Use open weight models as your AI coding agent with Amazon Bedrock
Pair OpenCode, an open-source terminal-native AI coding agent, with open weight models on Amazon Bedrock to get a secure, flexible, pay-per-use coding assistant. Learn how to configure multi-model workflows, match the right model to each task, and keep your data in your own AWS account with no infrastructure to manage.
AI coding agents have become integral tools for developers to create, debug, and refactor software. Amazon Bedrock now offers practical, cost-effective access to open weight models for these agents. However, most options necessitate sending proprietary data to a third-party API, limiting model choice, or imposing per-seat subscriptions irrespective of usage.
This can be problematic for those with data residency needs, cost-sensitive workloads, or a desire for model flexibility. OpenCode is an open-source, terminal-native AI coding agent built in Go. It can read and edit files, run shell commands, and comprehend project structure via Language Server Protocol (LSP) diagnostics. OpenCode connects to over 75 LLM providers, including Amazon Bedrock.
When paired with open weight models on Bedrock, OpenCode functions as a local coding assistant with secure inference within your AWS account. There's no infrastructure management and no per-seat fees. This post demonstrates setting up OpenCode with open weight models on Bedrock, configuring multi-model workflows, and providing coding examples using Moonshot AI Kimi K3, OpenAI GPT-OSS 120B, and NVIDIA Nemotron 3 Super 120B.
Additionally, it explains how Ethara.AI deploys this architecture in production with multi-agent orchestration for AI engineering and research workflows at scale.
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