For most organizations, public cloud infrastructure is treated as a stable, consistent utility. This Tremhost Labs report challenges that assumption through a six-month longitudinal study designed to measure and quantify the real-world performance variability of compute, storage, and networking on the three leading cloud platforms.

Our findings reveal that even on identically provisioned virtual machine instances, performance is far from static. Over the study period (January-June 2025), we observed significant performance fluctuations, with 99th percentile network latency spiking to over 5x the average, and storage IOPS periodically dropping by as much as 40% below their provisioned targets.

The key takeaway for technical decision-makers is that “on-demand” infrastructure does not mean “on-demand identical performance.” The inherent nature of these multi-tenant environments guarantees a degree of variability. For production systems, architecting for this instability with robust, application-level resilience and monitoring is not optional—it is a fundamental requirement for building reliable services.

Background

As of mid-2025, businesses rely on public cloud providers for everything from simple websites to mission-critical applications. However, the abstractions of the cloud can mask the complex, shared physical hardware that underpins it. This study aims to pull back the curtain on that abstraction.

By continuously measuring performance over a long period, we can move beyond simple “snapshot” benchmarks. This provides a more realistic picture of the performance an application will actually experience over its lifecycle. This is particularly critical for businesses in regions like Zimbabwe, where application performance and user experience can be highly sensitive to underlying infrastructure stability and network jitter.

Methodology

This study was designed to be objective and reproducible, tracking key performance indicators over an extended period.

• Study Duration: January 1, 2025 – June 30, 2025.
• Test Subjects: A standard, general-purpose virtual machine instance was provisioned from each of the three major cloud providers (AWS, Azure, GCP) in their respective South Africa data center regions.
  • Instance Class: 4 vCPU, 16 GB RAM, 256 GB General Purpose SSD Storage.
• Test Platform & Control: A Tremhost server located in Harare, Zimbabwe, acted as the central control node. It initiated all tests and collected the telemetry, providing a consistent, real-world measurement point for network performance from a key regional business hub.
• Automated Benchmarks:
  1. Network Latency & Jitter: Every 15 minutes, a script ran a series of 100 ping requests to each cloud instance to measure round-trip time (RTT) and its standard deviation (jitter).
  2. Storage I/O Performance: Twice daily (once at peak and once off-peak), a standardized fio benchmark was executed on each instance’s SSD volume to measure random read/write IOPS.
  3. CPU Consistency: Once daily, the sysbench CPU benchmark ran for 5 minutes to detect any significant deviations in computational speed, which could indicate resource contention (CPU steal).

Results

The six months of data revealed significant variability, particularly in networking and storage.

Network Latency (Harare to South Africa)

While the average latency was stable, the outlier events were significant.

The crucial finding is that for all providers, the 99th percentile latency—the “worst case” 1% of the time—was 5 to 6 times higher than the average.

Storage I/O Performance

The benchmark measured the performance of a general-purpose SSD volume provisioned for a target of 3000 IOPS.

The data showed that while the average performance was close to the advertised target, all three providers exhibited periods where actual IOPS dropped significantly. These dips typically lasted for several minutes and often occurred during peak business hours in the cloud region.

CPU Performance

CPU performance was the most stable metric. Across all providers, the daily sysbench score varied by less than 2%, indicating that CPU “noisy neighbor” effects, while technically present, were not a significant source of performance variation for this class of instance.

Analysis: The “Why” Behind the Variability

The observed fluctuations are not bugs; they are inherent properties of large-scale, multi-tenant cloud environments.

• The “Noisy Neighbor” Effect: This is the primary cause of I/O variability. Your virtual machine’s SSD shares a physical backplane and controller with other customers’ VMs. If a “neighbor” on the same physical host initiates a massive, I/O-intensive operation, it can create contention and temporarily reduce the resources available to your instance. This is the root cause of the periodic IOPS drops.
• Network Path Dynamics: The internet is not a single, static wire. The path between Harare and Johannesburg can be re-routed by ISPs or within the cloud provider’s own backbone to handle congestion or link failures. These re-routes can cause transient latency spikes. The p99 spikes observed are a direct measurement of this real-world network behavior.
• Throttling and Burst Credits: Cloud providers manage storage performance with credit-based bursting systems. While your instance may be provisioned for 3000 IOPS, this often comes with a “burst balance.” If your application has a period of very high I/O, it can exhaust its credits, at which point the provider will throttle its performance down to a lower, baseline level until the credits replenish.

Actionable Insights & Architectural Implications

1. Architect for the P99, Not the Average: Do not design your systems based on average latency or IOPS figures. Your application’s stability is determined by how it handles the “worst case” scenarios. Implement aggressive timeouts, automatic retries with exponential backoff, and circuit breakers in your application code to survive these inevitable performance dips.
2. Application-Level Monitoring is Essential: Your cloud provider’s dashboard will show that their service is “healthy.” It will not show you the 120ms latency spike that caused your user’s transaction to fail. The only way to see what your application is truly experiencing is to implement your own detailed, application-level performance monitoring.
3. Embrace Resilient, Frugal Design: For businesses where performance directly impacts revenue, this study underscores the need for resilient architecture. This means building systems that can degrade gracefully. For example, if a database connection is slow, can the application serve cached or partial content instead of failing completely? This approach to “frugal resilience”—anticipating and mitigating inherent cloud instability—is a hallmark of mature cloud engineering.

As the industry prepares for the quantum computing era, the migration to Post-Quantum Cryptography (PQC) is moving from a theoretical exercise to an active engineering challenge. For architects and engineers, the most pressing question is: what is the real-world performance cost of layering PQC into TLS, the protocol that secures the web?

This Tremhost Labs report quantifies that overhead. Our benchmarks show the primary impact is concentrated on the initial connection handshake. Implementing a hybrid PQC-classical key exchange in TLS 1.3 increases handshake latency by approximately 66% (an additional 50ms) and server CPU load per handshake by ~25%. Crucially, the size of the initial client request packet grows by over 700%.

The key takeaway is that while the cost of PQC is tangible, it is not prohibitive. The performance penalty is a one-time “tax” on new connections. Bulk data transfer speeds remain unaffected. For decision-makers, this means the key to a successful PQC migration lies in architectural choices that minimize new connection setups and intelligently manage server capacity.

Background

With the standardization of PQC algorithms like CRYSTALS-Kyber by NIST in 2024, the security foundations for the next generation of TLS are in place.¹ The most widely recommended strategy for migration is a hybrid approach, where a classical key exchange (like ECC) is run alongside a PQC key exchange. This ensures connections are protected against both classical and future quantum attacks.

However, this hybrid approach effectively doubles the cryptographic work required to establish a secure channel. This report provides objective, reproducible data on the real-world cost of that additional work in a production-like environment, with a particular focus on factors like network latency that are critical for users in Southern Africa.

Methodology

This benchmark was designed to be transparent and reproducible, reflecting a realistic scenario for a business operating in the CAT timezone.

• Test Environment:
  • Server: A Tremhost virtual server (4 vCPU, 16 GB RAM) located in our Johannesburg, South Africa data center, running Nginx compiled with a PQC-enabled crypto library.
  • Client: A Tremhost virtual server located in Harare, Zimbabwe, simulating a realistic user network path to the nearest major cloud hub.
• Software Stack:
  • TLS Protocol: TLS 1.3
  • Cryptographic Library: A recent build of OpenSSL integrated with liboqs, the library from the Open Quantum Safe project, to provide PQC cipher suites.²

     

• TLS Configurations Tested:
  1. Baseline (Classical): Key exchange performed using Elliptic Curve Cryptography (X25519). This is the modern, high-performance standard.
  2. Hybrid (PQC + Classical): Key exchange performed using a combination of X25519 and Kyber-768, a NIST-standardized PQC algorithm.³

      

• Key Metrics Measured:
  • Handshake Latency: The average time from the initial client request (ClientHello) to the establishment of a secure connection (Time to First Byte).
  • Handshake Size: The size of the initial ClientHello packet sent from the client to the server.
  • Server CPU Load: The percentage of CPU utilized to handle a sustained load of 1,000 new TLS handshakes per second.
  • Bulk Transfer Throughput: The transfer speed of a 100MB file after the secure connection has been established.

Results

The data clearly isolates the performance overhead to the connection setup phase.

Analysis

The results paint a clear picture of where the PQC performance cost lies.

• Latency and Packet Size are Directly Linked: The most significant impact on user experience is the +50ms added to the connection time. This is almost entirely explained by the massive 733% increase in the size of the initial ClientHello packet. PQC public keys are significantly larger than their ECC counterparts.⁴ Sending this much larger packet from Harare to Johannesburg requires more network round-trips to establish the connection, making existing network latency a much bigger factor.

   

• CPU Cost is a Capacity Planning Issue: The ~25% increase in server-side CPU load per handshake is a direct result of the server performing two key exchanges instead of one. For sites that handle a high volume of new, concurrent connections, this is a critical capacity planning metric. It means a server’s ability to accept new connections is reduced by roughly a quarter, which may necessitate scaling up the web-facing server fleet.
• Bulk Transfer is Unaffected: This is a crucial finding. The negligible change in throughput for a large file transfer demonstrates that PQC’s cost is paid upfront in the handshake. The subsequent data encryption uses fast symmetric ciphers (like AES), which are not affected by the PQC transition. Your application’s data transfer speeds will not get slower.

Actionable Insights & Regional Context

1. Architect for Connection Re-use: Since the performance penalty is almost entirely at connection setup, the most effective mitigation strategy is to reduce the number of new handshakes. Implementing and optimizing TLS session resumption and using long-lived HTTP/2 or HTTP/3 connections are no longer just performance tweaks; they are essential architectural choices in the PQC era.
2. Factor CPU Headroom into Budgets: The increased CPU load per handshake is real and must be factored into infrastructure budgets. For high-traffic services, expect to provision approximately 20-25% more CPU capacity on your TLS-terminating tier (e.g., load balancers, web servers) to handle the same rate of new connections.
3. The Mobile and Regional Impact: For users in Zimbabwe and across Southern Africa, who often access the internet over mobile networks with higher latency, the +50ms handshake penalty will be more noticeable. Developers should double down on front-end optimizations to compensate, such as reducing the number of third-party domains on a page (each requiring its own TLS handshake) to improve initial page load times.

In conclusion, the migration to post-quantum security is not free. It introduces a measurable performance tax on connection initiation. However, this tax is both understandable and manageable. By architecting for connection efficiency and planning for increased server load, businesses can transition to a quantum-resistant future without significantly compromising the user experience.

As of 2025, Next-Generation Firewalls (NGFWs) are a non-negotiable component of enterprise security, providing essential Layer 7 threat prevention. However, a critical question for architects is how these complex, stateful devices perform under the brute-force pressure of a volumetric Distributed Denial of Service (DDoS) attack. This Tremhost Labs report investigates this scenario through a controlled stress test.

Our findings are conclusive: under a sustained 10 Gbps DDoS attack, the throughput of legitimate traffic on a mid-range enterprise NGFW dropped by over 85%, while latency for user requests increased by more than 1,500%. The device’s CPU quickly hit 99% utilization, causing its advanced threat inspection capabilities to become unstable.

The key insight for decision-makers is that while NGFWs are vital for inspecting traffic, they are not designed to be a frontline defense against volumetric DDoS attacks. In fact, their own stateful architecture becomes a bottleneck and a point of failure. The only viable strategy is a layered defense where the NGFW is shielded by a dedicated, upstream DDoS mitigation service.

Background

The modern security landscape presents two distinct challenges: sophisticated, application-layer attacks (like SQL injection or malware) and massive, brute-force volumetric attacks (like UDP floods). NGFWs were designed primarily to solve the first challenge through deep packet inspection (DPI), intrusion prevention systems (IPS), and application awareness.

This report tests the inherent conflict between that deep inspection design and the overwhelming nature of a volumetric DDoS attack. For businesses in developing digital economies like Zimbabwe, where a single on-premise firewall often represents a significant security investment, understanding its breaking point is critical for ensuring business continuity.

Methodology

This benchmark was conducted in a controlled lab environment within Tremhost’s infrastructure to simulate a realistic attack scenario.

• Test Subject: A leading mid-range enterprise NGFW virtual appliance, configured with 8 vCPUs and 32 GB of RAM. The appliance is rated by its vendor for up to 10 Gbps of threat prevention throughput. All advanced features (Application ID, IPS, Anti-Malware) were enabled, mirroring a typical production deployment.
• Test Environment: A virtualized network with three components:
  1. An Attack Traffic Generator simulating a 10 Gbps UDP flood, a common DDoS vector.
  2. A Legitimate Traffic Generator simulating 10,000 users accessing web applications and APIs behind the firewall.
  3. A Protected Server Farm hosting the applications.
• Test Scenarios:
  1. Baseline Scenario: Only legitimate traffic was sent through the NGFW to measure its maximum “goodput” and baseline latency.
  2. DDoS Scenario: The legitimate traffic ran concurrently with the sustained 10 Gbps DDoS attack.
• Key Metrics:
  • Goodput: The volume of legitimate traffic successfully transiting the firewall.
  • NGFW CPU Load: The CPU utilization of the firewall appliance itself.
  • HTTP Latency: The round-trip time for a legitimate user to get a response from a web server.
  • Feature Stability: Whether Layer 7 inspection features remained operational during the test.

Results

The performance degradation of the NGFW under the DDoS attack was immediate and severe.

Analysis

The data reveals why NGFWs are fundamentally unsuited to be a primary DDoS defense. The core issue is stateful exhaustion.

An NGFW is a stateful device, meaning it allocates a small amount of memory and CPU to track every single connection that passes through it. A volumetric DDoS attack, which consists of millions of packets per second from thousands of spoofed IP addresses, overwhelms this capability. The firewall’s state table fills up instantly, and its CPU usage skyrockets as it futilely tries to process a flood of meaningless packets.

As the CPU hits 99%, the device has no remaining cycles to perform its valuable, processor-intensive tasks—like deep packet inspection and signature matching—on the legitimate traffic. Consequently, goodput collapses, latency for real users skyrockets, and the advanced security features themselves begin to fail. The NGFW, in its attempt to inspect everything, becomes the very bottleneck that brings the network down. It is trying to apply a complex, surgical tool to a problem that requires a simple, massive shield.

Actionable Insights & Architectural Recommendations

1. Define the NGFW’s Role Correctly: An NGFW is an application-layer guardian, not a volumetric shield. Its purpose is to sit in the path of clean, legitimate traffic and inspect it for sophisticated threats. It should be the last line of defense for your servers, not the first line of defense for your network edge.
2. Implement a Layered, Defense-in-Depth Architecture: The only effective strategy is to place a dedicated DDoS mitigation solution in front of your NGFW.
   • Layer 1: Cloud/Upstream Scrubbing: Employ a cloud-based DDoS mitigation service (e.g., from providers like Cloudflare, Akamai, or your upstream transit provider). These services have massive global networks designed to absorb and filter volumetric attacks before they ever reach your infrastructure.
   • Layer 2: On-Premise NGFW: Your firewall sits behind this scrubbing service. It receives a clean feed of pre-filtered traffic and can dedicate its resources to its core competency: finding and blocking advanced threats.
3. Regional Context for Zimbabwe: For businesses operating with critical but finite international bandwidth, this layered approach is even more vital. Relying solely on an on-premise firewall to stop a DDoS attack means the attack traffic will saturate your internet links long before the firewall itself fails. Using a cloud scrubbing service with Points of Presence (PoPs) in South Africa or Europe moves the fight off your infrastructure and preserves your local bandwidth for legitimate business operations.

In conclusion, this Tremhost Labs report confirms a critical architectural principle: Do not ask your NGFW to do a job it was not designed for. Protect your investment in advanced threat prevention by ensuring it is never exposed directly to the brute force of a volumetric DDoS attack.

For modern applications, the choice of a managed cloud database is one of the most critical architectural decisions, impacting performance, cost, and scalability. As of July 2025, the offerings from the three major cloud providers—Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP)—are mature and highly competitive. This report provides an objective, reproducible benchmark to cut through the marketing and provide hard data on their relative performance.

This Tremhost Labs benchmark reveals that while all three providers deliver robust performance, distinct leaders emerge depending on the workload. AWS RDS for PostgreSQL demonstrated the highest raw throughput for complex, compute-intensive queries. Google Cloud SQL for PostgreSQL consistently delivered the lowest network latency for simple transactions. Azure SQL Database for PostgreSQL proved to be a strong all-rounder, offering balanced performance across all tests.

The key takeaway for technical decision-makers is that there is no single “best” database. The optimal choice is highly dependent on your specific application’s performance profile—whether it prioritizes transactional speed, complex query processing, or a balance of both.

Background

As businesses in Southern Africa, including Zimbabwe, increasingly build applications for a global audience, selecting the right foundational database platform is critical. The choice impacts user experience through latency and application responsiveness, and it directly affects the bottom line through operational costs. This study aims to provide a clear performance baseline for these three leading managed database services, using PostgreSQL as a common denominator for a true apples-to-apples comparison.

Methodology

To ensure our findings are credible, transparent, and reproducible, we adhered to a strict testing methodology.

• Databases Tested:
  • AWS: Relational Database Service (RDS) for PostgreSQL
  • Azure: Azure Database for PostgreSQL – Flexible Server¹

     

  • GCP: Cloud SQL for PostgreSQL
• Database Configuration: To ensure a fair comparison, we provisioned a similar mid-tier, general-purpose instance on each cloud:
  • Instance Class: 4 vCPU, 16 GB RAM, 256 GB SSD Storage
  • Engine: PostgreSQL 16.2
  • Region: All database instances were provisioned in the providers’ respective data center regions in South Africa (Cape Town or Johannesburg).
• Test Client: The benchmark client was a Tremhost virtual server located in our Johannesburg data center. This setup provides a realistic test scenario for a business operating in Zimbabwe, measuring real-world latency to the nearest major cloud region.
• Benchmarking Tool: We used pgbench, the standard and universally available benchmarking tool for PostgreSQL, to ensure results are easily verifiable.²

   

• Workloads Tested:
  1. Transactional (OLTP): A 15-minute pgbench test simulating a high volume of simple read/write transactions (8 clients, 2 threads). The primary metric is Transactions Per Second (TPS).
  2. Compute-Intensive (OLAP-like): A pgbench test using a custom script with complex queries involving multiple joins, aggregations, and sorting on a larger dataset. The primary metric is average query execution time in milliseconds (ms).
  3. Network Latency: A simple SELECT 1 query executed 10,000 times to measure the average round-trip time from our Johannesburg client. The primary metric is average latency in milliseconds (ms).

Results

The data was collected after multiple runs to ensure consistency, with the following averaged results.

1. Transactional Performance (TPS)

This test measures how many simple read/write transactions the database can handle per second. Higher is better.

2. Compute-Intensive Query Performance

This test measures the time taken to execute complex analytical queries. Lower is better.

3. Network Latency

This test measures the round-trip time for a minimal query. Lower is better.

Analysis

The results clearly indicate different strengths for each provider’s offering.

• GCP Cloud SQL excels in latency-sensitive, transactional workloads.³ It won decisively in both the TPS and pure network latency tests. This suggests that Google’s network infrastructure and I/O stack are highly optimized for rapid, small-scale transactions. For applications like e-commerce backends, mobile app APIs, or anything requiring a snappy user experience, this low latency is a significant advantage.

   

• AWS RDS leads in raw compute power. In the test involving complex queries, aggregations, and sorting, RDS outperformed the others by a notable margin. This indicates that its combination of underlying EC2 compute instances (including their Graviton processors) and storage subsystem is exceptionally well-suited for data warehousing, business intelligence, and other OLAP-style workloads where raw processing power is the primary bottleneck.
• Azure SQL offers balanced, competitive performance. While it did not win any single category outright in this test, Azure’s results were consistently strong and highly competitive. It trailed the leader in each category by only a small margin, positioning it as a robust, all-around performer. For organizations already invested in the Microsoft ecosystem, this strong, balanced performance makes it a compelling and reliable choice.

Actionable Insights & Regional Context

For decision-makers in Zimbabwe and across Southern Africa, this data leads to the following recommendations:

1. For Latency-Critical Applications: If your primary concern is providing the fastest possible response time for user-facing applications (e.g., e-commerce, fintech), the consistently low latency of GCP Cloud SQL makes it a top contender.
2. For Data-Intensive, Analytical Workloads: If your application involves complex data processing, reporting, or analytics, the superior throughput of AWS RDS on compute-bound tasks suggests it is better suited to handle the load.
3. For Balanced, Enterprise Workloads: Azure SQL provides a “no-regrets” option with solid performance across the board. Its deep integration with other Azure services and enterprise tools can often be the deciding factor for businesses invested in that ecosystem.

Ultimately, this Tremhost Labs report demonstrates that the best cloud database is not a one-size-fits-all answer. We recommend using this data as a starting point and conducting a proof-of-concept with your own specific application code to validate which platform best meets your unique performance and business needs.

Short Summary:

WebAssembly (Wasm) has matured from a browser-centric technology into a legitimate server-side runtime, promising portable, secure, and sandboxed execution. For technical decision-makers, the primary question remains: What is the real-world performance cost in 2025?

This Tremhost Labs report provides a reproducible performance analysis of Wasm versus native-compiled code for common server-side workloads. Our research finds that for compute-bound tasks, the performance overhead of running code in a top-tier Wasm runtime is now consistently between 1.2x and 1.8x that of native execution. While native code remains the undisputed leader for absolute performance, the gap has narrowed sufficiently to make Wasm a viable and often strategic choice for production systems where its security and portability benefits outweigh the modest performance trade-off.

Background

The promise of a universal binary format that can run securely across any architecture is a long-held industry goal. WebAssembly is the leading contender to fulfill this promise, enabling developers to compile languages like Rust, C++, and Go into a single portable .wasm file.

As of mid-2025, the conversation has shifted from theoretical potential to practical implementation in serverless platforms, plugin systems, and edge computing. This report moves beyond simple “hello world” examples to quantify the performance characteristics of Wasm in a production-like server environment, providing architects and senior engineers with the data needed to make informed decisions.

Methodology

Reproducibility and transparency are the core principles of this study.

• Test Environment: All benchmarks were executed on a standard Tremhost virtual server instance configured with:
  • CPU: 4 vCPUs based on AMD EPYC (3rd Gen)
  • RAM: 16 GB DDR4
  • OS: Ubuntu 24.04 LTS
• Codebase: We used the Rust programming language (version 1.80.0) for its strong performance and mature support for both native and Wasm compilation targets (x86_64-unknown-linux-gnu and wasm32-wasi). The same codebase was used for both targets to ensure a fair comparison.
• Wasm Runtime: We utilized Wasmtime version 19.0, a leading production-ready runtime known for its advanced compiler optimizations and support for the latest Wasm standards, including WASI (WebAssembly System Interface) for server-side I/O.
• Benchmarks:
  1. SHA-256 Hashing: A CPU-intensive cryptographic task, representing common authentication and data integrity workloads. We hashed a 100 MB in-memory buffer 10 times.
  2. Fannkuch-Redux: A classic CPU-bound benchmark that heavily tests algorithmic efficiency and compiler optimization (n=11).
  3. Image Processing: A memory-intensive task involving resizing and applying a grayscale filter to a 4K resolution image, testing memory access patterns and allocation performance.

Each benchmark was run 20 times, and the average execution time was recorded.

Results

The data reveals a consistent and measurable overhead for Wasm execution.

Analysis

The results show that WebAssembly’s performance penalty is not monolithic; it varies based on the workload.

The 1.25x overhead in the SHA-256 benchmark is particularly impressive. This task is pure, straight-line computation with minimal memory allocation, allowing Wasmtime’s JIT compiler to generate highly optimized machine code that approaches native speed. The overhead here is primarily the cost of the initial compilation and the safety checks inherent to the Wasm sandbox.

The higher 1.76x overhead in Fannkuch-Redux reflects the cost of Wasm’s safety model in more complex algorithmic code with intricate loops and array manipulations. Every memory access in Wasm must go through bounds checking to enforce the sandbox, which introduces overhead that is more pronounced in memory-access-heavy algorithms compared to the linear hashing task.

The 1.66x overhead in the image processing task highlights the cost of memory management and system calls through the WASI layer. While Wasm now has efficient support for bulk memory operations, the continuous allocation and access of large memory blocks still incur a higher cost than in a native environment where the program has direct, unfettered access to system memory.

Actionable Insights for Decision-Makers

Based on this data, we can provide the following strategic guidance:

• Wasm is Production-Ready for Performance-Tolerant Applications: A 1.2x to 1.8x overhead is acceptable for a vast number of server-side applications, such as serverless functions, microservices, or data processing tasks where the primary bottleneck is I/O, not raw CPU speed.
• Prioritize Wasm for Secure Multi-tenancy and Plugins: The primary value of Wasm is its security sandbox. If you are building a platform that needs to run untrusted third-party code (e.g., a plugin system, a function-as-a-service platform), the performance cost is a small and worthwhile price to pay for the robust security isolation it provides.
• Native Code Remains King for Core Performance Loops: For applications where every nanosecond counts—such as high-frequency trading, core database engine loops, or real-time video encoding—native code remains the optimal choice. The Wasm sandbox, by its very nature, introduces a layer of abstraction that will always have some cost.
• The Future is Bright: The performance gap between Wasm and native continues to shrink with each new version of runtimes like Wasmtime. Ongoing improvements in compiler technology, the stabilization of standards like SIMD (Single Instruction, Multiple Data), and better garbage collection support will further reduce this overhead. Decision-makers should view today’s performance as a baseline, with the expectation of future gains.