Private AI Development Just Got More Practical: Opencode and Claude Code Now Work Inside Zylon

Opencode and Claude Code are now available inside Zylon

Developers can now configure both Opencode and Claude Code to work with Zylon.

Opencode is an interactive CLI coding assistant that runs directly in the terminal and helps developers build, debug, and maintain software with AI support. Zylon’s documentation now includes a setup guide for connecting Opencode to a Zylon instance using a Gateway Token and environment variables such as ZYLON_BASE_URL, ZYLON_API_TOKEN, and ZYLON_MODEL. (docs.zylon.ai)

Claude Code can also be configured to connect to Zylon. Instead of pointing the CLI directly to Anthropic’s default endpoint, developers can configure the relevant environment variables so Claude Code connects to the Zylon gateway using ANTHROPIC_BASE_URL, ANTHROPIC_AUTH_TOKEN, and ANTHROPIC_MODEL. (docs.zylon.ai)

That may sound like a small integration detail, but it changes the way enterprises can think about AI-assisted development. Developer teams can keep using the coding agent interfaces they already like, while organizations route requests through their own private AI infrastructure.

The question is no longer whether developers should use AI coding tools. In most technical teams, that shift is already happening. The more relevant question is whether companies can offer those tools in a way that protects internal code, avoids unmanaged AI usage, and keeps consumption predictable.

With Zylon, the answer becomes much more practical.

The privacy problem with AI coding assistants

Coding assistants are powerful because they sit close to the work.

They can read files, inspect project structure, explain unfamiliar modules, help reason about bugs, generate tests, and suggest changes across a codebase. That proximity is exactly what makes them useful, but it is also what makes them risky.

In many companies, source code is one of the most sensitive assets in the organization. It contains business logic, infrastructure assumptions, API contracts, security patterns, and sometimes secrets that should never have been committed in the first place. Even when secrets are not present, the codebase itself can reveal how the company operates.

For regulated industries, the risk expands further. Financial services, healthcare, government, defense, and critical infrastructure teams often work under strict requirements around data residency, auditability, vendor access, and third-party processing. In that context, sending development context to an external cloud AI provider may not be acceptable, even if the tool itself is useful.

This is where private AI becomes essential.

Zylon is built as an on-premise private AI platform for regulated industries. Its platform is designed to run inside enterprise infrastructure, including private cloud, on-premise servers, and air-gapped environments, so data does not need to be sent to external servers. (zylon.ai)

For developer teams, that means AI-assisted coding can happen closer to where the code already lives: inside the company’s own environment, governed by the organization, and connected to the tools developers already use.

The quota and token problem is becoming impossible to ignore

Privacy is only one side of the story. The other is usage.

AI coding agents are much more token-intensive than occasional chatbot prompts. A developer may ask an agent to inspect a codebase, understand a bug, edit multiple files, run tests, interpret errors, revise the approach, and repeat the process several times. Each step consumes context, and long sessions can quickly become expensive or run into usage limits.

This is why quotas have become such a visible pain point in AI development. A coding agent can be genuinely useful and still hit a hard ceiling at the exact moment a developer is deep in flow.

That creates a productivity paradox. The better the AI coding assistant is, the more developers want to use it. But the more it becomes part of real engineering work, the harder it is to rely on quotas, usage windows, or individual account limits.

For enterprise AI teams, this is not just annoying. It becomes an operational problem. A company does not want critical development workflows to depend on opaque limits or unpredictable token consumption. It needs a model that supports real adoption across engineering teams.

Zylon’s positioning is different: the platform emphasizes unlimited, fixed-cost usage independent of tokens, with no per-token pricing or usage limits.

That matters for private AI development because coding agents are most valuable when developers can use them continuously: not just for a demo, not just for a few isolated prompts, and not just until the quota resets.

Why this matters for enterprise AI teams

Enterprise AI adoption often starts with productivity, but once AI moves from experimentation into daily operations, the requirements change.

The organization needs governance, observability, authentication, model control, predictable cost, and integration with existing infrastructure. That is especially important in software development, where a single AI coding session may involve source code, database schemas, deployment scripts, internal documentation, logs, test outputs, and architecture decisions.

This is why private AI development should not be treated as a niche use case. It is becoming a core requirement for companies that want the benefits of AI coding assistants without introducing uncontrolled risk.

Zylon’s API Gateway is the extensibility layer for integrating with existing AI tooling, automation workflows, development environments, and custom applications. Supporting Opencode and Claude Code fits directly into that vision.

The developer experience remains familiar. The control plane becomes enterprise-grade.

How the setup works at a high level

The setup is intentionally straightforward.

For both Opencode and Claude Code, the first step is creating a Gateway Token in Zylon. 

From there, developers configure their local environment.

For Opencode, the setup uses variables like:

ZYLON_BASE_URL="https://your-host/api/gpt"

ZYLON_API_TOKEN="your-gateway-token"

ZYLON_MODEL="default"

Then Opencode is configured to use Zylon as the model provider, with the base URL and API key pulled from the environment. The Zylon docs also show a model configuration with a 128,000-token context limit and 20,000-token output limit. (docs.zylon.ai)

For Claude Code, developers configure Anthropic-compatible variables that point back to Zylon:

ANTHROPIC_BASE_URL="$ZYLON_BASE_URL"

ANTHROPIC_AUTH_TOKEN="$ZYLON_API_TOKEN"

ANTHROPIC_MODEL="$ZYLON_MODEL"

Once configured, running claude connects Claude Code to the Zylon instance instead of the default external endpoint. (docs.zylon.ai)

The important idea is simple: developers can continue using CLI-based coding agents, while model access, authentication, and infrastructure routing are managed through Zylon.

Private AI development is not just about keeping prompts private

When people talk about private AI, they often focus on prompts and documents. That is important, but for engineering teams privacy goes deeper.

Private AI development means protecting the entire development context: source code, internal tools, repositories, architectural patterns, tickets, test outputs, CI/CD errors, package choices, and technical debt.

A coding agent does not need production credentials to reveal sensitive information. The structure of a codebase can already say a lot. The naming of services can expose business priorities. Deployment files can reveal infrastructure strategy. Error logs can include customer identifiers. Internal comments can explain edge cases that should stay inside the organization.

This is why “just don’t paste secrets” is not enough as an enterprise AI policy.

Developer teams need systems that make the safe path the default path. By making Opencode and Claude Code available through Zylon, organizations can give developers useful AI assistance while keeping the workflow aligned with private AI principles: local control, enterprise governance, and reduced exposure to external AI services.

From individual AI coding to enterprise AI coding

Most AI coding tools became popular through individual adoption. A developer tries a tool, it saves time, and gradually it spreads across the team. That bottom-up adoption pattern is understandable because developers want better tools, but it also creates a gap between individual productivity and enterprise control.

For one developer, the main question is whether the assistant helps them ship faster. For the organization, the question is broader: whether hundreds or thousands of developers can use AI coding tools safely, consistently, and predictably.

At enterprise scale, companies need to know which tools are being used, which models are available, how access is granted, where data is processed, what happens when an employee leaves, and whether usage can be governed centrally.

Zylon’s approach makes AI development part of the enterprise AI platform, not an unmanaged side channel.

That is the real value of bringing Opencode and Claude Code inside Zylon. The coding assistant becomes part of the organization’s private AI infrastructure.

Why this is especially relevant for regulated industries

Regulated industries are under pressure to adopt AI, but they cannot treat privacy and compliance as afterthoughts.

In finance, healthcare, government, defense, and critical infrastructure, development environments often contain sensitive operational knowledge. Even when no customer data is involved, internal code and architecture can reveal too much about how systems work. That makes external AI coding workflows difficult to approve when they bypass procurement, security review, auditability, or data residency requirements.

At the same time, these organizations still need modern developer productivity. They often operate large software portfolios, complex internal systems, legacy applications, and strict review processes. AI coding assistants can help, but only if they fit the security posture of the organization.

Private AI development gives those teams a more realistic path forward.

Instead of blocking coding assistants completely, they can deploy them through a private AI platform that keeps control inside the enterprise.

The future of coding agents will be private, governed, and always available

Coding agents are not going away.

They will become more capable, more autonomous, and more deeply embedded in engineering workflows. The question is whether enterprises will adopt them intentionally or let them spread through unmanaged tools and personal accounts.

Zylon’s answer is to make AI coding assistants usable inside a private AI platform.

That matters for privacy, because development context can stay inside controlled infrastructure. It matters for quota control, because coding agents become much more useful when developers are not constantly thinking about token ceilings. It matters for cost predictability, because enterprise AI adoption needs a model that scales beyond individual usage limits. And it matters for governance, because AI development should be part of the company’s official AI stack.

Opencode and Claude Code inside Zylon give developers the experience they want: AI support directly in the terminal, close to the code, available for real work.

They also give organizations the control they need: private infrastructure, gateway-based access, token management, and a platform designed for regulated environments.

For companies building serious enterprise AI programs, this is where private AI development needs to go.

Not less AI for developers. Better AI infrastructure for developers.

Author: Daniel Gallego Vico, PhD, Co-Founder & Co-CEO at Zylon
Published: April 2026
Daniel specializes in secure enterprise AI architecture, overseeing on-premise LLM infrastructure, data governance, and scalable AI systems for regulated sectors including finance, healthcare, and defense.