Most teams that struggle to manage multi-cloud costs and security are splitting the effort across different tools, owners, and schedules. That is exactly why both keep getting worse.
Why multi-cloud cost and security management is getting harder
Multi-cloud environments do not stay static. New accounts, regions, and workloads are added constantly, often faster than governance frameworks can keep pace.
The deeper problem is that each provider brings its own billing model, security tooling, and policy framework, and none of them interoperate natively. Every expansion adds complexity that existing processes were not built to absorb.
This breakdown shows up the same way in every multi-cloud environment:
- Cost visibility breaks first. Each provider bills differently, so there’s no consistent view of what’s driving spend across environments.
- Recovery readiness goes unverified. Each cloud has its own restore tooling and testing process, so teams can't reliably prove they can recover across the full estate until an incident forces them to try.
- Backup coverage becomes unclear. Native tools operate per cloud, so you can’t see what’s protected or what’s missing across the full estate.
- Backup policy enforcement falls behind. New accounts, regions, and resources are created faster than governance expands.
- Compliance becomes fragmented. Audit logs and reporting are split across providers, making it harder to prove coverage.
In environments running more than a handful of workloads across multiple clouds, the most common problem is a lack of visibility. Nobody has a consolidated view of what's running, what's protected, and what has drifted until the bill arrives or an audit surfaces the gap.
9 ways to manage multi-cloud cost and security
These are the areas where teams consistently lose money while creating security exposure. Fixing one almost always improves the other.
1. Build unified cost visibility before anything else
When you set up native billing dashboards across AWS, GCP, and Azure simultaneously, you hit the same wall every time: the data exists, but it cannot be compared. AWS bills hourly or per-second, depending on the service.
GCP and Azure use different pricing structures, discount models, and instance naming conventions. Without normalizing all of that into one view, anomalies grow undetected across billing cycles, and the same visibility gaps apply to access and coverage.
What useful visibility requires:
- All spend consolidated across AWS, Azure, and GCP in a single interface with normalized pricing models.
- Costs mapped to business context by team, project, workload, or environment.
- Real-time anomaly detection that surfaces unexpected spend before it escalates.
- Forecasting that factors in reserved instance commitments and growth trends alongside current usage.
Tools like nOps, CloudHealth, and Ternary exist to consolidate this data into a single view.
According to the Flexera 2025 State of the Cloud Report, 84% of organizations name cloud spend management as their top challenge for the ninth consecutive year. Most of them know what needs to happen. What they are missing is a consolidated view of their spend that enables them to act.
2. Right-size resources continuously, not once
Right-sizing once solves the problem for about two quarters. Then, as provisioned capacity drifts from actual demand, the bill climbs back up.
Over-provisioning is the single largest source of cloud waste, and it accelerates as environments grow. In a multi-cloud environment, it is harder to catch because each provider uses different instance families and pricing tiers for comparable capabilities. There’s no direct comparison without dedicated tooling.
Use reserved instances for stable workloads (AWS publishes savings of up to 72% vs on-demand), spot or preemptible instances for batch and non-production work, and autoscaling configured to actual demand curves rather than peak estimates. Monthly utilization reviews matter more than annual ones.
For 50% of practitioners, workload optimization and waste reduction are top priorities, reflecting how persistent this problem is even in mature organizations.
One pattern that accelerates right-sizing is assigning spend ownership to the teams generating it. Teams move faster when the cost is reflected directly in their budget.
3. Address cross-cloud egress at the architecture level
Egress charges are consistently the line item that surprises teams most because the data movement was never modeled when the backup architecture was designed.
The fix is to make deliberate decisions about where backup data lives relative to the workloads it protects, before infrastructure is built. The egress drivers are worth modeling up front:
- Initial backup placement. Replicating backups to a different cloud after data has accumulated costs far more than placing them correctly at the start.
- Cross-region replication rules. Teams configure these once for a workload and rarely revisit them; abandoned replication rules drive egress charges long after the original workload is gone.
- Cross-cloud recovery paths. When workloads need to be restored across clouds (recovering an Azure VM into AWS or pulling objects from Google Cloud into another region), egress costs are included in the recovery budget.
Teams that don't model the third one end up choosing between expensive cross-cloud restore and inadequate single-cloud recovery during an incident.
4. Manage backup posture across the whole estate
AWS Backup, Google Cloud's native snapshot tools, and Azure Backup each operate independently.
There's no unified view of what's protected, what's drifting, and what's missing across the full estate. Each tool stores data at hyperscaler storage rates without consistent deduplication or compression.
Posture drift looks the same in every multi-cloud environment we work with:
- A new AWS account spun up for a feature project never gets added to the backup policy. The team that created it doesn't know the policy exists; the team that owns the policy doesn't know the account exists.
- A retention setting changed in one region for compliance reasons doesn't propagate to the other regions running the same workload. The audit clears in the changed region; the rest stays out of compliance.
- A data classification is misapplied during a migration, and a database holding regulated information ends up in a 7-day retention bucket instead of the 90-day one its policy requires.
Each of these is invisible until an incident or an audit forces someone to look.
Cloud Backup Posture Management (CBPM) is the category we created at Eon for what cloud backup should be: continuous discovery, classification, and policy enforcement across AWS, Azure, and Google Cloud, without manual tagging, agents, or per-cloud tooling.
Eon's autonomous CBPM scans accounts and regions on-demand as resources are backed up, flags drift the moment it happens, and produces audit-ready posture reporting at any moment without anyone running a manual check.
Take the retention drift case. A new RDS instance is created in a covered account, but the data classification is misapplied, and the instance lands in the default 7-day retention bucket instead of the 90-day bucket required by the data type.
CBPM detects and classifies the new resource when backup scanning is initiated, classifies the data, and identifies that the applied retention policy doesn't match the classification requirements. It then either auto-corrects the assignment or flags it for review.
At audit time, that same gap would be invisible without a manual review of each covered resource against its required retention period.
The cost result follows the posture work. Replacing native backup tools with a unified, deduplicated approach typically reduces storage costs by 30–50% through global deduplication, compression, and incremental backups.
The posture visibility is what makes cost optimization defensible. Every dollar of backup spend ties to a resource we can prove is protected.
When recovery is needed, granular restoration retrieves individual files, database records, or table-level data without rehydrating full environments.
5. Detect ransomware in backups, including managed databases
Ransomware that encrypts a production database before backup runs creates a worst-case scenario: clean recovery points that no longer exist, and an active backup pipeline writing compromised data to immutable storage.
Most cloud backup tools can't tell the difference because they don't scan the data they're protecting.
The hardest variant of this problem is ransomware in managed databases:
- Amazon RDS
- Amazon Aurora
- Google Cloud SQL
- Azure SQL
There's no filesystem to scan with traditional anti-ransomware tools, so detection has to happen at the data layer. Eon's logical ransomware detection works directly against managed database backups, comparing structural patterns and content changes across recovery points to flag anomalies that match ransomware signatures, without requiring an agent inside the database.
When detection fires, Eon identifies the last known clean recovery point and pairs it with granular restore so teams can recover only the affected records or tables, not the entire database.
That capability is differentiated by design. Most backup vendors can't see inside managed database backups at all.
6. Use infrastructure as code to enforce consistency and catch drift
Manual configuration across three cloud consoles is how environments become inconsistent, insecure, and expensive, often all three at once.
It usually starts with something spun up for a test. It never gets cleaned up. Months later, it’s still running, generating charges, and sitting outside every security policy.
Infrastructure as Code tools like Terraform and Pulumi define the desired state once in version-controlled code and deploy it consistently across providers. No manual changes or configuration gaps between environments.
Configuration drift turns this into a security problem. When infrastructure diverges from its defined state, you lose control over both cost and security.
Policy as Code via Open Policy Agent enforces governance before anything is deployed, rather than applying fixes after problems surface.
7. Tag everything, and enforce it
Untagged resources are invisible in two ways: they don’t appear in cost reports, and they fall outside security and backup policies.
Tagging breaks down quickly. Tags are inconsistent, cleanup doesn’t keep up, and part of the environment ends up unaccounted for. Poor tagging leads to 40% higher waste, while those same resources sit outside security controls.
The fix requires enforcement at the time of resource creation. Use a standard schema across all providers (team owner, project, cost center, data classification) and block resources that don’t meet it.
Chargeback models depend on accurate tags. The same data drives backup policies, security controls, and incident response at scale.
8. Automate compliance monitoring and audit reporting
Backup compliance drift builds between audit cycles. By the time a manual review catches it, it's already been there for months.
The audit-time question that breaks teams: "Show me backup coverage and retention compliance for every workload subject to HIPAA, SOC 2, or GDPR retention requirements across every account and region."
Without continuous posture data, the answer requires manual queries across three different cloud consoles, followed by reconciliation.
Continuous backup compliance is a part of compliance monitoring that Eon owns directly. CBPM does four things continuously across every covered resource:
- Discovers and classifies new resources by data type as they're created.
- Applies the right backup policy for each classification, including the right retention period.
- Surfaces drift instantly when a setting falls out of compliance.
- Produces audit-ready coverage and retention reporting at any moment.
The broader compliance stack (security incident logs, IAM audit trails, network policy enforcement) lives outside the backup layer. But for the question "is every piece of regulated data being protected and retained correctly across our cloud estate," CBPM gives a continuously verified answer.
9. Make backup data useful
Traditional backup systems store data in formats that require a full environment restore before anything can be accessed. Every compliance request, analytics workload, or AI training job that needs historical data becomes either a slow, expensive restore or a duplicative pipeline—neither scales.
Eon's Live Data Lake closes that gap with Zero-ETL access. Backup data is stored in open table formats (Apache Iceberg, Parquet, etc.) and is directly queryable by analytics and AI engines, including Snowflake, Databricks, BigQuery, Redshift, Microsoft Fabric, and Spark.
AI workloads connect through native integrations with Vertex AI, Amazon Bedrock, OpenAI, and MCP, so backup data feeds model training and inference pipelines without a separate ingestion layer.
The cost angle matters too. Teams are already paying to store this data for disaster recovery.
ETL-ing a copy of it into a warehouse for analytics is duplicate spend. Zero-ETL access turns one storage layer into two utility layers without doubling the bill. The backup investment stops being a sunk cost and starts being queryable infrastructure.
Tools that help you manage multi-cloud cost and security
Most teams don’t lack tools. They lack alignment between them.
This is where tools typically fit:
The right stack depends on your environment size, cloud mix, and where visibility breaks down first.
What matters is whether they give you a consistent view of what’s running, what’s protected, and what’s drifting across environments.
Fix the visibility gap before anything else
Start by auditing what’s running and what’s protected. You’ll find resources that generate costs without coverage, and those same resources falling outside security policies. That’s where both problems break down.
If you’re relying on native backup tools per provider, you don’t have a unified view of coverage or drift.
Eon provides that visibility across AWS, Azure, and Google Cloud through CBPM, granular recovery, ransomware detection in backups, and Zero-ETL access to backup data, without agents or changes to production infrastructure.
Get a demo to see how it works in your environment.
Frequently asked questions
What is multi-cloud cost management?
Multi-cloud cost management is the practice of tracking, controlling, and optimizing cloud spending across multiple providers, such as AWS, Azure, and Google Cloud. It combines unified billing visibility, resource right-sizing, and governance policies to reduce waste and align cloud spend with business outcomes.
How do you reduce costs in a multi-cloud environment?
You reduce costs in a multi-cloud environment by right-sizing over-provisioned resources, minimizing unnecessary data egress, replacing siloed native backup tools, and enforcing consistent tagging across all resources. These steps improve visibility and eliminate waste across providers.
What are the biggest security risks in multi-cloud environments?
The biggest security risks in multi-cloud environments are fragmented identity and access management, inconsistent policy enforcement, unmonitored resources, and gaps in backup coverage. These issues leave data exposed and make it harder to detect and respond to threats.
How does backup management affect multi-cloud costs?
Backup management affects multi-cloud costs by increasing storage spend, creating tool sprawl, and limiting visibility when each provider uses separate native backup tools. A unified approach typically reduces storage costs through global deduplication, with savings varying based on data types and change rates, and improves control across environments.
What is cloud backup posture management (CBPM)?
CBPM is the process of continuously scanning and mapping cloud resources to ensure the appropriate backup policy is applied to each one, based on business and compliance requirements. Eon's platform automates CBPM through autonomous scanning, classification, and policy enforcement across AWS, Azure, and Google Cloud.
