), with security orchestration, automation, and response (SOAR) available in the Premier Edition of Enterprise Security
• Splunk Cloud SaaS or self-managed Splunk Enterprise deployment options

Pros:

• Enterprise SIEM and Federal Risk and Authorization Management Program (FedRAMP) authorization cover regulated and federal deals
• Splunk Processing Language (SPL) is well-established with long-tenured Splunk operators
• Multiple pricing models accommodate varied workload patterns

Cons:

• Operational complexity demands in-house expertise
• Multiple pricing models can make total cost hard to project
• Splunk requires users to reindex their data into high performance storage before it can be analyzed

Best for: Large enterprises with dedicated security operations, regulatory compliance requirements, and existing Splunk expertise in-house.

5. New Relic

New Relic is an APM-rooted platform that combines per-user and per-GB pricing with first-class OpenTelemetry support. Logs, metrics, traces, browser monitoring, and infrastructure all sit under one user-based licensing model rather than per-host fees.

Key features:

• Per-GB ingestion beyond a 100 GB monthly allowance on the free tier, with user seats split across Full Platform, Core, and Basic tiers so write-heavy operators (Full Platform) carry a different cost profile than read-only stakeholders (Basic, free)
• OpenTelemetry treated as a first-class ingestion path alongside the New Relic agents
• Pixie eBPF integration for Kubernetes observability, with included cluster, node, and pod dashboards
• New Relic AI assistant for natural-language queries, with Proactive AIOps for anomaly detection and incident correlation
• Free basic users and a monthly ingestion allowance included on the free tier

Pros:

• Free tier covers 100 GB per month with free basic users included
• APM visualizations and out-of-the-box workflow integrations available across the platform
• No per-host charges, which keeps large infrastructure footprints predictable

Cons:

• The Full Platform user tier (the only seat type with full read-write access to most features) carries a meaningful per-seat cost, which compounds on larger engineering teams where the assumption is everyone needs full access
• Streaming export of data to Amazon S3 requires Data Plus, available only on Pro and Enterprise editions, which limits cost optimization on long-retention use cases
• Log functionality is less developed than dedicated log platforms, and data has to be indexed to be accessible, which raises cost implications on high-volume logs

Best for: APM-centric teams that want strong OpenTelemetry support and a pricing model that does not scale with host count.

6. Elastic

Elastic Cloud is a search-rooted system built on the open-source Elasticsearch, Kibana, and Beats stack. Teams pick between Elastic Cloud Hosted (managed deployment with named tiers), Elastic Cloud Serverless (consumption-based, fully managed), or self-hosting the same components on their own infrastructure.

Key features:

• Compute-capacity-based pricing with named tiers (Standard, Gold, Platinum, Enterprise) on Elastic Cloud Hosted, plus a separate consumption-based Elastic Cloud Serverless option that scales billing with usage rather than provisioned capacity
• OpenTelemetry data accepted alongside Elastic’s own Elastic Agent and Beats shippers
• Elastic Cloud on Kubernetes (ECK) operator for running Elasticsearch on Kubernetes
• Machine learning capabilities for anomaly detection and log clustering, gated to Platinum tier and above
• Security information and event management (SIEM) tooling layered on the search foundation

Pros:

• Open-source community around Elasticsearch, Kibana, and Beats
• Search engine foundation with flexible querying and customization
• Self-hosting option for teams that want full data control

Cons:

• Elastic Cloud Hosted ties pricing to provisioned compute capacity rather than ingestion, so right-sizing a cluster across spiky workloads becomes its own ongoing tuning exercise
• As of June 2026, Elastic ships prebuilt Kubernetes dashboards through its integrations, though the anomaly detection and machine learning layers that add root-cause context are gated to the Platinum tier and above
• Data has to be indexed before it’s queryable, so cost climbs steeply on high-volume logs that index-first storage forces into the hot tier
• Searchable snapshots for Amazon S3 archiving are restricted to enterprise tiers

Best for: Engineering teams with in-house search and Elasticsearch expertise that want flexibility and the option to self-host rather than a fully managed observability product.

7. Grafana Cloud

Grafana Cloud is a stack of separate open-source backends behind one visualization layer, with usage-based pricing across multiple dimensions. The platform pairs the widely used Grafana visualization tool with Loki for logs, Mimir for metrics, and Tempo for traces.

Key features:

• Logs at $0.50 per gigabyte ingested, metrics at $6.50 per 1,000 active series, and per-user fees layered on top of ingestion on the enterprise tier
• Built on Loki for logs, Mimir for metrics, and Tempo for traces
• Grafana Alloy collector for OpenTelemetry data ingestion
• Adaptive Metrics for cost reduction on high-cardinality time series
• Kubernetes dashboards and visualization through the Grafana layer

Pros:

• Widely used dashboarding and visualization layer in the observability market
• Open-source roots and broad ecosystem familiarity for site reliability engineering (SRE) teams
• Adaptive Metrics reduces cardinality-driven cost on time series

Cons:

• Three separate backends (Loki, Mimir, Tempo) create context switching and integration overhead for teams running all three signal types
• Loki was not designed for high-cardinality labels, and Grafana’s own documentation notes that too many label-value combinations build a large index and small chunks that reduce query performance. Loki indexes label metadata rather than the full log line, so query flexibility on unindexed fields is narrower than an index-everything system
• Enterprise plans layer per-user fees on top of ingestion, which pushes total cost higher as the engineering organization scales

Best for: SRE and platform teams already invested in Grafana dashboards that want a managed Loki, Mimir, and Tempo stack rather than self-hosting.

8. Sumo Logic

Sumo Logic is a full-stack platform priced through Flex Licensing, a credit-based model that meters ingestion and analytics scans rather than per-host counts. Logs, metrics, traces, infrastructure monitoring, and security operations all sit under one SaaS interface.

Key features:

• Flex Licensing charges credits for ingestion and analytics scans, with metrics billed on a separate data-point-per-minute (DPM) tier
• OpenTelemetry data accepted alongside Sumo Logic’s own collectors
• Helm-based Kubernetes collection with included dashboards and alerts
• LogReduce uses fuzzy-logic clustering to group similar log lines
• Built-in Cloud SIEM and Cloud SOAR, without Cloud Security Posture Management (CSPM)

Pros:

• FedRAMP authorization covers federal deals
• Built-in SIEM and SOAR tooling reduces the need for a separate security purchase
• Kubernetes monitoring works right after Helm install with no custom configuration

Cons:

• Flex Licensing meters analytics scans on top of ingestion, so investigation-heavy workloads burn credits faster than the ingestion line on the contract suggests
• Pricing transparency requires a sales conversation rather than a public price list
• Archived data on Amazon S3 requires re-ingestion on demand before queries can run against it, which adds a rehydration step to routine historical investigation
• Does not offer visibility into the security posture of cloud infrastructure (no CSPM), which means a separate tool is needed for misconfiguration and compliance checks

Best for: Federal buyers and mid-market teams that want bundled SIEM and SOAR tooling alongside observability under one contract, and can cover CSPM separately.

9. Honeycomb

Honeycomb is a per-event tracing and debugging tool built around OpenTelemetry, with recent expansion into infrastructure metrics. The focus is distributed tracing, span-level analysis, and BubbleUp outlier detection for engineering teams investigating live system behavior.

Key features:

• $130 per month for the Pro tier, which includes up to 1.5 billion events per month with unlimited seats and unlimited querying
• Separate telemetry pipeline charge of $0.10 per gigabyte for data collection
• OpenTelemetry treated as the primary instrumentation path, with no proprietary agent required
• BubbleUp for outlier and contributing-factor analysis on traces
• Honeycomb Metrics reached general availability in early 2026, adding infrastructure and host metrics support via OpenTelemetry

Pros:

• Purpose-built for distributed tracing and debugging workflows
• No proprietary software development kit (SDK) required, since standard OpenTelemetry libraries work out of the box
• Unlimited seats and querying included on paid tiers

Cons:

• No log management product and no remote archiving, which pushes log storage and long-term retention into a second tool
• No SIEM or Cloud Security Posture Management (CSPM) tooling included
• Metrics support is recent, and the product remains focused on tracing first, which limits full-stack infrastructure coverage relative to dedicated infrastructure platforms

Best for: Developer-led teams focused on distributed tracing and debugging where full-stack infrastructure monitoring, logs, and security live in another tool.

10. Prometheus

Prometheus is the open-source pull-based metrics system that powers most cloud-native metrics pipelines and underpins many commercial backends. The project is governed by the Cloud Native Computing Foundation (CNCF) and pairs with exporters, Alertmanager, and a visualization layer like Grafana for production deployments.

Key features:

• Free and open-source under Apache 2.0 as a CNCF graduated project, currently at version 3.11
• Pull-based scraping with native Kubernetes service discovery for pods, services, and nodes
• PromQL for querying time series
• Experimental OpenTelemetry Protocol (OTLP) ingestion, originally added in Prometheus 3.0 and still flagged as experimental in current releases
• Broad exporter library covering hosts, databases, message queues, and cloud services

Pros:

• No licensing cost beyond the infrastructure your team runs
• Cloud-native fit, with broad adoption as the metrics layer for Kubernetes deployments
• Community and knowledge base around PromQL and operator patterns

Cons:

• Metrics only, with no logs, traces, or AIOps in the box
• Local storage is not clustered or replicated, and defaults to 15 days of retention
• Federation or remote write needed for long-term storage and horizontal scale

Best for: Engineering teams with the operational capacity to run their own metrics infrastructure, often paired with a managed backend for long-term storage and cross-signal correlation.

Matching Your Monitoring Stack to Your Infrastructure

The right tool depends on your architecture, pricing model, and Kubernetes maturity, and a proof of concept against real ingestion volume and query patterns will surface issues a spec sheet misses. Against the five criteria in this guide, Coralogix lines up directly:

• Data architecture and storage model: Streama analyzes telemetry in-stream before storage, then writes it to your own cloud object storage in open Parquet format, so retention is decoupled from indexing cost and your team keeps full ownership of the data.
• OpenTelemetry support and open standards: Coralogix is OpenTelemetry-native across logs, metrics, traces, and security signals, with Fleet Management handling collector configuration through OpAMP so you are not locked into a proprietary agent.
• Pricing transparency and total cost of ownership: Ingestion-based pricing at $0.42 per GB for logs, $0.16 for traces, and $0.05 for metrics, with no per-host, per-user, per-query, or per-feature charges, paired with the TCO Optimizer that routes data into pipelines based on policies you define for each data stream, where customers report 40 to 70 percent savings.
• AI and automation capabilities: Olly, Coralogix’s autonomous observability agent, runs plain-language investigations and root cause analysis against full historical data, which is viable because that data sits in low-cost object storage with unlimited retention rather than an indexed vendor tier.
• Kubernetes and cloud-native readiness: Purpose-built Kubernetes dashboards, an eBPF DaemonSet for zero-instrumentation coverage, Prometheus-compatible metrics with PromQL, and GPU telemetry for AI workloads at the pod and node layer.

Of the ten tools above, Coralogix is the only one that pairs in-stream processing with customer-owned, indexless storage in open Parquet format, which is why its retention is unbounded at object-storage prices and its query speed doesn’t depend on how much you’ve indexed. If you want to see how query speed holds when archived logs sit in your own bucket instead of a vendor index, start a free 14-day Coralogix trial and run a Remote Query against your own Parquet archive.

Frequently Asked Questions About Infrastructure Monitoring Tools

What is the difference between infrastructure monitoring and application monitoring?

Infrastructure monitoring covers the layers underneath your code, while APM covers request behavior inside it. In containerized environments, the two overlap at the pod level, which is why most teams evaluate them together.

Can open-source infrastructure monitoring tools replace commercial platforms?

Open-source tools like Prometheus and Grafana cover metrics collection, storage, and visualization for production workloads, but your team owns the sharding, high availability, and scaling work that a managed service handles for you. Open source fits teams with the engineering capacity to operate it, and lands less comfortably when staffing or uptime requirements are tight.

How does infrastructure monitoring work in multi-cloud environments?

Multi-cloud monitoring usually means a single observability backend that ingests telemetry from Amazon Web Services (AWS), Google Cloud Platform (GCP), and Azure through cloud-native exporters and OpenTelemetry collectors. Tools with customer-owned storage in object stores like S3, including Coralogix, keep data in the cloud where it was generated, which reduces cross-cloud transfer work for teams running multi-region workloads.

What metrics should infrastructure monitoring tools track?

The baseline set covers central processing unit (CPU) utilization, memory usage, disk input and output, network throughput, and error rates at the host and container layer, plus pod restart counts, node conditions, and resource requests versus limits at the Kubernetes layer. AI workloads add tokens per second, time to first token, GPU utilization, and inference queue depth, since the same incident often touches all three layers at once.

How long should infrastructure monitoring data be retained?

Operational alerting needs only a few weeks of recent data, but historical baselining for AI investigation, capacity planning, and compliance often requires months or years. Platforms that store data in customer-owned object storage, like Coralogix, decouple retention cost from indexing cost, which lets your team keep historical data without paying premium rates to query it.