The problem
Cloud-first AI is too expensive for mass adoption in emerging markets. GPU inference costs remain high, connectivity is inconsistent, and mobile data is too expensive for many users.
AI-for-All is building edge-first inference infrastructure that routes each query to the cheapest capable layer. The result is lower cloud spend, lower latency, stronger privacy, and access for the next billion AI users in data-constrained markets.
Cloud-first AI is too expensive for mass adoption in emerging markets. GPU inference costs remain high, connectivity is inconsistent, and mobile data is too expensive for many users.
AI-for-All uses a 70 / 20 / 10 routing model: most queries stay on-device, a smaller set uses optimized cloud inference, and only the most complex requests touch full LLMs.