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🔒Local-first Apps Offer Privacy, Low Latency, and Cost Savings

Local-first apps are the future of privacy and cost efficiency

TL;DR

Local-first applications and small language models (SLMs) are delivering privacy-focused, low-latency alternatives to cloud-hosted LLMs. They reduce API costs and protect PII, making them a viable option for developers.

Local-first applications and small language models (SLMs) are delivering privacy-focused, low-latency alternatives to cloud-hosted LLMs. They reduce API costs and protect PII, making them a viable option for developers. Running SLMs directly on-device avoids high API costs, reduces latency, and protects user data. For example, Mima, a client-side AI project, aggregates social content and provides a local graph of all social connections using native web APIs, ensuring privacy-first and local-first principles. The cost of running inference on a multimodal model is four times higher than necessary, highlighting the efficiency of SLMs.

Local-first Apps Offer Privacy, Low Latency, and Cost Savings — InfoQ

Key Points

1

Local-first apps deliver privacy-focused, low-latency alternatives to cloud-hosted LLMs, reducing API costs and protecting PII.

2

Running SLMs directly on-device avoids high API costs, reduces latency, and protects user data, making them a viable option for developers.

3

Mima, a client-side AI project, aggregates social content and provides a local graph of all social connections using native web APIs, ensuring privacy-first and local-first principles.

4

The cost of running inference on a multimodal model is four times higher than necessary, highlighting the efficiency of SLMs.

5

API costs increase linearly with every call, making SLMs a cost-effective solution for developers.

Why It Matters

If you're building apps that require privacy, low latency, and cost efficiency, local-first applications and SLMs are a game-changer. Running SLMs directly on-device avoids high API costs, reduces latency, and protects user data. For example, Mima, a client-side AI project, aggregates social content and provides a local graph of all social connections using native web APIs, ensuring privacy-first and local-first principles.

local-firstprivacycost-efficiencysmall-language-modelsslm

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