Engineering
Why we build edge AI-first (and you should too)
Edge AI-first means AI runs close to the data, close to the user and close to the cost. Here is why it matters for SMBs.
By Renish Mithani6 min read
Edge AI-first means AI runs close to the data, close to the user and close to the cost. It is the architecture we use for every WebGrow24 product.
What edge AI-first means
Three principles:
- **Close to the data.** Models run on the edge, on the device or in the same region as the data. No round-trip to a US data centre for an Indian user.
- **Close to the user.** Inference happens at the edge, not in a centralised GPU cluster. Latency is in milliseconds, not seconds.
- **Close to the cost.** Edge inference is cheaper. We pass the savings to our customers.
Why it matters for SMBs
Most SMBs cannot afford a centralised AI stack. Edge AI-first changes the economics: you can ship an AI feature without a heavy monthly GPU bill.
How we do it
- We use small, distilled models on the edge for routine tasks (classification, embeddings).
- We use larger models in the region for tasks that need more reasoning.
- We use the largest models only when the task demands it, with caching and batching.
The trade-offs
Edge AI-first is not free. You give up some model quality and you have to think about deployment. For SMBs, the trade-off is worth it.
Keep reading
Related posts.
Questions
Frequently asked questions
Common questions about why we build edge ai-first (and you should too), answered plainly.
Models run on the device, on the edge or in the same region as the data. No round-trip to a US data centre for an Indian user.
Edge inference is cheaper per token. We pass the savings to customers and use the larger models only for tasks that demand them.
Smaller models are less capable than the largest ones. We pick the model to the task: distilled for routine work, larger for reasoning.
Yes, when the task needs them. We use them through regional endpoints with caching and batching, not as the default for everything.
Yes, when you have a clear use case and a sensible volume. The economics change once you go from a few hundred to a few million inferences.
On the discovery call. We pick the smallest piece of AI that is worth shipping and grow it from there, with the cost and the latency target written down.
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