AI Coding Without Leaving Your Network — Local LLM Deployment Support

Harness AI coding without exposing your source code to external services — using the latest open-weight models such as Qwen3-Coder, we provide end-to-end support from building a local coding LLM environment to adopting AI coding agents in-house.

Amatsukaze Inc. has launched its local coding LLM deployment support service, enabling organizations to use AI for code generation, review, and test authoring without sending their source code to external services. The service provides end-to-end support — from selecting open-weight coding models and building the inference infrastructure, to connecting AI coding agents such as Claude Code, Cline, and Continue, and optimizing for your own codebase — answering the needs of development organizations that want the productivity of AI coding but cannot let their source code leave their walls.

The Current Landscape: AI Coding Goes Mainstream, Yet Source-Code Confidentiality Looms ​

On the front lines of software development, AI-assisted code completion, generation, and review have become standard tools. AI coding agents that support everything from design and implementation to review and test authoring have substantially raised productivity, and the shift to an "AI-first development process" has now reached a stage where it shapes competitiveness.

At the same time, many cloud-based coding assistants, by their very design, transmit surrounding source code as context to the vendor's model with each completion or generation. That context can include the business logic, algorithms, configuration values, and credentials at the core of a business — and many development organizations are uneasy about a fundamental reality: source code, a critical piece of corporate intellectual property (IP), is processed outside their control. The handling of client assets in contract development and export restrictions in regulated industries only intensify this concern.

Against these challenges, the technology provides a strong tailwind. The Qwen3 series, released by Alibaba in 2025, is largely offered under the Apache 2.0 license, and its code-specialized variant, Qwen3-Coder, supports repository-scale understanding and agentic coding. Alongside it, a steady stream of commercially usable open-weight coding models — the DeepSeek family, Mistral AI's Devstral, OpenAI's gpt-oss, and the GLM family — has arrived, making it possible to generate production-ready code even within your own environment.

Why Local Coding LLMs Now ​

There are three main reasons organizations are choosing to run coding LLMs locally.

• Source code and IP confidentiality: Because completion and generation are completed entirely within your internal network or air-gapped environment, source code is never sent to an external vendor. You can adopt AI coding while keeping the code assets at the core of your business under your own control.
• The usage-based cost of cloud assistants: Agentic coding involves long conversational loops and tends to accumulate token consumption, so as usage grows, cloud billing becomes harder to predict. Adopting a local deployment offers a way to reframe this cost as a fixed cost of your in-house infrastructure.
• The imperative to accelerate in-house development: Shifting to an AI-first development process is no longer an experiment but a management priority. Organizations need an environment that their own development teams can operate and manage independently while limiting external dependencies.

That said, even as open-weight coding models have grown more capable, a new challenge has surfaced: deploying these models in-house requires deep expertise. Selecting the right model for the use case, designing and procuring GPUs, building the inference infrastructure, and reliably connecting to existing development tools and agents all demand specialized knowledge. This is precisely where a knowledgeable partner adds value.

Key Benefits You'll Realize ​

Deploying a local coding LLM environment can deliver the following benefits.

• Faster in-house code generation, review, and test authoring: AI supports day-to-day development work — drafting implementations, assisting with code review, and generating test code — boosting your entire development team's velocity.
• No external transmission of source code: Because code is processed entirely in-house, AI coding can be applied even to highly confidential codebases and to projects with strict export controls or data residency requirements.
• Cost predictability: Because inference cost can be treated as a fixed cost of your in-house infrastructure, you achieve a predictable cost structure that is less affected by usage volume or the length of conversational loops.
• Optimization for your own codebase: By having the AI learn from or reference your coding conventions and domain knowledge, you can draw out code assistance grounded in your own context rather than generic suggestions.

How We Make It Happen ​

Amatsukaze adopts a low-risk "small start" as its core principle, supporting you step-by-step from technical validation through full deployment to integration into your development process.

• Model selection and validation: From open-weight coding models such as Qwen3-Coder, gpt-oss, the DeepSeek series, Devstral, and the GLM series, we select the optimal model balancing target languages, accuracy, and required hardware. We verify the impact in a small environment before moving to full deployment.
• Inference infrastructure setup: Using inference engines such as vLLM, Ollama, and llama.cpp, we build a coding LLM foundation suited to on-premises and air-gapped environments, with the required context length and concurrent usage in mind.
• Connecting AI coding agents: We connect agents such as Claude Code, Cline, Continue, and Aider to a local OpenAI-compatible endpoint, embedding AI coding into your everyday development flow across your IDE, terminal, and CI pipeline.
• RAG and fine-tuning on your own code: Through RAG applied to your internal codebase and design documents, and fine-tuning of small language models (SLMs), we implement lightweight, low-cost code assistance optimized for your own context.
• Evaluation and governance: By designing evaluations (Evals) suited to coding use cases, version-controlling models and prompts, and building a mechanism that integrates generated code into your existing build and review processes, we support safe, reproducible AI use across the whole team.

Why Amatsukaze Is the Right Partner ​

Deploying a local coding LLM is not simply about running a high-performance model; success hinges on whether you can integrate infrastructure, AI, and development practices into a single, cohesive system. Amatsukaze's strength is a structure that lets us work alongside you across all three.

• Robust infrastructure operations expertise: We bring knowledge cultivated through the design of high-load, high-transaction systems and the construction and operation of robust infrastructure. We support everything from GPU resource design to stable multi-user operation — grounded in real operational experience, not abstract theory.
• AI / LLM engineering: We have hands-on capability with the latest open-weight models, RAG, and agentic AI. We embed AI coding agents into your development workflow and translate them into measurable gains across design, implementation, review, and testing.
• End-to-end partnership: We support everything from model selection and validation to the shift to an AI-first development process and the launch of in-house development teams, in a single engagement. Rather than "deploy and done," we embed these practices into your team and ensure they create lasting value.