According to a 2020 survey, almost 76% of companies used two or more than two public clouds. Big enterprises (those pulling in over $1B) were twice as likely to be knee-deep in three or more.
So you’re living in a multi-cloud world now. Whether you planned it or not, it’s happening.
However, more clouds do not equal more calm. Sure, you get flexibility, cost benefits, and the freedom to avoid vendor lock-in, but managing it all is a mess.
That mess is what multi-cloud management came to control.
In this guide, we’ll learn what is multi-cloud management, and the challenges and benefits it brings. You’ll also discover some top multi-cloud management platforms.
So let’s get into it.
What is multi-cloud management?
A multi-cloud strategy is when you don’t put all your digital eggs in one corporate cloud basket.
You use multiple public clouds—AWS, Microsoft Azure, Google Cloud, etc.—because each one has its own strengths. For example, AWS might be better for storage, but Google has superior AI tools.
However, managing all of them at once without overspending your budget requires multi-cloud management. It’s a system to keep everything from exploding when let’s say, one cloud randomly increases egress fees by 400%.
Multi-cloud vs. single cloud vs. hybrid cloud
Don’t confuse multi-cloud with hybrid cloud. A hybrid cloud is when you mix public clouds with private infrastructure (like your own on-premises servers).
Multi-cloud is strictly about using multiple public clouds. You might do this for different applications or disaster recovery. Or perhaps one provider’s AI tools are better, while another’s storage is cheaper.
This is unlike a single-cloud setup, where you’re at the mercy of one provider’s unplanned downtime or pricing whims. Multi-cloud distributes workloads across different vendors, ensuring no single point of failure can cripple everything.
Here’s what is meant by public and private cloud services to give you a better understanding of the concept:
- Public cloud: Shared computing cloud resources (servers, storage, etc.) that cloud providers (AWS, Azure, GCP) rent to multiple customers over the internet.
- Private cloud: A company’s proprietary cloud infrastructure, with workloads running either in the company’s data center or through a dedicated third-party host.
What are the benefits of multi-cloud management?
Reduces strain on IT teams
Multi-cloud management consolidates monitoring, security, and workload controls into a single dashboard. That way, IT teams aren’t juggling six different vendor portals just to check if their databases are on fire.
Instead of manually cross-referencing AWS CloudWatch with Azure Monitor while Google’s logging system silently drops half your metrics, you get one unified view.
Improves security
More clouds mean more attack vectors.
Multi-cloud management enforces uniform security policies (encryption, IAM, network rules) across providers. As a result, you’re not stuck with AWS’s convoluted IAM roles, while Azure’s NSGs mysteriously allow traffic from Antarctica.
Automated threat detection scans all multicloud environments simultaneously. So when a zero-day exploit hits, you’re not manually patching three separate consoles while hackers party in your S3 buckets.
Optimizes costs
Public clouds are designed to make you overspend.
Multi-cloud management tracks spending across providers, flags idle instances, and compares pricing to route workloads to the cheapest zone.
Reserved Instances on Amazon Web Services? Spot Virtual Machines (VMs) on Azure? Preemptible GPUs on Google Cloud (GCP)? The system auto-allocates based on cost-performance ratios. Because no CFO wants to hear, “We blew the budget on a Kubernetes cluster.”
Maximizes availability
When AWS us-east-1 implodes (again), multi-cloud fails over to Azure West Europe without users noticing.
The geographic distribution also means Japanese users aren’t waiting two seconds for a response from Virginia.
No provider has 100% uptime (despite SLAs promising “five-nine availability”. Spreading workloads means your entire app doesn’t die because someone at Google tripped on a fiber cable.
Enables specialized workloads
No single cloud does everything best.
Multi-cloud lets you run SQL Server on Azure, TensorFlow on GCP, and your frontend on AWS.
Trying to force everything into one provider is like using a Swiss Army knife to chop down a tree. It’s possible, but why do it?
Eliminates vendor lock-in
Multi-cloud keeps you agile. If AWS jacks up S3 prices, you shift cold storage to Azure Blob.
No more “Oh, you built everything on DynamoDB? Now you’ll pay $10/GB for scans forever.”
Portability tools (Terraform, Kubernetes) prevent rewriting entire apps just to switch providers. Because nobody has time to refactor 500 Lambdas into Azure Functions.
Challenges of multi-cloud management
Creates operational complexity
Juggling multi-cloud platforms means dealing with entirely separate ecosystems.
Each provider has unique APIs, management consoles, and quirks that force engineers to context-switch constantly.
What works in AWS Lambda breaks in Azure Functions. Kubernetes might be standardized until you realize EKS, AKS, and GKE each have their own special flavors of pain.
The cognitive load alone could power a small data center.
Integration becomes a constant headache
Applications need to work across clouds, but nothing is truly portable without significant rework.
Storage APIs differ, authentication methods vary, and even basic things like DNS management become nightmares.
Moving data between clouds often means writing custom sync jobs that fail at 2 a.m. Licensing gets messy, too; for example, an enterprise SQL license might work in Azure but violate terms in AWS.
Suddenly, your “cloud-agnostic” architecture requires three different implementations of the same service.
Security gaps multiply exponentially
Each cloud provider has its own security model.
Misconfigurations creep in when teams apply one cloud’s security patterns to another. Storage buckets get left public. VM firewalls are misapplied. Audit logs go unwatched because they’re scattered across five different interfaces.
The attack surface isn’t just larger. It’s full of blind spots where no single team has complete visibility.
Talent requirements become unrealistic
Finding engineers who deeply understand even one cloud platform is hard enough.
Now, you need people fluent in AWS, Azure, GCP, etc.. Plus, you need all the ancillary technologies, like Terraform and Kubernetes, that glue them together.
Training existing staff means pulling them away from critical work for months. Vendor certifications help, but real-world multi-cloud experience is rare.
The result is either overworked experts or teams constantly hitting walls because “that’s not how it works in this cloud.”
Visibility shatters across platforms
Troubleshooting requires checking AWS CloudWatch, Azure Monitor, Google Operations Suite, and whatever third-party tools you’ve bolted on.
Correlating events across systems is like playing detective across multiple crime scenes. Cost reporting becomes a spreadsheet nightmare, as each provider uses different billing constructs and metrics.
Without third-party tools, you’re left manually stitching together dashboards that are always out of date.
Managing multi-cloud
Going multi-cloud means not putting all your eggs in one provider’s basket. After all, that basket might go down, get expensive, or suddenly change its policies.
The strategy lets you mix and match public cloud services based on what each public cloud provider does best. As a result, you avoid several issues we just discussed in the benefits of multi-cloud section.
But managing multi-cloud solutions manually is a recipe for chaos (refer to the challenges section above).
That’s where multi-cloud management platforms come in. They fall into four main categories:
- Cost management/FinOps platforms track spending across clouds, flag waste (like idle VMs), and help you allocate costs efficiently.
- Monitoring and performance tools give a single dashboard for metrics, logs, and alerts across different cloud vendors. No more switching tabs to troubleshoot why an app is slow.
- Automation and orchestration tools handle deployments, scaling, and workflows uniformly across clouds. They turn manual, error-prone processes (like provisioning infrastructure) into code that runs the same way everywhere.
- Security and regulatory compliance tools enforce consistent policies for access controls, encryption, and audits. Instead of juggling the cloud organization’s policies separately, you define rules once and apply them everywhere.
How do you pick the right tool from these? How do you ensure cross-cloud functionality and scalability as you grow? Are you just replacing one vendor lock-in with another?
Automation features should handle repetitive tasks, while cost controls need to show real-time spending across providers.
The interface has to be usable because no one wants another convoluted system that requires a PhD to navigate.
And when things break (because they will), support matters. Otherwise, you’re left Googling error messages at 3a.m..
Multi-cloud use cases and examples
Going multi-cloud makes cloud workloads efficient by leveraging the best features of each provider.
Here are a few use cases and with examples of multi-cloud managed services:
Disaster recovery without a single point of failure
When one cloud goes down, having workloads replicated across cloud service providers helps ensure business continuity.
Financial firms, for example, use multi-cloud solutions to ensure that transactions keep processing even if a region fails.
Low latency for global users
Hosting apps in a single cloud means customers far from that provider’s data centers get sluggish performance.
Multi-cloud fixes that by placing workloads closer to users, Netflix-style. Companies like Spotify deploy across AWS and Google Cloud; so, whether you’re in Tokyo or Toronto, playlists load instantly.
Meeting regional compliance headaches
General Data Protection Regulation (GDPR) in Europe. California Consumer Privacy Act (CCPA) in California. Such regulations dictate where data can live.
Multi-cloud environment lets companies park data in Azure’s German regions for EU compliance while using AWS GovCloud for US government workloads.
Killing shadow IT before it kills security
Employees using unsanctioned apps (e.g., random Dropbox accounts) is a security nightmare.
Multi-cloud management provides approved alternatives like OneDrive for Business in Azure, plus AWS WorkDocs. Teams get flexibility, without the risk.
Kubernetes without the vendor handcuffs
Running containers? Multi-cloud Kubernetes clusters (AKS, EKS, GKE) mean no single cloud holds your apps hostage.
Adobe uses this multi-cloud approach to deploy creative tools globally while keeping costs in check.
Top multi-cloud management tools
Implementing and managing multi-cloud architectures can be a headache.
That’s why there are different multi-cloud management platforms available to handle different aspects of this setup.
Here are a few top tools.
Ternary: multi-cloud FinOps platform
Ternary is a FinOps platform that cracks open the black box of multi-cloud spending. It shows exactly who’s burning cash on overprovisioned VMs, idle storage, or redundant services across GCP, AWS, and Azure.
Ternary tags resources by team, project, or cost center (so Marketing can’t blame Engineering for that $50K/month Redis cluster).
Its AI flags anomalies like sudden cost spikes, while optimization recommendations suggest rightsizing or purchasing Reserved Instances.
It also normalizes billing data from all providers into one dashboard. That way, you don’t have to manually reconcile GCP “credits” with Azure “overages.”
For enterprises drowning in hidden costs, Ternary is the financial lifeguard.
Lacework: cloud security and compliance tool
Multi-cloud security usually means stitching together AWS GuardDuty, Azure Security Center, and Google’s Chronicle. Until gaps appear and hackers RSVP to your data.
Lacework replaces that patchwork with a unified multi-cloud security platform. It auto-detects misconfigurations (like unencrypted S3 buckets or overly permissive IAM roles) across all three clouds.
It also monitors behavior for anomalies (why is this VM suddenly talking to Belarus?). It tracks vulnerabilities in containers and enforces compliance (HIPAA, SOC 2) with pre-built rules.
The agentless option works without installing software, which is handy when shadow IT has already deployed 200 unsanctioned cloud instances.
Dynatrace: performance-monitoring tool
When your app spans AWS Lambda, Azure SQL, and Google BigQuery, troubleshooting is likely to slow down.
Dynatrace uses AI to address the problem. It auto-maps every service, container, and API call across clouds into a real-time dependency graph.
For example, if checkout times spike, Dynatrace identifies whether the bottleneck is Azure’s database, GCP’s Pub/Sub, or a misconfigured AWS Application Load Balancer (ALB). Then it suggests fixes.