Quick Summary
In this guide, we explore the growing demand for multi-cloud observability solutions and what it takes to implement them effectively. From the 4 pillars to tool comparisons, cost considerations, and a step-by-step rollout guide, we cover everything CTOs need to make informed decisions.
Table of Contents
Introduction
No modern application relies on a single cloud. Several teams pick and choose between AWS vs Azure vs Google Cloud, depending on what each does best, and that’s become the norm rather than the exception.
According to the Cloud Report, 64% of organizations expect their use of multi-cloud to increase in the next two years. But the more clouds you add, the harder it becomes to see what’s actually going on.
A single user request can touch multiple clouds within seconds, yet logs, metrics, and traces remain scattered across different dashboards. That’s where multi-cloud observability comes in.
This guide breaks down my experience on how it works, why traditional monitoring falls short in distributed systems, and what it takes to build a unified view across your clouds, without adding operational chaos.
What Is Multi-Cloud Observability?
Multi-cloud observability refers to the ability to monitor and analyze data, such as metrics, logs, and traces, from applications that operate across multiple cloud providers, including Amazon Web Services, Microsoft Azure, and Google Cloud.
It gives your teams one unified view of system health so they can quickly detect, understand, and fix issues.
The 4 Pillars of Multi-Cloud Observability
The four pillars of multi cloud observability are essential to maintain application health, performance, and operational efficiency in distributed cloud systems.
1. Metrics
Metrics are numerical signals that show system performance over time, such as CPU usage, memory usage, request latency, throughput, and error rates. It helps teams understand overall system health and identify performance issues early.
Why is it Difficult?
In multi-cloud environments, each provider uses different naming conventions, dimensions, and tagging structures for similar metrics. Once teams add labels like regions, services, containers, or instance types, metric volume grows rapidly. It leads to high cardinality, increased observability costs, and slower query performance.
My Take:
In my experience, most teams end up collecting far more metrics than they ever actually use. What works better is standardizing naming conventions early, being selective about dimensions, and focusing on the metrics tied to business-critical services rather than tracking everything available by default.
2. Logs
Logs are time-stamped records generated by applications, infrastructure, and services. They provide detailed context such as error messages, user activity, and system behavior, which makes them essential for troubleshooting.
Why is it Difficult?
Each cloud platform stores and structures logs differently. Centralizing logs improves visibility, but cross-cloud data transfer and storage costs increase quickly at scale. On the other hand, keeping logs distributed reduces cost but makes debugging across environments far more complex.
My Take:
Based on my experience, centralizing every log is rarely a sustainable approach. What I have seen work well is centralizing critical application and security logs while archiving lower-value logs separately. It keeps costs under control without giving up the visibility your team actually needs.
3. Traces
Traces follow the journey of a request as it moves across services, APIs, and cloud environments. It helps teams understand latency, service dependencies, and bottlenecks in distributed architectures.
Why is it Difficult?
In multi-cloud systems, requests often cross different runtimes, networks, and cloud-native services. Without consistent instrumentation standards, trace context breaks between services, which creates visibility gaps during incident analysis.
My Take:
Distributed tracing only delivers value when instrumentation is consistent across every service. Based on what I have seen across projects, teams that adopt standards like OpenTelemetry early tend to avoid most of the trace correlation issues that come up later.
4. Events
Events capture important system changes, such as deployments, scaling actions, configuration updates, and outages. They provide operational context that helps teams understand what changed before an incident occurred.
Why is it Difficult?
Events are generated by multiple systems like CI/CD pipelines, infrastructure tools, Kubernetes clusters, and cloud services. Since these events are rarely standardized or connected to observability platforms, teams often miss critical context during outages.
My Take:
In my experience, many outages are not caused by infrastructure failures but by a recent change that slipped through without enough visibility. When I connect deployment events and configuration changes directly to observability dashboards, it usually significantly reduces root cause analysis time.
Struggling to Manage Four Pillars Across Multiple Clouds?
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Top 6 Multi-Cloud Observability Tools in 2026
We get asked some version of which tool we should use almost every week, and the honest answer never changes: no single platform is best since it depends on every team, scale, and budget.
Below are the hand-picked tools that help you to centralize monitoring, correlate telemetry data across cloud providers, and gain deeper visibility into complex multi-cloud environments.
1. Datadog
Datadog is the platform I see most often when we are called in to fix a runaway observability bill, and that says something about both its pros and cons. The integration catalog is the broadest in the industry, the UI is the most polished, and one platform covers infrastructure, APM, logs, security, RUM, CI visibility, and LLM observability without you having to stitch tools together. For CTOs who want a single host, this adds value to it.
The pricing model of the Infrastructure monitoring starts at $15 per host per month and Enterprise at $23, with separate per-GB charges for log ingestion ($0.10), log indexing ($1.70 per million events), custom metrics, and AI features. It also provides an infrastructure free with up to 5 hosts.
Each line is reasonable in isolation. Stacked across a real multi-cloud workload, they compound, and Datadog bills are growing 30 to 50% year over year for most teams.
When to pick it: You need capability breadth more than budget predictability, you have FinOps capacity to manage the bill, and model data volume against the pricing structure for at least 12 months out.
2. Dynatrace
Dynatrace is the one I recommend most often for large enterprises running genuinely complex distributed systems. Their two features earn it that spot. Smartscape automatically maps service topology across clouds, which removes the manual diagramming work most platforms still expect from your team. Davis, Dynatrace’s causal AI, does the closest thing to true root-cause analysis on this list when the underlying telemetry is structured well enough to feed it.
The cost is based on two approaches: APM pricing runs $29 to $58 per host per month, nearly double Datadog’s host rate, and the Davis Data Unit model adds a second pricing axis that’s harder to forecast than per-host or per-GB.
When to pick it: You are at 500+ hosts with complex service meshes, have a budget for premium tooling, and are willing to invest in the telemetry quality the platform’s AI actually needs to work.
Important Note: Dynatrace is actively shifting customers to Dynatrace Platform Subscription (DPS), a consumption-based model billed at $0.01 per GiB-hour with a 4 GiB minimum per host. The per-host pricing the draft cites is becoming the legacy framing.
3. New Relic
New Relic took a contrarian path on pricing a few years ago, and that choice is now its strongest selling point. Instead of charging per host, which punishes containerized and serverless architectures, New Relic charges are based on the amount of data ingested. You get 100 GB per month of free ingestion, then pay around $0.0 to $0.60 per GB, with user licenses billed separately from $49 to $349 per month, depending on access level.
For teams that want to instrument widely without watching the host count, this model is genuinely easier to reason about. The hidden cost is the user-license line, which surprises teams when their full-platform user count grows past the original estimate.
When to pick it: You are instrumenting heavily across many services, want a predictable per-GB cost rather than per-host cost, and can keep your full-access user count tight.
4. Splunk Observability Cloud
Splunk Observability Cloud is the right answer when an organization is already deep in Splunk for log analytics and security, and wants observability to share that data plane. The convergence story is real here, and we’ve seen it work well in regulated industries (financial services, healthcare) where the same data needs to serve SRE and SecOps simultaneously without duplicating ingestion costs.
Splunk observabiity pricing is the least transparent on this list. Splunk does not publish clean list prices for its observability tier, and most engagements are enterprise-negotiated based on data volume, host count, and which other Splunk products you already license.
When to pick it: Security and observability genuinely need to share a data plane, you are already invested in the Splunk ecosystem, and have procurement bandwidth for an enterprise sales cycle.
5. Grafana Cloud
Grafana Cloud is the option we recommend to our clients who want OpenTelemetry-native tooling without the burden of self-hosting Prometheus, Loki, and Tempo. It is a crucial open-source Grafana stack operated by the company that builds it, which means you get vendor expertise without permanent lock-in.
If you decide to bring it in-house later, your dashboards, queries, and data model travel with you. Its pricing is consumption-based and significantly cheaper than the commercial APM tier.
The free tier includes 10K active metrics series, 50 GB of logs, and 50 GB of traces per month, which is enough to run real production workloads for smaller teams. The Pro tier starts at $19 per month and scales with active series and ingested data; for a representative 100-host mid-size workload, monthly costs typically land between $3,000 and $7,000, materially below Datadog or Dynatrace.
However, the limitation is that its AI and ML capabilities are weaker than the commercial leaders, so automated anomaly detection often needs to be built or bolted on.
When to pick it: You want OTel-first observability, predictable pricing, and the architectural option to move to self-hosted later without re-instrumenting everything.
6. OpenTelemetry + Self-Hosted Stack
The self-hosted route, OpenTelemetry for instrumentation plus DevOps monitoring tools like Prometheus, Loki, Tempo, and Grafana for the backend. It has the lowest license cost and the highest operational burden on this list. License cost is effectively zero: every component is open-source and free to run.
Real cost moves to two other places. Infrastructure to run the stack typically lands between $1,500 and $4,000 per month for a 100-host workload (compute, storage, retention), and you need at least one full-time platform engineer to operate it.
When to pick it: You are at a scale where commercial licensing becomes prohibitive, have dedicated platform engineering capacity, and a clear compliance, residency, or cost-control reason to own the stack outright.
How Much Does Multi-Cloud Observability Cost?
Multi-cloud observability usually costs between 15% and 25% of your total cloud infrastructure bill. However, multi-cloud observability costs vary because most platforms charge based on a mix of hosts, containers, metrics, traces, retention rates, users, and advanced features.
If two companies with the same number of servers can end up with completely different bills, it depends on how much telemetry they generate and how long they keep it.
In my experience, most teams fall into one of these ranges:
| Company Size/Setup
| Approx Monthly Cost
| Reason for Cost
|
|---|
| Small startup (10-30 hosts)
| $500-$3,000/month
| Infrastructure monitoring + basic logs
|
| Mid-size company (50-200 hosts)
| $5000-$25000/month
| APM, log retention, Kubernetes
|
| Enterprise multi-cloud environment
| $50,000+/month
| High-volume traces, compliance retention, custom metrics
|
How to Set Up Multi-Cloud Observability?
When I talk to teams about multi cloud observability, one thing becomes clear very quickly: the challenge is rarely “how do we monitor multiple clouds?”
The real challenge is how to make AWS, Azure, Kubernetes clusters, APIs, containers, and distributed services feel like one connected system instead of isolated environments generating disconnected telemetry.
1. Standardize Telemetry Collection
The first thing my team focuses on is standardizing telemetry collection across all environments. Without this, observability becomes chaotic very quickly.
Every cloud provider structures logs, metrics, and traces differently. AWS CloudWatch, Azure Monitor, and Google Cloud Operations Suite all use their own formats and metadata styles. If my team simply pushes everything into one dashboard without normalization, the result is fragmented telemetry that becomes difficult for me to search, correlate, or trust.
At this stage, my focus is to define:
- What telemetry is actually valuable
- Which logs should be filtered
- How traces should be sampled
- How long should data be retained
Because if my systems collect everything without control, observability costs rise fast while signal quality drops.
Once telemetry collection becomes consistent, my next step is creating a centralized observability layer.
Many teams rely heavily on native cloud monitoring tools at first. That works in single-cloud environments, but in multi-cloud architectures, switching between separate dashboards slows down troubleshooting and incident response for my team.
Instead, I prefer a unified platform where logs, metrics, traces, infrastructure health, and application performance can all be analyzed together.
Depending on the organization, my stack could include:
- Datadog
- Dynatrace
- New Relic
- Splunk
- Grafana
- Custom observability setup
3. Implement Consistent Tagging and Naming Conventions
It is one of the most underestimated parts of observability, yet it creates some of the biggest operational problems.
We have seen environments where one team uses prod, another uses production, and another labels the same workload as live. At scale, that inconsistency creates reporting issues, broken dashboards, and confusing alerts that make incident response harder for everyone involved.
My tagging strategy usually covers:
- environments
- services
- clusters
- teams
- regions
- and deployment versions.
For example:
environment=productionservice=payment-apiteam=platform-engineering
These tags help me simplify:
- Alert routing
- Dashboard filtering
- Cost tracking
- Compliance reporting
- Incident ownership
4. Deploy a Collector in Each Cloud to Forward Telemetry
Rather than sending telemetry directly from workloads to the backend, my preference is to deploy collectors inside each cloud environment.
This gives me better control over:
- Telemetry routing
- Filtering
- Enrichment
- Batching
- Vendor portability
OpenTelemetry Collectors are especially useful because they allow my team to normalize and process telemetry before forwarding it to a centralized platform.
In Kubernetes environments, my teams usually deploy collectors as DaemonSets or sidecars so that telemetry gets captured automatically across nodes and services. If you run K8s clusters on more than one cloud provider, our multi cloud Kubernetes guide goes deeper on the topic.
This architecture also improves resilience for me because telemetry collection continues locally even if one backend experiences issues.
5. Set Unified Alerting and Cost Controls
One of the fastest ways to overwhelm engineering teams is poorly managed alerting.
In many multi-cloud environments, every tool generates alerts independently. That creates duplicate notifications, alert fatigue, and slower incident response for my team.
Instead, my preference is centralized alerting policies with standardized severity definitions across clouds.
At the same time, I pay close attention to observability cost controls because telemetry volume grows aggressively in distributed systems.
My strategy usually includes:
- Log filtering
- Trace sampling
- Retention limits
- Cardinality controls,
- Ingestion policies.
From my experience, observability rarely becomes expensive because of infrastructure monitoring alone. Most of the cost comes from uncontrolled log ingestion and high-volume tracing.
6. Enable Automated Incident Response
The final step for me is making observability operational instead of passive.
I do not want observability systems that only show dashboards after something breaks. My goal is to build systems that actively reduce downtime. That is where automated incident response becomes important.
Depending on the use case, my observability workflows may:
- Trigger alerts
- Scale infrastructure automatically
- Restart failing services
- Roll back deployments
- Create incident tickets
- Notify response teams instantly
Common Mistakes to Avoid in Multi-Cloud Observability
1. One of the biggest multi cloud adoption challenges we faced was treating each cloud provider’s native monitoring tools in isolation. It created data silos and left the entire team without a unified view of the infrastructure.
2. A clear multi cloud strategy was never defined before the expansion across multiple clouds, and that oversight quickly resulted in inconsistent implementations that became a nightmare to govern and maintain.
3. The absence of standardized tagging and naming conventions across AWS, Azure, and GCP made data correlation nearly impossible when incidents hit, and that was a painful lesson learned under pressure.
4. The decision to overlook end-to-end distributed tracing across cloud boundaries cost hours during critical outages, as there was no clear way to trace how requests moved across services.
5. Too much focus on infrastructure metrics like CPU and memory created a significant blind spot; application-level traces and logs were the actual indicators of what went wrong.
6. Separate observability tools across development, DevOps, and SRE teams meant there was no shared context during critical moments, and coordination became nearly impossible as a result.
7. Observability was treated as a one-time setup rather than an area that needed constant attention, and by the time it was revisited, the entire setup had fallen out of sync with the actual state of the infrastructure.
Multi Cloud Observability vs Multi Cloud Monitoring: The Difference
Most CTOs I talk to get confused between multi cloud observability vs multi cloud monitoring. Both serve different purposes, and the following comparison is for you to understand the core difference.
| Aspects
| Multi Cloud Observability
| Multi Cloud Monitoring
|
|---|
| Primary questions
| Why is it broken?
| What is broken?
|
| Data model
| High-cardinality metrics, logs, traces, events
| Pre-defined metrics and thresholds
|
| Failure modes
| Unknown and emergent
| Known and predictable
|
| Investigation pattern
| Query then correlate
| Dashboard then alert
|
| CTO concern
| MTTR and customer impact
| Uptime SLA
|
| Best fit
| Distributed systems, microservices, cross-cloud workflows
| Stable architectures, predictable workloads
|
For CTOs, the practical takeaway is that monitoring is sufficient when your architecture is stable and predictable. Observability becomes necessary when you run distributed services across multiple clouds, when your engineers cannot predict every failure mode, and when MTTR is starting to affect revenue or SLAs.
If your team is not on multi cloud yet, but still need to explore the difference between the two on a single cloud setup, check oot our blog on cloud monitoring vs cloud observability.
Healthcare Case Study: Multi Cloud Observability Solution by Bacancy
Our client, a leading healthcare provider, was running patient data, clinical applications, and billing systems across AWS, Azure, and GCP, with zero unified visibility across any of them.
The Problem Client Faced:
Every cloud had its own monitoring tool. Every team had its own alerts. And when something broke, nobody could trace it fast enough. For a healthcare organization, it is a patient safety risk. That is when our client reached out for our multi cloud services.
The Bacancy Solution:
We built a unified observability platform on OpenTelemetry, pulling metrics, logs, and traces from all three clouds into one place. Our team set up cross-cloud distributed tracing end to end, standardized tagging conventions across all environments, and put sensitive data masking in place to meet HIPAA requirements.
With our multi cloud expertise, we replaced the fragmented alerting system that had been slowing our client’s team down for months with a single, consolidated framework.
The Results We Delivered:
- Mean time to resolution dropped by 60%
- Observability costs reduced by 35%
- Full HIPAA compliance achieved across log retention and data governance
- Development, DevOps, and SRE teams are finally working from the same visibility layer
How Bacancy Helps CTOs Implement Multi-Cloud Observability
For most CTOs we work with, multi-cloud observability is to gain clear visibility and control across AWS, GCP, and Azure while keeping systems streamlined, growth-ready, and cost-effective. As a cloud consulting service provider, Bacancy helps simplify this by turning fragmented telemetry into a unified and actionable system.
Our team starts the process by defining a clear multi cloud observability strategy aligned with business-critical services. It is followed by standardized telemetry collections to ensure consistent data across environments.
Our team unifies monitoring across cloud platforms into a single observability solution for multi-cloud environments, which gives a consolidated view of performance, health, and cost in one place.
By standardizing how resources are tagged, named, and governed across providers, we eliminate the blind spots that fragmented tooling creates, improve alert accuracy, streamline reporting, and enable precise cost tracking at every layer of the infrastructure.
Frequently Asked Questions (FAQs)
Yes, but it is increasingly rare in 2026. OpenTelemetry has become the standard for vendor-neutral instrumentation, and most commercial platforms are using it as a first-class input. Skipping OTel locks you more deeply into whichever vendor’s agent you choose, which becomes a problem during the next contract renewal.
For a commercial platform with mature integrations, a basic rollout across two or three clouds usually takes 6 to 12 weeks for instrumentation and dashboard setup, plus another 3 to 6 months to reach mature alerting and ownership. Self-hosted stacks add another 2 to 4 months for the build phase.
The ROI usually shows up in three places: lower MTTR, which protects revenue during incidents; faster engineering velocity, because debugging time drops significantly; and reduced cloud waste, because observability reveals over-provisioned services.
It depends on your engineering organization structure. Under DevOps, observability tends to optimize for speed. Under SRE, it optimizes for reliability targets. Under platform engineering, it optimizes for developer experience across teams.
Choose an observability solution that can unify logs, metrics, traces, and events across AWS, Azure, GCP, and Kubernetes in one platform. Look for features like OpenTelemetry support, centralized dashboards, real-time alerting, distributed tracing, and cost optimization controls. The right solution should simplify troubleshooting, improve visibility, and scale easily with your cloud infrastructure.
