The challenges of multi-cloud observability and how to overcome them

As organizations embrace multi-cloud architectures—leveraging providers like AWS, Azure, and Google Cloud—the complexity of monitoring their cloud environment skyrockets. 

Multi-cloud observability refers to the ability to monitor, measure, and understand the state of applications and infrastructure spread across multiple cloud providers. While observability is crucial for ensuring performance, availability, and security, it becomes exponentially harder to implement in a multi-cloud world.

In this blog, we'll dive into the core challenges of multi-cloud observability and explore practical strategies to overcome them.

Challenge 1: Monitoring tool fragmentation across cloud providers

Each cloud platform offers its own monitoring tools—like AWS CloudWatch, Azure Monitor, or GCP Cloud Monitoring. These tools are designed to work best within their own ecosystems and don’t integrate seamlessly with others. This leads to fragmented monitoring dashboards, duplicated alerting systems, and a lack of centralized insights.

The solution: Implement a cloud-agnostic monitoring platform like ManageEngine Applications Manager, which integrates natively with multiple cloud providers. These versatile monitoring platforms unify metrics and events into a single dashboard, reducing context-switching and streamlining incident response.

Challenge 2: No unified view across environments   

In multi-cloud setups, workloads are often distributed across regions and providers. This makes it difficult to correlate metrics from different systems—for example, tracing a latency issue in GCP to a backend database in AWS.

The solution: Deploy a centralized monitoring solution that supports cross-cloud telemetry correlation. Look for features like distributed tracing, topology maps, and service dependency visualization to understand system behavior end-to-end—regardless of where the services are running.

Challenge 3: Inconsistent metrics and log formats

Cloud-native services often use different naming conventions, units, and data formats. For instance, a simple metric like CPU usage might have different thresholds or aggregation methods in AWS vs. Azure. This inconsistency makes it hard to compare or analyze performance metrics across services.

The solution: Use OpenTelemetry and standardized metric exporters (like Prometheus) to ensure uniform data collection. Normalize logs and metrics during ingestion using tools like OpenTelemetry Collector, Fluent Bit, or Vector, so data is uniform before it reaches your monitoring backend.

Challenge 4: Scalability and performance issues in monitoring systems   

As organizations scale across clouds, the volume of telemetry data explodes. Poorly architected monitoring pipelines struggle to ingest and process logs, metrics, and traces in real-time—resulting in slow dashboards, missing data, or delayed alerts.

The solution: Design a scalable telemetry pipeline that can handle high-throughput data. Use stream-processing and buffering technologies like Kafka or Kinesis, and adopt storage-optimized backends, like Loki for logs or Thanos and Cortex for metrics, to ensure performance doesn’t degrade at scale.

Challenge 5: Security, compliance, and data residency risks   

Sending monitoring data across regions or clouds raises security and compliance issues. Sensitive logs or telemetry may be exposed or stored in regions that violate regulatory requirements like the GDPR or HIPAA.

The solution: Ensure data encryption at rest and in transit, use role-based access control (RBAC) for dashboards and alerting, and maintain region-specific storage where required. Choose cloud monitoring tools that offer compliance certifications and allow data residency configuration.

Challenge 6: Tool sprawl and operational overhead  

Using different monitoring tools for each cloud service results in tool sprawl—multiple UIs, alerting systems, data formats, and maintenance overhead. It also creates knowledge silos within teams and increases costs.

The solution: Standardize with a single monitoring solution that supports all major cloud platforms and consolidate data pipelines and alerting mechanisms on it. This reduces costs, simplifies training, and improves team collaboration across DevOps, SRE, and platform engineering teams.

Challenge 7: Ineffective alerting and incident response 

In a multi-cloud setup, alert fatigue can occur due to redundant or noisy alerts from different systems. Meanwhile, critical issues might go unnoticed due to a lack of visibility across services and dependencies.

The solution: Leverage monitoring solutions with AI-driven anomaly detection, dynamic thresholds, and intelligent alert routing. Integrate alerting with tools like PagerDuty, Opsgenie, or Slack to ensure fast and contextual incident response. Correlate alerts with service health to prioritize real issues.

Unified visibility isn't optional, it's essential

Achieving observability in a multi-cloud environment isn’t easy—it requires intentional strategy, the right tools, and a culture of collaboration. But with unified cloud observability platforms like Applications Manager, open standards, and centralized data pipelines, organizations can overcome these challenges and gain deep visibility into their systems, no matter where they run.

Whether you're running microservices, hybrid infrastructure, or containerized workloads, a purpose-built cloud monitoring solution like Applications Manager empowers you to stay ahead of outages, maintain compliance, and deliver seamless digital experiences. Don’t let fragmented tools slow you down—invest in a centralized, intelligent monitoring solution that grows with your cloud strategy.

New to Applications Manager? Start your 30-day, free trial today and take control of your multi-cloud observability challenges.