Use open weight models as your AI coding agent with Amazon Bedrock

AI coding agents have become a core part of how developers write, debug, and refactor software. Open weight models on Amazon Bedrock now make these agents practical to run privately and cost-effectively.

What happened

But most options require you to send your proprietary data to a third-party API, lock you into a single model provider, or charge per-seat subscriptions regardless of how much you use them. If you have data residency requirements, cost-sensitive workloads, or a need for model flexibility, these constraints create real friction.

Where the sources line up

What if you could run an AI coding agent that keeps your data in your own AWS account, switches between frontier open weight models on demand, and charges only for what you consume?

Practical impact for readers

OpenCode is an open source, terminal-native AI coding agent built in Go. It reads and edits files, runs shell commands, and understands project structure through Language Server Protocol (LSP) diagnostics. It connects to over 75 large language model (LLM) providers including Amazon Bedrock . When you pair OpenCode with open weight models on Bedrock, you get a coding assistant that runs locally while inference happens securely within your AWS account. There’s no infrastructure to manage and no per-seat fees.

Who should pay attention now

In this post, we show you how to set up OpenCode with open weight models on Amazon Bedrock, configure multi-model workflows that match the right model to each task, and walk through practical coding examples using Moonshot AI Kimi K3 , OpenAI GPT-OSS 120B , and NVIDIA Nemotron 3 Super 120B . We also share how Ethara. AI deploys this architecture in production with multi-agent orchestration to power AI engineering and research workflows at scale.

What is still unclear

The industry is shifting toward open weight models. According to McKinsey’s Open-source technology in the age of AI report (2025) , 76 percent of organizations expect to increase open source AI usage, and leading AI adopters are 40 percent more likely to use open weight models. For coding workloads, five factors drive this shift:.

Source notes

AWS ML Blog