Why Edge AI Needs Lightweight Semantic Caches — and What Makes Them Hard to Build
Originally published on Medium.com on November 6, 2025.
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Today edge computing is reshaping the way AI systems are deployed. Instead of sending every request to centralized cloud infrastructure, more computation is happening on devices closer to end-users. These “edge environments” include IoT gateways, on-premise servers, mobile devices, micro-VMs, serverless functions, and browser-based applications. The appeal is clear: moving computation closer to where data is generated reduces latency, minimizes bandwidth requirements and allows organizations to satisfy strict data-privacy rules.
At the same time, WebAssembly (WASM) has emerged as a portable, sandboxed runtime for executing code in highly constrained or security-sensitive environments. Originally designed for browsers, WASM now runs in cloud edge workers, serverless platforms, and isolated environments where traditional binaries cannot be executed. These runtimes often restrict access to system calls such as networking, threading, or the local filesystem. They operate under strict memory limits, sometimes as low as tens of megabytes, and they prioritize deterministic, predictable execution.
Altogether, while offering obvious advantages, running AI components at the edge introduces its own challenges, especially when applications rely on semantic search, embeddings, or large language models (LLM).
A major issue arises when AI applications repeatedly generate similar responses to similar prompts. In a cloud setting this inefficiency is tolerable, but at the edge it becomes costly. Edge nodes often have hard limits on CPU time and memory allocation, meaning that even small local language models may struggle to meet real-time latency budgets. A semantic cache — a system that stores answers together with an embedding vector and returns a cached answer when the incoming request is semantically similar — is a natural solution. However, building such a system for constrained environments is significantly more difficult than building one for the cloud.
The first challenge is memory. Classical vector databases and similarity search engines rely on complex indexing structures such as HNSW graphs, which are fast but memory-intensive. Standard configurations easily grow to hundreds of megabytes and often assume the availability of multi-threading, background maintenance processes, and dynamic memory growth. Edge workers and WASM isolates cannot accommodate this. In many cases, the runtime enforces strict caps on linear memory and disallows growing beyond a fixed boundary. This immediately rules out most existing semantic search libraries, even before considering cold-start overhead or storage.
The second constraint is the execution environment itself. WASM runtimes typically do not expose POSIX-like APIs (Portable Operating System Interface, a family of standards developed by IEEE that define consistent application programming interfaces). Features such as mmap, file descriptors, or native sockets are unavailable unless the host explicitly provides them through WASI (WebAssembly System Interface), and even then, support varies. This makes it almost impossible to run vector databases “as-is,” because they depend heavily on operating system functionality and persistent background services. In edge environments developers have only a few milliseconds to initialize modules, produce a response, and return control to the runtime. A semantic cache that takes hundreds of milliseconds to load an index simply cannot be deployed in these contexts.
Cold-start behavior is another architectural concern. Unlike long-running cloud servers, edge workers may be rapidly created and destroyed. A new isolate might handle only one or two requests before being recycled. For AI applications, this means that any semantic cache must load extremely quickly — ideally in a few milliseconds — and must not rely on heavy initialization or dynamic graph reconstruction. Snapshotting becomes essential: developers need the ability to store the cache state in a compact format that loads deterministically and quickly into memory.
Then, there is also the question of energy and cost efficiency. Edge nodes operate on limited power budgets, especially in IoT scenarios. Recomputing the same embedding or calling an external LLM repeatedly wastes both energy and bandwidth. Reducing redundant inference calls requires a semantic memory layer that can match incoming queries to existing knowledge without exceeding stringent resource constraints.
Privacy regulations add an additional layer of complexity. One of the motivations for moving AI workloads to the edge is to keep sensitive data local. But to do that effectively, the system must avoid unnecessarily sending repeated questions or logs to a central model. A semantic cache therefore becomes not just a performance optimization but a privacy mechanism: if the system can answer from its local memory, no data transmission to external LLMs is required. Unfortunately, building such a cache in environments with restricted storage, no access to background processes, and strict runtime quotas is a non-trivial task.
These are the conditions under which traditional semantic search infrastructure begins to struggle. Large vector databases simply assume too much: too much RAM, too much access to the operating system, too much startup time, and too much persistence. Even lightweight semantic caches designed for server applications often rely on threading, shared memory, file-based checkpointing, or dynamically growing allocations. Most embedding-based caches were never designed with WASM runtimes, edge workers, or IoT gateways in mind.
This is precisely the gap that newer designs aim to address. Solutions like VCAL approach semantic caching not as a distributed system or standalone service but as a small in-process library that can run with minimal memory and without heavy OS dependencies. Instead of behaving like a database, it behaves more like a CPU-level cache for AI reasoning, storing question-answer pairs and their embeddings in an optimized structure that can fit within the constraints of edge and WASM environments. By avoiding reliance on network calls, background threads, or large indices, such systems become suitable for serverless workers, browser WASM modules, or embedded devices with limited RAM.
In this sense, the semantic cache becomes a missing piece of infrastructure for edge AI. As more organizations push inference closer to the user, the need for a lightweight, deterministic, low-memory semantic lookup system grows. The limitations of edge platforms — from strict memory caps to rapid cold starts — make this a difficult problem, and the lack of suitable solutions has slowed the adoption of AI features outside centralized cloud environments. As WASM matures and edge utilities evolve, semantic caching may become a standard part of the AI pipeline, enabling faster, cheaper, and more privacy-preserving deployments across a wide range of devices.