Your app runs across three AWS regions, a managed Postgres instance, a Stripe integration, and an OpenAI dependency. Something slows down at 2 AM. Is it your containers? A regional network blip? A vendor degradation you have no control over? "Cloud monitoring" is supposed to answer that question, but the category is so broad that two tools both claiming the label can solve completely different problems.
Some tools ingest host metrics, traces, and logs from your infrastructure (CPU, memory, container restarts, slow queries). Others watch your services from the outside: endpoints, SSL, response bodies. They correlate failures with the third-party clouds you depend on. Most teams need both layers, and the mistake is buying one when you needed the other.
We compared nine tools across the dimensions that actually decide the purchase: infrastructure metrics, APM and tracing, log management, external endpoint checks, multi-cloud coverage, and the one that surprises finance teams: how the bill scales. This comparison maps each tool to the job it actually does, whether you're on a single provider or spanning AWS, Azure, and GCP. Every pricing model below was checked against official pages in July 2026.
TL;DR comparison
| Tool | What it monitors | Best For | Model | Free Tier | Entry Price | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Host/infra metrics, APM & traces, logs, synthetics, RUM, security | Full-stack observability across infra, APM, and logs | Usage + per-host | Limited | $15/host/mo | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| GCP metrics, uptime checks, logs, traces (GCP only) | GCP-native observability (Cloud Operations) | Pay-as-you-go | Generous allotment | Usage-based | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Infra metrics, APM & traces, logs, synthetics, RUM | All-in-one observability with data-based pricing | Data + per-user | 100 GB/mo, 1 user | $0.35/GB | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Metrics (Prometheus), logs (Loki), traces (Tempo), synthetics | Open-source stack (Prometheus, Loki, Tempo) managed | Usage-based | 10k series, 50 GB logs | Usage-based | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| monitors cloud-hosted applications from the outside and correlates their failures with the cloud services they depend on. It is not a CloudWatch replacement for host metrics. It is the black-box layer that answers "is my service actually working for users, and if not, is a vendor the reason?" Every monitor, including the free tier, supports HTTP checks with custom headers, request bodies, response body assertions, and SSL certificate monitoring. The differentiator for cloud teams is resource groups. When Stripe degrades, your checkout monitor, billing webhook monitor, and subscription monitor would normally all fire separate alerts. Group them by the shared dependency and you get one alert that says "checkout is degraded, and the common factor is Stripe." Not seven pages at 2 AM. This is the alert-correlation problem that pure infra-metrics tools leave to you. Because checks are defined through a CLI, Terraform provider, and SDKs (monitoring as code), your configuration lives in version control next to the infrastructure it watches: CODE Key strengths
Pricing
Cost traps
Limitations
Best for: Teams running apps in the cloud who want fast external checks, response validation, and vendor-dependency correlation at flat pricing, layered on top of (or instead of) heavyweight infra tools.
|
| Component | Price | Notes |
|---|---|---|
| Basic metrics | Free | Standard AWS service metrics |
| Custom / detailed metrics | ~$0.30/metric/mo | First 10k, then tiered down |
| Dashboards | $3/dashboard/mo | Beyond the free 3 |
| Logs ingestion | ~$0.50/GB | Plus storage and Insights query cost |
| Synthetics canaries | ~$0.0012/run | Per canary run |
Cost traps
- Custom metrics and high-resolution metrics add up quickly across many resources
- Logs cost is ingestion + storage + query (three separate meters)
- Cross-account and cross-region dashboards add operational and cost overhead
Limitations
- AWS-centric, poor fit as a multi-cloud pane of glass
- No APM in CloudWatch itself (that's AWS X-Ray, a separate service)
- Alerting UX and correlation are basic compared to dedicated platforms
Best for: AWS-only teams that want native, low-friction infrastructure metrics, logs, and alarms without adding a third-party vendor.
Google Cloud Monitoring
is Microsoft's native monitoring stack, combining platform metrics, Log Analytics (Kusto/KQL queries), and Application Insights for APM. For workloads on Azure VMs, AKS, App Service, and Functions, it collects telemetry natively and feeds a shared alerting and dashboarding layer.
Application Insights is the standout piece. It delivers real APM, distributed tracing, and availability tests for apps instrumented with its SDK. The cost center to watch is Log Analytics: nearly everything interesting flows through it, and it's billed per GB ingested, so verbose logging or broad diagnostic settings can drive the bill up faster than teams expect.
Key strengths
- Native metrics, logs, and APM (Application Insights) for Azure workloads
- Powerful KQL querying in Log Analytics
- Availability tests for external endpoint monitoring
- Deep integration with Azure RBAC, resource groups, and Bicep/ARM
- Terraform support for config-as-code
Pricing
| Component | Price | Notes |
|---|---|---|
| Platform metrics | Free | Standard Azure resource metrics |
| Log Analytics ingestion | ~$2.30/GB | The primary cost driver |
| Application Insights | Billed via Log Analytics | Ingestion-based |
| Alert rules | Per rule/mo | Metric and log alerts priced separately |
Cost traps
- Log Analytics ingestion is the silent budget-killer. Diagnostic settings can flood it
- Data retention beyond the included period is billed separately
- Alert rules and notifications carry their own per-rule charges
Limitations
- Azure-first, not a natural multi-cloud primary
- Log Analytics has a learning curve (KQL) for teams new to it
- Cost governance requires active management of ingestion settings
Best for: Azure-centric teams that want native infrastructure metrics, APM via Application Insights, and KQL-powered log analytics.
New Relic
targets the enterprise end of cloud monitoring with a heavy bet on automation and AI. Its OneAgent auto-discovers services and dependencies, and the Davis AI engine does automated root-cause analysis. Instead of a wall of correlated alerts, it aims to tell you the single underlying cause of an incident. For large, complex, multi-cloud estates, that automation is the core value proposition.
Pricing moved to a consumption model (Dynatrace Platform Subscription) metered in granular units for full-stack monitoring, log ingestion, and synthetics. It's genuinely capable and genuinely expensive. Dynatrace is rarely the choice for a small team, and there's no free tier (evaluation is a 15-day trial).
Key strengths
- OneAgent auto-instrumentation with minimal manual setup for discovery
- Davis AI for automated root-cause analysis and anomaly detection
- Full-stack: infra, APM, logs, synthetics, RUM across clouds
- Strong Kubernetes and large-scale enterprise support
- Config-as-code via Terraform and API
Pricing
| Component | Price | Notes |
|---|---|---|
| Full-stack monitoring | ~$0.08/host-hour (8 GB) | Consumption units (DPS) |
| Log management | ~$0.20/GB ingest + retention | Metered separately |
| Synthetic monitoring | Per-request units | Consumption-based |
| Free tier | None | 15-day trial only |
Cost traps
- Consumption units make total cost hard to estimate before you're running at scale
- Log ingestion and retention are separate meters on top of host monitoring
- Enterprise contracts and minimums put it out of reach for small teams
Limitations
- No free tier (trial only)
- Enterprise pricing and complexity are overkill for straightforward cloud monitoring
- Full value depends on adopting the OneAgent/Davis model end to end
Best for: Large enterprises with complex multi-cloud environments that want AI-driven root-cause analysis and can commit to consumption-based pricing.
Grafana Cloud
bundles uptime monitoring, incident management, on-call scheduling, log management, and status pages into one dashboard. For cloud monitoring, it sits closer to the external/uptime layer than to deep infra telemetry. It watches your cloud-hosted endpoints, collects logs, and gives you on-call and a status page without stitching together separate products.
The consideration is per-seat pricing. Every person who manages monitors or responds to incidents is a "responder" seat, so cost scales with team size rather than infrastructure size. Solo developers and small teams get a lot from the all-in-one bundle; larger on-call rotations should total the seat cost before committing.
Key strengths
- All-in-one: uptime + logs + incident management + on-call + status pages
- Clean, fast dashboard with a good free tier for getting started
- Log management in the same platform as monitoring
- Multi-cloud endpoint checks with global locations
- Terraform provider for config-as-code
Pricing
| Component | Price | Included |
|---|---|---|
| Free | $0/mo | 10 monitors, 3-min intervals, 1 status page |
| Responder | $29/mo/seat (annual) | Monitoring, incidents, on-call |
| Additional monitors | $21/50 monitors | Added to any paid plan |
| Logs | Separate pricing | Ingestion + retention-based |
Cost traps
- Per-seat pricing multiplies with team size. A 10-person rotation is $290/mo in seats alone
- Monitor add-on packs stack up when you watch hundreds of endpoints
- Log management is a separate meter from monitoring
Limitations
- Not an infrastructure-metrics or APM platform. It's the external/uptime + logs layer
- Response body validation is keyword-based, not JSON path
- Deep container/Kubernetes telemetry isn't its focus
Best for: Small teams that want uptime monitoring, logs, on-call, and a status page in one place and are comfortable with per-seat pricing.
Decision framework
The right cloud monitoring tool depends on which half of the problem you're solving. Most teams eventually need both layers.
Start with your cloud shape. If you're all-in on one provider, the native tool (CloudWatch, Google Cloud Monitoring, or Azure Monitor) gives you the deepest, lowest-friction infrastructure telemetry with no new vendor. The moment you're genuinely multi-cloud, a native tool becomes one silo among several, and a cloud-agnostic platform (Datadog, New Relic, Dynatrace, Grafana Cloud) earns its cost.
Separate infra telemetry from external checks. Host metrics, traces, and logs answer "what is my infrastructure doing?" External checks and dependency correlation answer "is my service working for users, and is a vendor the reason it isn't?" Datadog, New Relic, and Dynatrace cover both at a price. If you already have infra metrics covered by a native tool, adding a focused external layer like DevHelm or Better Stack is often cheaper than upgrading to a full-stack suite.
Model total cost at your real scale. "Starting at $15/host" is not the bill. Usage-based tools grow with metric cardinality and log volume; per-seat tools grow with headcount; consumption tools grow with units you can't easily predict. Run the numbers for your host count, data volume, team size, and check frequency before signing anything.
| Scenario | Recommended Tool | Why |
|---|---|---|
| AWS-only, want native infra metrics | Amazon CloudWatch | Zero-setup AWS telemetry, no extra vendor |
| GCP-only, want SLOs and traces | Google Cloud Monitoring | Native metrics, logs, and error-budget tracking |
| Azure-only, want APM + logs | Azure Monitor | Application Insights + KQL log analytics |
| Multi-cloud, want one full-stack pane | Datadog or New Relic | Infra + APM + logs across all clouds |
| Enterprise, want AI root-cause | Dynatrace | OneAgent + Davis automation at scale |
| Prometheus/Grafana shop | Grafana Cloud | Managed open-source stack, cloud-agnostic |
| Have infra metrics, need external + dependency layer | DevHelm | Endpoint checks + vendor correlation at flat pricing |
| Small team, want uptime + logs + on-call | Better Stack | All-in-one dashboard, per-seat pricing |
Frequently asked questions
What is cloud monitoring?
Cloud monitoring is the practice of collecting metrics, logs, traces, and availability data from applications and infrastructure running in the cloud, then alerting when something degrades. In practice it splits into two layers: infrastructure monitoring (host, container, and service telemetry) and external monitoring (endpoint checks and third-party dependency correlation). Most teams end up running tools from both layers.
Which cloud monitoring software is best for AWS, Azure, or Google Cloud?
For single-provider shops, the native tool is usually the lowest-friction choice: CloudWatch for AWS, Azure Monitor for Azure, and Google Cloud Monitoring for GCP. They collect provider telemetry with no setup. The trade-off is lock-in. None of them is a strong multi-cloud pane of glass.
What are the best multi-cloud monitoring tools?
If you run workloads across two or more providers, a cloud-agnostic platform beats stitching native tools together. Datadog and New Relic are the most complete options for infra, APM, and logs across clouds. Grafana Cloud is the open-source-friendly choice. DevHelm covers the external layer (endpoint checks plus vendor-dependency correlation) without per-host pricing.
What should I use for Kubernetes and container monitoring?
For deep Kubernetes and container telemetry, Datadog, Dynatrace, New Relic, and Grafana Cloud (via Prometheus) all ingest pod, node, and cluster metrics. If you only need to know whether the services exposed by your cluster are reachable and correct from the outside, an external checker is a cheaper complement to cluster-level telemetry.
Is there open-source cloud monitoring software?
Yes. Prometheus with Grafana is the standard open-source stack, and Grafana Cloud is its managed form (Prometheus, Loki, Tempo). It's cloud-agnostic and covers metrics, logs, and traces, at the cost of more assembly and cardinality management than a turnkey tool.
How do I keep cloud monitoring costs under control?
Watch the meters that scale silently: metric cardinality, log ingestion and retention, per-host fees, and per-seat charges. Flat-rate tools (like DevHelm) and native pay-as-you-go services are the most predictable. Usage-based full-stack suites are the least. Model total cost at your real host count, data volume, and team size, not the advertised entry price.
What's the difference between cloud monitoring and observability?
Monitoring answers known questions ("is CPU high, is the endpoint up, did the error rate spike?") with metrics, dashboards, and alerts. Observability adds the ability to ask new questions after the fact by correlating high-cardinality metrics, traces, and logs. Full-stack platforms market themselves as observability; focused uptime and dependency tools market themselves as monitoring. Most teams need some of both.
What's the best cloud monitoring tool for startups?
Priorities for a startup or small SaaS team: a usable free tier, predictable pricing, and fast setup. New Relic's free tier and DevHelm's flat plans are both strong starting points. You get real coverage without an enterprise contract, and you can add infra-metrics depth later as you scale.
Related guides
If you want to go deeper on one slice of cloud monitoring, these guides pick up where this comparison leaves off.
Compare more monitoring tools
— how the leading uptime and page-monitoring tools compare on check types, probe regions, and alerting.
— the Prometheus and Grafana ecosystem plus other self-hosted options.
— what you can actually monitor at $0, and where free tiers stop being enough.
— head-to-head between the two enterprise full-stack platforms on features, AI analysis, and pricing.
Go deeper on the concepts
— defining monitors in version control with a CLI, Terraform, and SDKs.
— how tracing complements metrics and logs in an observability stack.
— common certificate errors and how to catch expiries before users hit a warning.
The real question
Every tool on this list can draw a graph of your cloud. The ones that matter are the ones that answer the question you actually ask during an incident: what broke, and can I fix it, or is it someone else's cloud? Infrastructure-metrics platforms tell you your containers are healthy. They're less helpful when your app is degraded because a third-party API you depend on is having a bad day. That's a failure inside your service that isn't your infrastructure's fault.
If your infra telemetry is already handled by a native tool or a full-stack suite, the missing layer is usually external checks with response validation and dependency correlation. Knowing that checkout is down and that the common thread is Stripe. If you want that layer without a per-host or per-GB bill, .
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