How Encryption Affects Cloud Storage Speed

Encryption is key to securing cloud storage but can slow performance. It adds processing steps for encrypting and decrypting data, increasing CPU usage, memory consumption, and latency. For UK businesses, balancing security, speed, and costs is critical, especially under GDPR regulations. Here's what you need to know:

• Impact on Performance: Encryption can raise CPU usage by 15–35%, memory by 10%, and add milliseconds of delay per operation.
• Types of Encryption:
  • At Rest: Minimal impact due to hardware acceleration.
  • In Transit: Affects CPU during connection setups.
  • Client-Side: Heaviest burden on endpoints.
• Mitigation: Use hardware-accelerated encryption, selective encryption for sensitive data, and optimise TLS connections.

Tracking metrics like latency, throughput, and resource usage can help identify and address slowdowns. By using cloud-native tools like AWS KMS or upgrading to hardware-accelerated instances, businesses can reduce encryption overhead while staying compliant and cost-efficient. Balancing encryption scope and performance is the key to effective cloud storage management.

How Can You Minimize Data Encryption's Performance Impact? - Talking Tech Trends

How Encryption Affects Cloud Storage Performance

Encryption impacts cloud storage performance in three key ways: it increases latency (the time it takes to access data), reduces throughput (the amount of data transferred per second), and raises resource consumption, particularly for CPU and memory. Every read or write operation requires extra processing - encrypting data before storage or decrypting it during retrieval - which adds an additional step to the process [1].

For businesses in the UK, especially those operating high-speed connections between London data centres, these effects can become quite noticeable. On multi-gigabit links, the CPU often becomes the limiting factor before the network bandwidth is fully utilised. This is because encryption tasks consume significant CPU resources, meaning that even with fast infrastructure, the system’s ability to encrypt and decrypt data may determine overall performance.

Different Encryption Types and Their Impact on Performance

Not all encryption methods affect performance in the same way. The three primary types - encryption at rest, encryption in transit, and client-side encryption - each introduce different levels of overhead.

Encryption at rest occurs at the storage layer, where the cloud provider encrypts data as it’s written to disks or volumes. This method typically adds a small, consistent delay because providers often use hardware acceleration to optimise this process [1][3]. For UK SaaS applications, provider-managed encryption generally results in low-millisecond write latency, with the performance impact remaining predictable due to the provider’s streamlined infrastructure.

Encryption in transit secures data as it moves across networks, using protocols like TLS for HTTPS or SMB over TLS. This mainly increases CPU usage during connection setup and for per-packet processing [3]. The effect is more pronounced in workloads with frequent, short-lived connections. For example, a microservices architecture in the UK using mutual TLS for communication between services may experience higher CPU usage and slower connection setup times when services make repeated, brief calls.

Client-side encryption has the most significant impact, as all encryption and decryption tasks are handled by the client or application before data reaches the cloud [2][4]. This shifts the resource burden entirely to the endpoint, preventing the cloud provider from optimising or offloading encryption tasks. For instance, a desktop backup solution that encrypts data on the client side before uploading it may experience extended backup windows when dealing with large datasets. If the endpoint lacks sufficient processing power or hardware acceleration, the CPU quickly becomes a bottleneck, slowing down upload and download speeds.

The choice of encryption algorithm also plays a critical role. Symmetric encryption methods like AES are much faster and more scalable than asymmetric algorithms like RSA, especially for large data volumes and high-throughput environments [2][4]. Cloud providers typically use symmetric encryption for bulk data, reserving asymmetric methods for tasks like key exchange and identity verification, striking a balance between security and performance.

Performance Metrics to Monitor

To identify where encryption might be slowing things down, tracking specific performance metrics is essential. Four key indicators can highlight the impact of encryption on workloads: latency percentiles, IOPS, throughput, and resource usage.

• Latency percentiles (such as p95 and p99) help pinpoint whether encryption is causing delays, especially under heavy loads [1][3]. For example, switching to HTTPS or enabling more complex encryption settings may cause noticeable spikes in tail latency, even if median latency remains stable. These spikes indicate that encryption is adding time to requests rather than network variability being the culprit.

• IOPS (input/output operations per second) and throughput (measured in MB/s) reveal whether systems can maintain their required operation rates and data transfer volumes when encryption is enabled. Benchmarks show that encryption can add 10–50 milliseconds of overhead per disk I/O request, while network-level encryption typically adds 0.5–2 milliseconds per kilobyte transferred [1]. These delays can quickly add up in I/O-heavy workloads like databases or virtual machines.

• CPU and memory usage should be closely monitored on both clients and servers. This can help identify whether upgrading hardware or switching to instances with hardware acceleration could improve performance. For example, modern CPUs with dedicated encryption instruction sets can significantly reduce the processing time for algorithms like AES, cutting encryption overhead dramatically [1][3][4]. Benchmarks comparing general-purpose CPUs to hardware-accelerated solutions show a fivefold improvement in processing times for AES encryption - 1 millisecond per 1 MB of data versus 0.2 milliseconds when using specialised hardware [1].

For UK organisations using older hardware or legacy virtual machine types, missing out on these hardware accelerations can make encryption overhead much more noticeable. Upgrading to newer instance families or storage options can yield immediate performance improvements.

Performance Impact Across Storage Types

Encryption affects various storage types - object storage, block storage, and file storage - differently, as each handles data in unique ways and supports different workloads.

• Object storage integrates much of the encryption-at-rest overhead into the managed service. This results in minor additional latency per operation, often overshadowed by network latency, particularly for geographically distributed users [1][3]. For workloads involving numerous small requests, like those in analytics or media processing, enabling server-side encryption may reduce throughput slightly, particularly when CPU resources are the limiting factor. However, for UK organisations handling large media files or batch jobs, the performance impact is generally minimal since network and backend processing dominate overall timing.

• Block storage is more sensitive to encryption overhead, especially for I/O-intensive workloads like databases or virtual machines. Encrypting data at the volume layer increases CPU usage and introduces small per-I/O delays [1][3]. These delays can compound under high IOPS, potentially affecting service-level agreements for applications that require low latency, such as transactional databases or real-time analytics. Ensuring sufficient CPU headroom and choosing high-performance storage classes can help mitigate this.

• File storage, such as NFS or SMB, experiences overhead both at the storage layer and during transit when secure protocols are used. This is particularly noticeable for workloads involving numerous small file operations, such as shared departmental file servers [3]. UK businesses migrating on-premises file servers to encrypted cloud storage have reported longer wait times when accessing large files, like CAD designs or video assets, over office networks.

The impact of encryption also depends on file size, data type, and access patterns. Large media files and workloads with heavy write or random I/O operations tend to experience more noticeable slowdowns [1][2]. Tests have shown that enabling encryption universally can reduce data transfer speeds by 20–30%, whereas selectively encrypting only sensitive data can limit performance losses to 5–10% [1]. By classifying data and applying encryption only where necessary, organisations can minimise performance penalties while still meeting compliance requirements.

To better understand the effects of encryption, teams can run benchmarks comparing encrypted and unencrypted configurations using realistic workloads. For UK businesses, testing scenarios like nightly backups, batch analytics, and daytime interactive use can reveal how encryption impacts performance during peak and off-peak hours. Incorporating these benchmarks into regular performance testing can help identify and address encryption-related slowdowns when adjusting settings, cipher suites, or infrastructure.

How to Reduce Encryption Overhead

Reducing encryption overhead is all about striking the right balance between maintaining security and optimising performance. By fine-tuning existing tools, leveraging cloud-specific capabilities, and rigorously testing configurations, organisations can protect sensitive data without unnecessary costs or slowdowns.

Using Cloud-Native Encryption Features

Cloud providers have made significant strides in improving encryption efficiency, and their built-in tools are often the easiest way to enhance performance. A key factor here is hardware acceleration. Modern CPUs are equipped with features like AES-NI, which can handle encryption tasks much faster than traditional software-based methods. For example, enabling hardware offload for AES encryption can reduce processing time by as much as 80% [1].

To take advantage of this, it’s important to ensure that the chosen instance types or storage classes support hardware acceleration. Older virtual machines or legacy storage systems might lack this capability, forcing workloads to rely on slower software encryption. Upgrading to newer instance families that support hardware-accelerated cryptography can deliver noticeable improvements without requiring changes to application code.

Additionally, managed key services - such as AWS KMS, Azure Key Vault, and Google Cloud KMS - help streamline encryption processes. These services centralise key management, optimise key retrieval, and integrate seamlessly with cloud storage systems. This means encryption and decryption happen close to the data, minimising latency. For most scenarios, enabling default encryption-at-rest with these services adds minimal overhead while simplifying compliance and auditing.

A practical approach is using server-side encryption with managed keys and controlling access through IAM policies. For highly sensitive data, such as financial records or personal information, customer-managed keys (CMEK) can be selectively applied to specific storage buckets. This keeps performance impacts low for most workloads while meeting stricter security requirements for critical data.

Once cloud-native optimisations are in place, further refinements can be made at the application level.

Techniques to Optimise Encryption Performance

At the application level, several strategies can help reduce the impact of encryption on performance:

• Batch I/O operations and reuse TLS connections: Grouping small read or write requests into larger batches spreads encryption costs over fewer operations. Similarly, connection pooling and protocols like HTTP/2 or HTTP/3 allow multiple requests to share a single encrypted channel, reducing the overhead of repeated handshakes.

• Eliminate redundant encryption layers: Applying encryption at every level - client-side, application, database, and storage - can waste resources without adding meaningful security. A better approach is to map data sensitivity against regulatory requirements and use only the necessary encryption layers.

• Selective encryption: Rather than encrypting everything, focus on sensitive data such as payment details or National Insurance numbers. Encrypting only critical fields can reduce performance overhead significantly. For instance, encrypting all data might lower throughput by 20–30%, but selectively encrypting sensitive data reduces this impact to around 5–10% [1].

For high-throughput tasks like backups or data ingestion, scheduling encryption-heavy jobs during off-peak hours and adjusting parallelism can prevent CPU bottlenecks. Combining streaming uploads with compression also helps maintain smoother data flow. In cases where security policies allow, caching decrypted data in memory can reduce the need for repeated decryption, though these caches must be carefully managed to meet compliance standards.

Testing and Benchmarking Encryption Performance

The only way to truly understand how encryption impacts performance is through careful measurement. Synthetic load tests can compare workloads with and without encryption to establish a clear baseline. Metrics like throughput, latency, and response times reveal how encryption affects performance under different conditions, such as nightly backups or real-time user interactions.

A/B testing in staging environments is another useful approach. By running one environment with encryption enabled and another without, teams can pinpoint where slowdowns occur. Adding CPU and memory profiling to these tests helps identify whether encryption is consuming excessive resources and whether hardware upgrades or instance type changes might help.

To prevent performance issues from reaching production, encryption performance tests can be integrated into CI/CD pipelines. Dedicated benchmarking stages can catch regressions early, blocking deployments if encryption-related metrics degrade. Key metrics to track include CPU usage on encryption-heavy services, storage I/O latency, network throughput on encrypted channels, and cost per encrypted gigabyte.

Setting alert thresholds for metrics like CPU saturation or encryption-related costs ensures that issues are detected before they impact service-level agreements. For DevOps teams, this means continuously monitoring encryption performance and ensuring that new keys, libraries, or algorithms don’t introduce unacceptable delays. By doing so, organisations can maintain a strong balance between security and performance.

Balancing Security, Performance, and Costs

Encryption isn't without its trade-offs. Every time data is encrypted or decrypted, it consumes CPU power, uses memory, and can require extra storage or network operations. These technical demands inevitably translate into higher operational costs.

For organisations in the UK, the challenge is particularly pressing. They must ensure encryption is robust enough to meet UK GDPR and industry-specific regulations, maintain smooth performance for users, and keep operations cost-efficient to remain competitive. Striking this balance means understanding how encryption affects cloud costs, leveraging DevOps practices to optimise configurations, and knowing when to bring in external expertise.

How Encryption Affects Cloud Costs

Encryption can significantly increase cloud expenses. One of the most immediate impacts comes from higher CPU usage. Depending on the workload and whether hardware acceleration is available, enabling encryption can push CPU usage up by 15–35% [1]. For systems already running close to capacity, this can mean upgrading to larger instances or triggering more frequent autoscaling, both of which inflate monthly costs.

Storage and networking are other areas where encryption adds to the bill. Encrypted data often involves extra metadata reads or key rotation processes, which increase storage requests. Since many cloud providers charge per request and per gigabyte stored, frequent access to encrypted data can quickly become expensive. Similarly, encrypting data in transit adds processing delays - about 0.5–2 milliseconds per kilobyte [1].

Key management introduces further costs. Services like AWS KMS, Azure Key Vault, and Google Cloud KMS charge monthly fees per active key, along with per-operation fees for encryption, decryption, and key rotation. If your application relies heavily on these services for every operation, costs can escalate quickly. Estimating these expenses by analysing usage patterns and pricing is essential for managing these fees effectively.

To keep encryption costs under control while maintaining security, organisations can implement a tiered strategy. Provider-managed encryption is usually sufficient for most data and has minimal performance impact. For sensitive datasets - like financial records or National Insurance numbers - customer-managed keys (CMEK) and tighter controls can be used. Additional measures such as batching operations, caching decrypted data where compliant, and moving older encrypted data to cheaper storage tiers can also help reduce costs.

The financial benefits of these strategies are clear. Hokstad Consulting, for instance, helped a SaaS company save £120,000 annually by optimising cloud usage, resizing instances, automating resource allocation, and refining encryption settings [5]. These savings highlight the value of integrating encryption cost management into broader DevOps workflows.

Integrating Encryption Optimisation into DevOps

To ensure cost savings don’t come at the expense of performance, encryption optimisation should be embedded within DevOps practices. DevOps provides a framework for standardising encryption settings, enforcing policies, and balancing security, speed, and cost across an organisation’s infrastructure.

One way to achieve this is by using Infrastructure as Code (IaC) templates. These templates can automatically apply the correct encryption mode, key type, and rotation policies to new storage buckets, databases, and file systems. This ensures consistency and reduces the risk of misconfigurations.

Automated testing and policy-as-code checks within CI/CD pipelines can further enhance security and cost management. For example, a policy might enforce AES-256 encryption with managed keys for all customer data storage. Any non-compliant configuration would be flagged and blocked before reaching production. This proactive approach minimises security risks and prevents unnecessary operational expenses.

Performance benchmarking is another crucial step. By testing encrypted workloads before major changes, teams can measure the impact on latency, throughput, and CPU usage. Presenting this data during deployment reviews helps teams make informed decisions about instance sizing, encryption scope, and architectural design.

Ongoing monitoring and feedback loops are just as important. Metrics like request latency, CPU usage, and detailed billing data for storage and key management services can help identify performance or cost issues early. If encryption starts to strain resources or inflate costs, alternative configurations can be tested in staging environments and rolled out using blue–green or canary deployments.

Aligning encryption standards with organisation-wide security policies ensures consistency and compliance. Policies specifying approved algorithms, key lengths, and rotation intervals can be enforced automatically through DevOps pipelines. For UK organisations, this alignment is particularly critical for meeting regulatory requirements while maintaining efficient, secure, and user-friendly services. When internal resources fall short, external expertise can fill the gaps.

How Consulting Services Can Help

Optimising encryption can be complex, especially for organisations operating in multi-cloud or hybrid environments. When rising cloud costs or increasing regulatory demands start to slow progress, consulting services can offer valuable support.

Specialist consultancies can identify inefficiencies in encryption practices, such as overuse, misconfigurations, or unnecessary CPU and key management overhead. Hokstad Consulting, for example, works with UK organisations to design encryption strategies that integrate seamlessly into DevOps workflows and align with broader cloud cost management goals [5]. Their services include cloud cost audits, DevOps transformations, and automation solutions tailored to encryption needs [5].

Hokstad Consulting helps companies optimise their DevOps, cloud infrastructure, and hosting costs without sacrificing reliability or speed. Our proven optimisation strategies reduce your cloud spending by 30–50% while improving performance through right-sizing, automation, and smart resource allocation. [5]

By combining technical analysis with financial insights, consultants like Hokstad help organisations measure the trade-offs between security, performance, and cost. They use controlled experiments to gather data on CPU usage, latency, and storage expenses, presenting results in pounds sterling to aid decision-making. For companies without in-house expertise - or those needing quick results - consultants can implement best practices, automate processes, and train internal teams to maintain these improvements over time. One e-commerce business reported a 50% performance boost alongside a 30% cost reduction after working with Hokstad Consulting [5].

Whether or not to engage external expertise depends on the complexity of the challenges, the scale of the organisation’s operations, and the available internal skills. When encryption begins to noticeably affect cloud costs or service quality, bringing in experienced consultants can provide the analysis and solutions needed to balance security, performance, and cost effectively.

Conclusion

Encryption plays a crucial role in safeguarding sensitive data in the cloud. However, it comes with trade-offs - using more CPU power, adding latency, and increasing storage expenses. For UK organisations, the challenge lies in balancing GDPR compliance, performance, and cost-effectiveness.

Fortunately, encryption overhead doesn't have to mean compromising on speed or inflating costs. Modern symmetric algorithms, like AES, especially when paired with hardware acceleration, help keep these impacts manageable. Additionally, encryption features provided by major cloud providers are built to operate efficiently at scale, often with minimal disruption to typical workloads. Think of encryption not just as a security measure but as a variable you can optimise for performance and cost.

Key Points to Keep in Mind

Here’s a summary of practical strategies and metrics to guide your encryption efforts:

• Monitor key performance metrics: Keep an eye on CPU usage, request latency, throughput, and disk I/O performance before and after enabling encryption. Be alert to signs like latency increases of several milliseconds, CPU loads rising by 15–30%, or unexpected spikes in cloud bills. Investigate these promptly to avoid larger issues.

• Leverage cloud-native encryption and hardware acceleration: Provider-managed encryption at rest is typically fine-tuned for performance and requires minimal setup. Hardware features like AES-enabled CPUs, hardware security modules (HSMs), SmartNICs, and TLS offloaders can reduce encryption processing times by up to 70%, freeing up resources for other tasks. Reserve customer-managed encryption for highly sensitive data requiring stricter controls.

• Apply encryption strategically: Focus your strongest controls on highly sensitive data, such as financial records, National Insurance numbers, or health information. For less critical workloads, lighter encryption can suffice.

• Optimise TLS for network encryption: Reduce handshake overhead by enabling session reuse, persistent connections, and connection pooling. This is especially useful in microservices architectures where frequent API calls are common.

• Integrate encryption into DevOps workflows: Use Infrastructure as Code to standardise encryption settings across environments. Include performance testing in your CI/CD pipelines to catch potential issues early. Automated policy checks can ensure new resources are encrypted correctly from the start.

Next Steps for Your Business

To refine your encryption strategy, start with a thorough audit of your current setup. Identify which datasets are encrypted at rest and in transit, map where encryption and decryption occur in your workflows, and document any gaps in coverage or compliance. Run benchmarks to compare performance with and without encryption, using realistic traffic volumes. Track metrics like CPU usage, latency, throughput, and costs in pounds sterling to evaluate trade-offs.

From there, implement the techniques outlined above. Enable provider-managed encryption for most data, upgrade to hardware-supported instances where necessary, and refactor applications to avoid redundant encryption processes. Schedule resource-intensive encryption tasks, like backups or archiving, during off-peak hours to minimise user impact.

Set a regular review schedule to keep your encryption strategy aligned with changing workloads, new cloud features, and evolving regulations. Plan annual reviews or revisit strategies after major system updates. For UK organisations, ensure your approach meets local compliance standards and budget considerations.

For complex environments or high-traffic systems, consider working with specialists. Hokstad Consulting, for example, helps UK businesses optimise encryption strategies while integrating them into DevOps workflows and managing cloud costs. Their tailored solutions have delivered results like a 50% performance boost and a 30% cost reduction for an e-commerce client [5].

Balancing security, performance, and cost is an ongoing process. By tracking the right metrics, using modern tools, and embedding encryption into your DevOps practices, you can protect sensitive data effectively without sacrificing speed or overspending.

FAQs