Serverless computing runs application code on demand while the platform handles the underlying server operations. Its defining operational pattern is scale to zero: idle workloads are deprovisioned and started again when demand returns. That makes startup latency, memory use, and deployment artifact size central concerns rather than background details. section 1.2.2
WebAssembly (Wasm) is presented as a strong fit for serverless workloads because its small, lightweight containers can start faster than conventional container deployments. Traditional serverless applications can incur cold starts of roughly 300 milliseconds or longer, depending on networking and initialization. For workloads with inactive periods, improved Wasm cold starts were associated with faster responses: 70% for moderate tasks and 90% for basic tasks in the cited comparison. section 1.2.2 section 1.5
When evaluating Wasm for serverless computing, weigh these practical characteristics:
- Favor it for scale-to-zero services where repeated provisioning makes cold-start time important. section 1.2.2
- Use its low memory overhead and hardware agnosticism where constrained or varied deployment hardware matters, including edge computing. section 1.3
- Expect portability from a binary that is independent of its build platform, while checking whether required language packages support Wasm. section 1.5
- Account for ecosystem limits, especially limited multithreading support and less mature debugging tools than traditional technologies offer. section 1.3 section 1.5
An intermittently used function
Consider an application function that receives requests only after quiet periods. A scale-to-zero platform may have removed its running container, so the next request pays a cold-start cost. Packaging the function as Wasm aims to reduce that restart penalty through a smaller artifact and lower runtime overhead. The benefit is most relevant when inactivity and reprovisioning are frequent, not merely because the function is serverless. section 1.2.2 section 1.3
Table 1.1 Comparing Wasm to traditional technologies across key dimensions
| Capability |
Wasm |
Traditional technologies |
|---|---|---|
| Sandboxed execution |
Built-in, lightweight isolation |
Containers (not as secure), VMs (heavy/slow), or language-specific (Java, .NET) |
| Cold start |
Microseconds |
Containers: seconds; VMs: minutes |
| Artifact size |
KBs to low MBs |
Containers: 100s of MBs; traditional: varies widely |
| Portability |
Compile once, run anywhere |
Docker (platform-specific builds); interpreted languages (performance penalty) |
| Memory footprint |
Minimal overhead |
Containers/VMs: significant overhead |
| Multithreading |
Limited (proposals in progress) |
Well supported |
| Ecosystem maturity |
Growing (many packages incompatible) |
Mature |
| Debugging tools |
Improving but limited |
Well established |
| Runtime performance |
Near-native |
Varies (native to interpreted) |
Compare Wasm with traditional deployment approaches across cold start, artifact size, memory footprint, portability, security, multithreading, debugging, and ecosystem maturity before selecting a serverless runtime. section 1.3
Performance claims should not erase the trade-offs. Wasm can be paired effectively with serverless and edge computing, but some packages may be unavailable, language support differs in maturity, and real-world applications can be constrained by missing multithreading support. Choose it where rapid startup, small deployments, and portability outweigh those current limitations. section 1.3 section 1.5
