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

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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. 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.

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?

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.

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 . AI deploys this architecture in production with multi-agent orchestration to power AI engineering and research workflows at scale.


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