Cloud monitoring services that cover metrics, logs, and traces
Cloud monitoring services turn the signals your systems already emit into answers. We instrument applications with OpenTelemetry and collect infrastructure metrics with Prometheus and Grafana. Logs are centralized, so one query covers every service. Our note on observability versus monitoring explains where the two differ.
Metrics tell you a service is slow. Traces tell you which downstream call made it slow. We build both, then tie alerts to SLOs rather than raw CPU thresholds. Pages route into an on-call rotation with a named owner per service.
This runs across AWS, Azure, and GCP, on virtual machines and on Kubernetes. Collectors, dashboards, and alert rules ship as Terraform and Helm, so every environment gets the same coverage.
What's Included in Our Cloud Monitoring Services
Scope runs from instrumenting the first service to running the alerting rotation. Blocks are grouped by the stage they belong to, and most teams start at collection.
Application Instrumentation with OpenTelemetry
Auto-instrumentation for Java, Node.js, Python, Go, and .NET, plus manual spans where the agent stops. Cloud application monitoring covers request latency, error rates, and dependency calls, with trace IDs carried into every log line.
Infrastructure and Kubernetes Metrics
Node, container, and control-plane metrics through node-exporter, kube-state-metrics, and cloud exporters. Prometheus scrapes them; Thanos or Mimir holds the history you need for capacity planning.
In practice: the observability layer we built for a SaaS platform on AWS EKS.
Centralized Log Pipeline
Fluent Bit or Filebeat shipping into Elasticsearch, OpenSearch, or Loki. Index lifecycle and retention policies hold storage cost steady as volume grows. One query replaces a tour of every service.
In practice: EFK logging with lifecycle policies for Harappa.
Distributed Tracing Across Services
OpenTelemetry Collector with Tempo or Jaeger and tail-based sampling. A single request is followed across every hop. Latency is attributed to a service rather than debated in a call.
SLOs, SLIs, and Error Budgets
Availability and latency indicators per user journey, targets agreed with product owners, and burn-rate alerts that fire on user impact. The same practice underpins our site reliability engineering work.
Alert Routing and On-Call Integration
Alertmanager, PagerDuty, or Opsgenie with severity tiers, escalation paths, and Slack or Teams delivery. Every rule carries a runbook link and an owning team before it goes live.
In practice: Prometheus and Grafana alerting built for Mathleaks across two regions.
Security and Audit Telemetry
CloudTrail, VPC Flow Logs, GuardDuty findings, and Kubernetes audit logs land in the same pipeline as reliability data. Detection engineering and posture management sit with our cloud security services.
Dashboards for Engineers and for the Business
Grafana dashboards per service and per team, plus one uptime and latency view for leadership. Each panel states the query behind it, so numbers can be checked rather than trusted.
24×7 Monitoring Operations
We watch the alerts, triage, and escalate under agreed response times. Available on its own or inside managed AWS operations, where the same team is allowed to fix the cause.
In practice: the 24×7 monitoring and on-call cover we run for OurShopee.
Telemetry Cost Control
Metric cardinality budgets, log sampling, and tiered retention keep the observability bill in proportion to the answers it buys. Reviewed each month next to cloud cost management and Kubernetes cost work.
Monitoring Challenges We Solve
Alerts fire constantly and on-call engineers stop reading them
Static CPU and memory thresholds page on symptoms that clear themselves. Within weeks, a real incident looks identical to the noise around it.
Our Solution
We remove alerts nobody acts on, group the rest by service, and rewrite thresholds as SLO burn rates. Every page carries a runbook and a named owner.
Infrastructure dashboards read healthy while users report errors
Host metrics look fine because the failure is inside the application. Nobody can see the failing dependency, the retry storm, or the slow query.
Our Solution
OpenTelemetry instrumentation, request tracing, and error-rate indicators per endpoint. In practice: the Prometheus, Grafana, and ELK layer we built on AWS EKS.
Logs, metrics, and traces sit in three tools that do not link
An incident means three consoles and manual timestamp matching. Root-cause analysis takes hours the outage does not give you.
Our Solution
One pipeline: trace IDs injected into logs, exemplars linking metrics to traces, one Grafana entry point. In practice: Prometheus, Alertmanager, and EFK unified for Harappa.
There is no agreed uptime or latency target
Engineering and product disagree on what counts as an outage. Reliability work loses to feature work because nothing measures the gap.
Our Solution
SLIs defined per user journey, with SLO targets signed off by product owners. Error-budget burn is published on one shared dashboard.
Coverage drops every time a new service ships
Instrumentation is a manual checklist item at the end of a sprint. Services reach production with no dashboard, no alert, and no owner.
Our Solution
Instrumentation, dashboards, and alert rules ship inside the service template and deploy through GitOps. A service with no telemetry fails the pipeline check.
Observability tooling costs rise with log and metric volume
Per-host and per-gigabyte pricing scales with growth rather than value. One high-cardinality label can multiply the bill without adding an answer.
Our Solution
Cardinality budgets, tail-based sampling, and tiered retention. Self-hosted Prometheus carries the volume; commercial APM stays where it earns its price.
How a Cloud Monitoring Engagement Works
Five stages from telemetry audit to a tuned alerting rotation. Teams with an existing stack usually join at stage two or three.
Most teams already have something: CloudWatch dashboards, a Grafana instance nobody owns, or an APM trial that expired. We audit what exists before adding tools, the same way an infrastructure audit opens a DevOps engagement.
Telemetry Audit
We inventory the agents, dashboards, alert rules, and spend you have today. The output is a gap list: services with no instrumentation, alerts nobody acts on, logs never queried.
Instrumentation and Collection
OpenTelemetry SDKs and auto-instrumentation go into the applications; exporters and collectors go onto hosts and clusters. Everything is deployed by Helm and Terraform so the same setup repeats per environment.
SLO and Alert Design
Indicators per user journey, targets agreed with product owners, and burn-rate alerts replacing static thresholds. Each rule gets a runbook and an owning team before it can page anyone.
Dashboards and On-Call Rollout
Grafana dashboards per service plus a leadership uptime view. Routing into PagerDuty or Opsgenie, escalation tiers configured, then a game day that tests whether the rotation works.
Tuning and Cost Control
A monthly review of alert precision, dashboard use, metric cardinality, and retention. Rules that page without action are deleted, and telemetry spend is trimmed as volume grows.
Ready to cut alert noise and close monitoring gaps?
Get a free observability assessment: what is covered today, alert precision, telemetry spend, and the gaps that hide incidents.
Talk to an Observability EngineerObservability Stacks We Build and Run
We deploy the stack that fits your scale, budget, and team, then operate it with you. These are the five we implement most often.
Prometheus, Grafana, and Alertmanager
The open-source default for metrics, dashboards, and alerting, with Thanos or Mimir for long-term storage. Deployed as code and handed over with runbooks. Details on our Prometheus and Grafana consulting page.
OpenTelemetry and Distributed Tracing
Vendor-neutral instrumentation feeding Tempo, Jaeger, or a commercial backend. Instrument once and change backend later without editing application code. Background reading: observability in microservices architecture.
Log Management with ELK, OpenSearch, or Loki
Fluent Bit shipping, index lifecycle policies, and query patterns written for incident work rather than demos. Sized against the log volume you produce, including audit and compliance retention.
Cloud-Native Monitoring
CloudWatch, Azure Monitor, and Google Cloud Operations for teams standardized on one provider. Fastest to switch on, and the closest fit to managed AWS operations and cloud-native architectures.
Commercial APM Platforms
Datadog, New Relic, or Dynatrace where a managed backend beats running your own. We size the contract, instrument with OpenTelemetry to keep the exit open, and tune ingest. See New Relic services.
Self-Hosted, Cloud-Native, or Commercial APM
Three ways to buy cloud monitoring solutions, and what each one costs in effort, money, and lock-in. Most teams end up with two of the three.
| Factor | Self-Hosted (Prometheus, Grafana, Loki) | Cloud-Native (CloudWatch, Azure Monitor) | Commercial APM (Datadog, New Relic) |
|---|---|---|---|
| Setup effort | High. You run the storage, upgrades, and scaling. | Low. Metrics are on by default for managed services. | Medium. Agents, config, and ingest rules per service. |
| Cost model | Compute and storage you provision. | Per custom metric, per GB ingested, per dashboard query. | Per host, per GB, per user, per feature tier. |
| Cost as you grow | Flattest curve. Retention and cardinality are yours to set. | Rises with custom metrics and log volume. | Rises fastest. Cardinality and host count drive the bill. |
| Application tracing | Tempo or Jaeger, run by your team. | X-Ray or Cloud Trace, limited to that provider. | Strongest out of the box, including code-level profiling. |
| Multi-cloud coverage | Same stack on every provider and on-premise. | One console per provider, no shared view. | Single view across providers. |
| Lock-in | None. Data and queries stay in your accounts. | Tied to one provider's agents and query language. | Vendor agents, unless you instrument with OpenTelemetry. |
| Best fit | Kubernetes-heavy teams with platform engineers. | Single-cloud teams that want coverage this week. | Small teams who would rather buy than operate. |
Pricing depends on host count, log volume, and metric cardinality more than on list price. Vendor calculators rarely match the invoice. We model all three against your current telemetry before recommending a stack. Hybrid setups are common: self-hosted metrics and logs, plus commercial APM where code-level tracing earns its price.
