---
title: "Discussion on \"Best LLM for Coding: Cloud vs Local\" | Hashnode"
description: "Discussion on \"Best LLM for Coding: Cloud vs Local\". In recent months, the landscape of Large Language Models (LLMs) has been transformed by groundbreaking developments in LLMs like GPT-4, Gemini, and PaLM v2. These innovations have not only revolutionized the industry but have also set a new standard ..."
url: "https://hashnode.com/posts/best-llm-for-coding-cloud-vs-local/65c15c66f535a405c143cd07"
---

[Feed](https://hashnode.com/feed) /

Discussion

# [Best LLM for Coding: Cloud vs Local](https://pieces.hashnode.dev/best-llm-for-coding-cloud-vs-local?utm_source=hashnode&utm_medium=feed)

In recent months, the landscape of Large Language Models (LLMs) has been transformed by groundbreaking developments in LLMs like GPT-4, Gemini, and PaLM v2. These innovations have not only revolutionized the industry but have also set a new standard ...

Feb 5, 2024 · 10 min read · pieces.hashnode.dev  
Read the article

## Written by

[Pieces for Developers](https://hashnode.com/@pieces)

@pieces

  
[View profile](https://hashnode.com/@pieces)

Long Term Memory For Your Whole Developer Workstream

Pieces is your AI companion that captures live context from browsers to IDEs and collaboration tools, manages snippets and supports multiple LLMs - all while processing data locally for maximum control.

## Responses 1

Markdown supported · ⌘↵ to post

[dougame841114](https://hashnode.com/@dougame)

[Sep 21](https://hashnode.com/posts/best-llm-for-coding-cloud-vs-local/65c15c66f535a405c143cd07/comment/6ab0ab4dbb09a8259357af88)

Great comparison! The local vs cloud debate is one of the most common questions we see.

Our take: it depends on what you're doing.

**Local models are great for:**

-   Privacy-sensitive work
-   Offline development
-   Quick tests and prototyping
-   Simple tasks that don't need frontier intelligence

**Cloud models are better for:**

-   Production work that needs reliability
-   Hard reasoning and complex coding
-   Access to the latest frontier models
-   Teams that don't want to manage GPU infrastructure

For most developers, the sweet spot is a mix. Use local models for routine work, and cloud API for hard tasks.

We use JZS Token as our cloud API gateway. One OpenAI-compatible endpoint, 40+ models. Switching between models is just changing the model name. We keep local models for offline work, and use the gateway for everything else.

The cost savings from mixing and matching are significant. You don't have to pay for the most expensive model for every single task.

Nice breakdown. This should help a lot of developers decide what's right for them.