Successful businesses continually reassess their operations to find the best way of doing what they do. In some cases, this means returning to technologies they had previously abandoned. For example, at present, many businesses are repatriating workloads from the cloud to other environments. Here is a guide to the three main reasons why.

Concerns about cost

There are many reasons why using the cloud can work out more expensive than using on-prem hardware. Here are 10 of the main ones.

Variable autoscaling costs increase spending. Additional compute resources activate during traffic spikes and generate extra charges for associated services such as load balancers and API gateways. These fees often exceed initial expectations.

Per-request and per-operation fees create cost unpredictability. API calls, serverless functions, and managed service operations accumulate quickly as application usage grows.

Monitoring and observability charges rise as organizations collect more metrics, traces, and logs. Log storage expands rapidly and often becomes one of the largest hidden expenses in cloud deployments.

Data transfer fees add significant cost. Transfers between availability zones or regions incur per-gigabyte charges, and analytics workloads can move terabytes during normal operation. These transfers increase bills by 20 to 30 percent in some environments.

Inter-service dependencies multiply costs because many cloud services charge separately for each layer of an application workflow. Architectures that rely on multiple managed components often generate unexpected charges.

Unpredictable monthly fluctuations create budgeting challenges. Bills change when user behavior, data volume, or application performance shifts, which reduces financial control and complicates forecasting. A workload projected at $10,000 per month may reach $20,000 after variable charges accumulate.

Compliance-related tooling increases cost in regulated industries. Meeting HIPAA, PCI, or GDPR requirements often requires third-party logging, scanning, auditing, and encryption systems that add storage and processing fees. These tools generate ongoing charges for retained logs and security events.

Premium support tiers raise expenses. Mid-market organizations often upgrade support levels to gain timely assistance during incidents because standard tiers provide limited engagement. These upgrades add recurring operational cost.

Stable workloads degrade cloud economics because continuous consumption eliminates the financial benefit of elastic scaling. Applications that run 24/7 often cost less on dedicated or colocated infrastructure.

Data egress fees create large one-time costs when organizations move workloads or datasets out of the cloud. Moving hundreds of terabytes can cost tens of thousands of dollars before any migration work begins.

Security and compliance issues

Although public clouds prioritize security, they still create potential security and compliance headaches. Here are 7 of the main ones.

Confusion over shared responsibility leads businesses to assume cloud providers handle all security. This often leaves gaps in patching, configuration, and access control that attackers can exploit.

Insecure APIs or third-party integrations can expose cloud services to injection attacks, data leaks, or denial-of-service. Regular testing, strict authentication, and rate limiting reduce these threats.

Vulnerabilities in virtual machines or containers can allow attackers to break isolation, access other workloads, or spread malware across the cloud environment. Regular vulnerability scanning mitigates this risk.

Identity and access management failures allow unauthorized privilege escalation. Implementing multi-factor authentication, role-based access, and least-privilege policies limits exposure even if credentials are compromised.

Misconfigured network controls, security groups, and firewalls create open pathways for attackers. Continuous auditing and automated compliance checks help maintain secure network boundaries.

Insufficient logging and monitoring delay breach detection. Continuous monitoring, SIEM systems, and automated alerting allow rapid response to suspicious activity and limit damage.

Data residency and sovereignty laws require businesses to store data in compliant locations. Failing to follow the applicable rules can result in fines and operational restrictions.

The challenge of maximizing performance

It’s easy to scale up resources in a public cloud but it can be much harder to maximize performance. Here are 10 of the main challenges businesses face.

Latency slows application responsiveness, caused by physical distance between users and cloud data centers, network congestion, or inefficient routing. Content delivery networks and edge locations reduce delays.

Bandwidth limitations reduce data transfer speed for large files, streaming, or backups. Choosing higher throughput plans, optimizing compression, and using parallel transfers can improve performance.

Resource contention occurs when multiple tenants share CPU, memory, or storage I/O. Dedicated instances, reserved resources, or autoscaling can prevent inconsistent performance under load.

Network jitter creates variable response times, affecting real-time applications like VoIP, video calls, or trading systems. Traffic shaping, prioritization, and monitoring reduce jitter impact.

Storage type affects read/write speed and latency. HDDs are slower than SSDs, and object storage may have higher access times. Matching storage tier to workload is critical.

Auto-scaling delays can leave applications under-provisioned during traffic spikes, causing slow response or errors. Pre-warming instances and predictive scaling improve readiness.

Load balancing misconfigurations can create hotspots, overloading some servers while underutilizing others. Regular testing and dynamic balancing policies ensure even request distribution.

Database performance suffers from high query volumes, unoptimized indexing, or insufficient memory. In-memory caching, query optimization, and horizontal scaling enhance throughput.

API and microservice response times degrade under load if services are unbalanced. Implementing caching, rate limiting, and service replication maintains consistent performance.

Maintenance tasks like snapshots, patching, or backups can temporarily consume CPU or I/O resources. Scheduling these during off-peak hours minimizes impact on applications.