Building smarter, scalable AI applications

While GenAI has been driving transformative change, WebAssembly (WASM) works in the background to enhance performance and security in web and cross-platform applications. Although WASM and GenAI are distinct in their respective capabilities, combining them unlocks valuable possibilities that can benefit your business, as part of your GenAI adoption. 

WASM can deliver near-native performance in constrained environments, which perfectly complements the typically computation-heavy demands of GenAI models. Together, WASM and GenAI create a powerful solution for deploying and scaling AI-driven applications with greater efficiency and flexibility for your business across diverse use cases. 

In this article, we unpack how GenAI and WASM intersect, discuss why your organisation should consider investing, and share strategies to future-proof your AI infrastructure with a strong focus on security and compliance.

Why WASM belongs in your AI infrastructure 

WASM has evolved into a high-performance runtime, enabling efficient execution across diverse computing environments, from edge devices to serverless infrastructures. Its core strengths include:

• Near-native performance even on constrained or distributed devices
• True portability, enabling “build once, run anywhere” across browsers, servers, and edge nodes
• Built-in sandboxed security, ideal for zero-trust environments and multi-tenant systems

These attributes position WASM as a compelling solution for modern application development, offering performance, flexibility, and security across a wide range of deployment scenarios.

While GenAI platforms grow more fragmented with increased costs, WASM enables faster launches, efficient scaling, and reduced cloud dependency without the need to re-architect. GenAI workloads are typically resource-heavy, so WASM’s lightweight execution model makes it possible to run them securely and efficiently across a wide range of environments.

We’re already seeing this shift in action with the rise of Vercel-like WASM-native platforms. This shift means that developers are gaining autonomy, deploying high-performance applications without being locked into heavyweight platforms. 

At the same time, new models like o1 and Deepseek-R1 are expanding GenAI capabilities, such as generating code, content, and insights at scale. 

WASM is the lightweight, future-proof runtime that makes these innovations more accessible and manageable. 

Overcoming GenAI deployment challenges 

Traditional GenAI deployments often struggle to scale in real-time, particularly on lower-end devices like entry-level laptops or smartphones. These performance limitations create bottlenecks as demand grows, driving up infrastructure costs, causing inconsistent user experiences, and limiting GenAI’s reach in mobile or offline-first environments.

Tools like Ollama have shown that running models locally is possible, but performance drops significantly outside of high-spec systems. 

This is where WASM comes in. Integrating WASM into your AI infrastructure enables teams to optimise GenAI workloads across a broader range of devices without the burden of expensive infrastructure. 

However, its impact depends on how well it’s aligned with your specific use case within your business. Key factors to consider:

• Model size and complexity: WASM is ideal for lightweight GenAI tasks like text summarisation or transcription. Larger models may need quantisation or pruning to fit within WASM’s current memory limits. Tools like WebLLM demonstrate how browser-based LLMs can work with WebGPU support.
• Performance and acceleration: WASM offers near-native speeds but lacks direct GPU support, which limits compute-heavy GenAI workloads. WebGPU is starting to close this gap for in-browser inference.
• Portability: WASM’s platform-neutral design lets you “write once, run anywhere” across browsers and server environments, simplifying your deployment pipeline.
• Scalability: Combining WASM with serverless platforms reduces cold start times and improves resource efficiency, making it easier to scale GenAI apps.
• Security: WASM’s sandboxing enhances runtime isolation, crucial for secure AI execution, especially in multi-tenant or zero-trust setups.
• Privacy: Local-first deployment through WASM keeps sensitive data on-device, reducing reliance on external servers and enhancing user trust.

To fully take advantage of pairing WASM and GenAI, your team must navigate a few current technical limitations, such as access to specialised hardware, like GPUs and the availability of AI-ready development tools. A practical way to address this limitation is by using modern tools like Turso. 

Turso is a lightweight, distributed database designed to run efficiently on a wide range of devices, even without high-end infrastructure. Turso is well-suited for AI workloads because it can quickly search and retrieve information using built-in vector search and compress large datasets to run smoothly on local machines. This means:

• Faster responses for your AI applications
• Lower cloud dependency, which can reduce costs
• Better data control, helping with privacy and compliance

Incorporating a tool like Turso into your AI stack makes it easier to run GenAI apps locally, securely, and at scale, without needing to overhaul your entire infrastructure.

Strengthening AI security and compliance with WASM 

Adhering to regulations like GDPR is not an option, so as your organisational needs grow and your teams' use of AI models grows, safeguarding sensitive data, confidential information, and proprietary business data needs to be a top priority. It’s not just a box to tick, but a core part of building trustworthiness and future-ready systems. When AI models are deployed using WASM, it offers meaningful security and compliance benefits for organisations deploying GenAI, particularly in highly regulated industries like finance and healthcare. 

By executing code in a sandboxed environment, WASM isolates applications from the host system and other modules. This separation significantly reduces the risk of unauthorised access, helping to contain potential vulnerabilities and safeguard sensitive data.

Local data processing is another major advantage. WASM allows AI workloads to run directly on user devices or within defined geographic boundaries, supporting data sovereignty requirements. This approach limits exposure to cross-border data transfer risks and simplifies compliance with regulations like the GDPR.

In healthcare, for instance, WASM can be used to run AI models locally, ensuring patient information such as electronic Protected Health Information (ePHI) stays on-device. Similarly, financial institutions can handle sensitive customer data without sending it to external servers, lowering the risk of breaches and maintaining compliance with industry-specific standards.

For senior technology leaders, the opportunity is clear. Start by assessing which AI workloads are best suited to local execution. Integrate WASM into your development workflows to take advantage of its built-in security architecture. And finally, stay ahead of regulatory changes to ensure ongoing compliance. Taken together, these steps will help future-proof your AI investments, strengthen your security posture, and give you greater control over how data is processed and protected.

Real-world use cases and business impact

WASM and GenAI are already converging in ways that evoke a more efficient and decentralised future for AI. We’re seeing real-world applications like Whisper running directly in the browser, which is proof that you can deploy GenAI models without relying on heavyweight infrastructure. 

This hybrid model gives users more control, letting them select the model and fine-tune performance based on local constraints, which means faster performance, lower costs, and more flexibility in where and how you run AI.

Here’s a glimpse at what’s emerging:

• In-browser AI assistants: Deploy lightweight GenAI models directly in browsers using WASM, providing real-time assistance without network latency.
• Edge device applications: Run WASM-optimised GenAI models on IoT devices for tasks like image recognition or anomaly detection.
• Serverless AI APIs: Host GenAI models as WASM modules on serverless platforms, reducing operational costs while improving scalability.

These use cases demonstrate how WASM empowers GenAI to operate efficiently in diverse environments, from cloud servers to smaller edge devices.

When should companies invest in WASM for GenAI?

• To enhance security and compliance: Industries such as finance and healthcare handle sensitive data that requires stringent security measures. WASM’s sandboxed execution environment ensures that AI models run in isolation, reducing the risk of unauthorised access. Moreover, processing data locally on devices rather than in the cloud can help meet data sovereignty requirements, ensuring compliance with regulations like GDPR.
• To optimise performance and scalability: WASM allows AI models to run efficiently across various platforms, from browsers to edge devices. This capability is particularly beneficial for applications requiring real-time processing with minimal latency. For instance, deploying lightweight GenAI models directly in browsers using WASM can provide real-time assistance without network latency, enhancing user experience.
• Reducing operational costs: By leveraging WASM’s lightweight nature, organisations can run AI models locally, reducing the need for extensive cloud infrastructure. This approach not only cuts down on operational costs but also minimises dependency on external servers, leading to more efficient resource utilisation.

How to leverage WASM for AI at scale 

Looking ahead, advancements in both WASM and GenAI promise to deepen their integration through the following ways:

• WebGPU Support: Introducing WebGPU to WASM environments will enable hardware acceleration for AI workloads. However, it's worth noting that WebGPU is still an experimental API, and its integration with WASM for AI workloads is in the early stages, requiring further development and testing.
• Model Optimisation: Techniques like quantisation and pruning will make it easier to deploy GenAI models within WASM’s constraints.
• Standardised Toolchains: Improved tooling will simplify the process of compiling and deploying GenAI models as WASM modules.
• Private Personal Assistants: Running the model locally will give users greater control over their data, reducing the need for constant data sharing, which is something that becomes inevitable as AI adoption grows.

These developments are set to enable real-time, AI-driven applications that are fast, portable, and accessible to a wide range of users.

Building smarter and deploying faster

WASM and GenAI are two disruptive technologies poised to change how we build and deploy software. WASM’s portability and performance make it an ideal runtime for GenAI, enabling the creation of applications that are both powerful and accessible.

As these technologies continue to evolve, now is the time for developers to explore their synergy. Whether you’re building the next-gen AI assistant, an innovative edge computing solution, or serverless applications, the pairing of WASM and GenAI offers endless possibilities to be unlocked.

Reach out to our team of experts to see how you can bring these technologies to life.