OpenCode, Antigravity, and Gemma 4: Safe AI Coding Tools

Posted: December 31, 1969 to Technology.

If you run a development team at a regulated business in 2026, three tools are probably on your evaluation list right now: OpenCode, Google Antigravity, and Gemma 4. All three promise to turn your IDE into an agent-driven productivity machine. All three arrived in the last six months. And all three ask very different questions about where your source code actually lives during a coding session.

We get the same call at Petronella Technology Group several times a month. A CTO or an engineering lead reads a Hacker News thread about Google Antigravity, or a Reddit post about OpenCode plus a local Gemma model, and they want to know one thing: can we let our developers use this without breaking CMMC, HIPAA, or a client NDA? The honest answer is "it depends on how you set it up." The longer answer is the rest of this guide.

We help clients evaluate, pilot, and deploy AI coding tools on private infrastructure, including self-hosted model hosting on our enterprise private AI cluster. This post walks through what each of these three tools actually does, where your code and prompts go, and how to think about risk when your developers are working inside a federal contract, a HIPAA Business Associate Agreement, or an SEC-regulated environment.

Why This Matters Now

AI-assisted coding is not a future trend anymore. GitHub and other vendors have been reporting for almost two years that developers using AI assistants ship code meaningfully faster. The productivity number varies by study and task type, but the direction is unambiguous: teams that do not use AI coding tools will be outpaced by teams that do.

The problem is that most of the coverage skips straight to "which tool is best" and ignores the part that matters to a regulated business: what happens to the code, the prompt, the file tree, the stack traces, and the environment variables the moment a developer hits send? For a shop that writes consumer apps, the answer is mostly "who cares." For a shop writing claims adjudication logic, controlled unclassified information handlers, or trading software, the answer is the difference between a clean audit and a reportable incident.

Here is the real question for regulated teams. When a developer types "fix this function" into an AI coding assistant, how many parties now have a copy of that function? How long do they retain it? And did they agree, in writing, to the same data-handling standard the rest of your business is contractually obligated to meet?

Let us look at the three tools through that lens.

OpenCode: The Terminal-Native Open-Source Option

OpenCode is an open-source AI coding agent that runs in your terminal. The project describes itself as "the open source AI coding agent" and the installation is a single curl command to install it from opencode.ai. The GitHub repository at github.com/opencode-ai/opencode has over 140,000 stars as of this writing, and the project claims more than 6.5 million monthly developers.

What actually differentiates OpenCode for regulated teams is not the popularity. It is the architecture.

How OpenCode Handles Your Code

OpenCode is written in Go. When you run it, it launches a Bubble Tea terminal UI on your workstation. That terminal UI reads files from your local filesystem, assembles context, and sends a prompt to whichever model provider you have configured. Critically, the provider is your choice. The OpenCode project itself is not a model provider.

That means OpenCode can talk to Anthropic Claude, OpenAI, Google Gemini, AWS Bedrock, Groq, Azure OpenAI, OpenRouter, or a local model server like Ollama or vLLM. As the official docs note, you "configure your API keys" for whichever provider you want.

The implication for a regulated team is significant. If you run OpenCode against a local Ollama server hosting Gemma 4 on a GPU in your own rack, your source code literally never leaves the machine. There is no third-party SaaS in the path. The network traffic goes from the terminal to localhost. That is a very different risk profile than sending your code to a SaaS IDE.

If you run OpenCode against Anthropic's API instead, your code does go to Anthropic, but under Anthropic's enterprise terms, which include zero-retention options and standard business associate language for HIPAA-covered clients when you are on the right plan. That is also very different from a consumer IDE pinging a shared backend.

The OpenCode project itself states that it "does not store any of your code or context data, allowing it to operate in privacy sensitive environments." In practical terms, OpenCode is a router. Your code goes where you point it.

Weaknesses to Know

OpenCode is a terminal tool. There is a desktop app and an IDE extension, but the core experience is text-first. Developers who are used to Cursor or Copilot inside VS Code may find the transition awkward. The vim-like key bindings and the TUI are productive once you adapt, but the on-ramp is steeper than a point-and-click IDE.

The project also iterates fast. That is good for features, less good for a regulated business that wants pinned dependencies and a stable audit trail. If you deploy OpenCode in an environment under CMMC or HIPAA scrutiny, you need a process for pinning a specific release, tracking CVEs, and validating updates before they roll to developer workstations. We help clients build that kind of change control around OpenCode as part of a broader secure development lifecycle.

Google Antigravity: The Agent-First IDE

Google Antigravity is a different animal. It was announced November 18, 2025 as part of Google's Gemini 3 launch. The official announcement on the Google Developers Blog describes it as "a new agentic development platform designed to help you operate at a higher, task-oriented level." As of launch it was free for individuals in public preview.

Antigravity is a fork of Visual Studio Code with heavy modifications. Where VS Code and Cursor treat AI as an assistant that lives in a sidebar, Antigravity treats agents as first-class entities. The platform has two surfaces. The editor view looks like a conventional IDE with an AI sidebar. The manager view is a control plane where you spawn and orchestrate multiple agents working in parallel across workspaces.

Agents in Antigravity produce what Google calls "Artifacts," which are verifiable deliverables like task lists, implementation plans, screenshots, and browser recordings. The goal is to let you review what an agent actually did without digging through raw tool calls. It is an impressive engineering feat. It also runs fundamentally on Google's cloud.

How Antigravity Handles Your Code

Antigravity is not a local-only tool. When an agent plans a task, executes a command, or verifies output, the prompts and code context flow to Google's model endpoints. By default the platform supports Gemini 3 Pro, Anthropic's Claude Sonnet 4.6 and Opus 4.6, and OpenAI's GPT-OSS-120B.

Privacy posture here is the real issue for regulated buyers. A thread on the Google AI Developers Forum captures the concern bluntly. Google's own terms of service say that interactions are used to improve Google's products and that employees and contractors may access, review, and use those interactions. There is a carve-out for Google Workspace and GCP access paths, but at the time of this writing Workspace sign-in is not fully supported inside the Antigravity client, and the GCP path is not trivial for an IDE end user to enable.

A subsequent forum thread titled Data privacy for commercial use has developers asking the same question and not getting a clean answer. Opt-out instructions are not centralized, and at least one community thread documents a user struggling to get a data deletion request honored.

To be fair to Google, Antigravity launched in November 2025 and the privacy controls will likely mature. Enterprise Workspace and GCP paths will probably get fully wired up. But today, if you are running a CMMC Level 2 assessment or you handle protected health information, you cannot install Antigravity on developer workstations and assume the code they open in it stays out of a model training pipeline. It may or may not. The controls are not yet clear enough for a regulated signoff.

That is not a knock on Antigravity as a tool. The agent orchestration is genuinely useful. It is a reminder that regulated businesses cannot buy the same tools consumer developers buy, not without a careful read of terms and a honest conversation about what gets sent across the wire.

When Antigravity Makes Sense

For internal tooling, prototypes, and greenfield work that does not touch regulated data, Antigravity is worth trying. The agent orchestration is better than what most IDEs ship, and the artifact model is a real improvement over opaque "trust me" AI output.

For anything touching controlled unclassified information, protected health information, or client code under a strict NDA, the honest answer today is to wait for Google to publish clearer enterprise data-handling commitments for Antigravity specifically, or to route developers through an approved alternative that you control. We help clients make that call on a case-by-case basis and maintain an internal policy matrix mapping tool choices to data classifications.

Gemma 4: Google's Open-Weights Answer

Gemma 4 is Google's April 2, 2026 open-weights release. The announcement went up on the Google Keyword blog and Google DeepMind documented the model family at deepmind.google/models/gemma/gemma-4/. Unlike Gemini 3, which lives on Google's servers, Gemma 4 is downloadable. Weights. Apache 2.0 license. You run it on your hardware.

This is the tool that changes the conversation for regulated businesses, and it is the one most teams have not yet thought through carefully.

What Gemma 4 Actually Is

Google released four Gemma 4 variants. The two edge models are called E2B and E4B. The two larger models are a 26B mixture-of-experts and a 31B dense model. The 26B MoE activates only about 3.8 billion parameters per inference, which gives it latency characteristics closer to a mid-range model while retaining the quality of a larger one. The 31B dense model is the quality flagship. Google reports it at number three on the LM Arena open-source leaderboard.

Context windows run up to 256K tokens on the larger models, 128K on the edge variants. The models support over 140 languages and handle text, image, and (on small sizes) audio inputs. The license is Apache 2.0, which is as commercial-friendly as an open-weights license gets.

Coding performance is strong but not dominant. Third-party analysis at the Gemma 4 Wiki SWE-bench breakdown puts Gemma 4-31B at 82.7 percent on HumanEval and 52.0 percent on SWE-bench Verified. LiveCodeBench comes in at 80.0 percent. Those are not the highest numbers in the industry. Closed frontier models and DeepSeek V4 post higher SWE-bench scores. But for an open-weights model you can run entirely inside your own perimeter, 52 percent on SWE-bench Verified is plenty to anchor a regulated development workflow.

Why Self-Hosting Matters for CMMC and HIPAA

Here is the part that matters for a compliance officer. When you run Gemma 4 on your own GPU infrastructure, the code and the prompts never leave your environment. There is no API call to a third-party cloud. There is no "interaction" that a vendor's employees can review under a terms of service clause. The boundary of your assessment is the boundary of the server rack.

For CMMC Level 2, that means the model inference falls inside your existing in-scope systems. You are not adding a new external service provider. You are not introducing a new flow-down contract. You are running another internal application, and the NIST SP 800-171 controls you already have in place apply to it the same way they apply to your Git server, your Jira instance, or your internal wiki.

For HIPAA, the same logic applies. A Business Associate Agreement is not required for software you host and operate yourself. The covered entity or business associate is still you, and the model is a tool you control. You do not add a new BAA chain.

For SEC and financial data, the argument is the same. Your data never touches a third-party training pipeline because the inference never leaves your network.

That is why Gemma 4 is a meaningful release. It lets regulated businesses get most of the productivity benefit of AI coding without taking on new external vendor risk.

Real Hardware Requirements

The 31B dense model in full bfloat16 precision fits on a single NVIDIA H100 80GB, per the Google DeepMind model documentation. With quantization, the same model runs comfortably on consumer GPUs like a pair of RTX 4090s or a single RTX 6000 Ada. The 26B MoE runs well on similar hardware with better throughput for multi-user scenarios. The E4B edge model runs on a decent laptop GPU or even CPU inference, albeit more slowly.

For a small development team, a single-node inference server with a pair of consumer GPUs can host Gemma 4 and serve a dozen developers comfortably during a normal workday. That is well under the cost of a year of a commercial SaaS AI coding seat per developer for the same team. We sized this out for a CMMC-covered client last month and the ROI broke even in under eight months including the cost of the GPU hardware and the installation labor.

Serving Gemma 4 in Practice

The easiest on-ramp is Ollama. You install Ollama on a Linux host with a suitable GPU, run a single pull command, and the model is available behind an OpenAI-compatible HTTP API at an address on your internal network. You point OpenCode, Continue.dev, or any OpenAI-API-compatible client at that address. Done.

For production, we typically move clients to vLLM or TGI for better batching, concurrency, and observability. That adds some operational complexity but pays back quickly once you have more than a handful of developers hitting the model simultaneously. We also add logging, usage tracking, and prompt auditing so that the compliance team has a record of what was asked and what was generated, which closes a gap in most AI coding deployments.

The Pattern That Actually Works for Regulated Teams

If you pull the three tools together, a clear pattern emerges.

OpenCode is the client. It runs on the developer workstation. It gives the developer a terminal-native, provider-agnostic way to prompt a model, inspect files, run commands, and apply patches.

Gemma 4 is the engine. It runs on a GPU host inside your network. It is the open-weights model that OpenCode talks to. Because it runs on your hardware, your prompts and your code never leave the perimeter.

Google Antigravity is the one you evaluate, possibly pilot for non-sensitive work, and keep out of your regulated environments until Google publishes clearer enterprise data-handling commitments and ships a fully supported Workspace or GCP path for IDE users.

That layered approach gives your developers the productivity boost they want, keeps the code inside the boundary your auditors care about, and avoids the "we did not read the terms of service" mistake that has burned more than one company in the last year.

Compliance Considerations That Most Teams Skip

Even when you self-host, AI coding tools introduce control questions that a classical IDE does not. A few that we raise every time we help a client deploy one of these stacks.

Prompt Logging and Retention

A traditional IDE does not keep a log of what the developer typed. A self-hosted model deployment can, and for a CMMC or HIPAA environment, probably should. The question is not whether to log but what to log, where to store it, how long to retain it, and who can access it. Overly aggressive logging creates its own risk, because those logs will contain snippets of protected data. Overly light logging makes incident response impossible.

The practical answer is structured prompt metadata plus hashed payloads, with access restricted to the security team and a documented retention window that matches your incident response policy. We typically set this up as part of the deployment rather than as an afterthought.

Supply Chain Integrity

Open-weights models are, in theory, reviewable. In practice, almost no one reviews model weights for backdoors. The mitigation is to pull weights only from the official Google distribution channels (Kaggle, Hugging Face, or Ollama's official Gemma tags), verify checksums, and pin a specific version. Your AI model is now part of your software supply chain and deserves the same scrutiny as any other dependency.

Agent Permissions

An agentic tool like Antigravity, or even OpenCode running with full shell access, can do a lot on behalf of the developer. It can write files, run tests, push branches, install packages, and hit network endpoints. In an unregulated environment, that is a productivity win. In a regulated one, the principle of least privilege applies to the agent just as it applies to the human.

We typically run AI coding tools inside a purpose-built developer sandbox with restricted network egress, restricted filesystem access outside the project, and a documented set of approved actions the agent is allowed to perform without human confirmation. That workflow is not hard to set up once, but it has to be designed in from the start rather than bolted on after a breach.

Training Data Provenance

When a developer accepts a suggestion from a closed model trained on scraped public code, there is a nonzero chance the suggestion is a near-copy of a GPL-licensed project. The legal exposure from that is small but not zero, and it is an issue auditors are starting to ask about. Open-weights models released under Apache 2.0 by major vendors, like Gemma 4, reduce the provenance question to one vendor's training corpus policy rather than a chain of unknown SaaS providers. That is a narrower and more defensible audit answer.

How Petronella Technology Group Helps

Petronella Technology Group runs more than ten production AI agents for our own business and for clients. That fleet includes Penny, our inbound voice assistant at (919) 348-4912 who answers sales calls and books the first working session. Peter is the site chatbot that handles cyber, compliance, and AI questions in real time. ComplyBot walks buyers through CMMC and HIPAA readiness questions. An Auto Blog Agent produces long-form content with editorial review. Several private digital twin voice assistants are deployed on behalf of clients who wanted a branded voice presence without handing call audio to a consumer AI vendor. We mention all of that because we are not recommending AI coding tooling from the outside. We run this stack ourselves, we have felt the compliance friction, and we have built the controls that let it scale without becoming a liability.

This is the kind of work we do with clients every week. A typical engagement looks like the following.

First, we run an AI coding tool assessment. We sit with the development leads, the security team, and the compliance lead, and we map your data classifications against the tools your developers are actually using or want to use. Shadow IT is real in this space. Many teams find that developers have already installed personal Cursor or Copilot seats on work laptops. That conversation is better had proactively than after an incident.

Second, we design a safe AI coding environment. That usually means a self-hosted inference server running Gemma 4 or a similar open-weights model, a hardened client like OpenCode with appropriate policy wrappers, and a set of documented approved workflows. For clients under CMMC, we align the whole setup with the relevant NIST SP 800-171 controls and prepare the documentation an assessor will ask for.

Third, we pilot. We pick one team, typically four to eight developers, roll the stack out, measure productivity and satisfaction for a month, and adjust. Real-world friction is different from whiteboard friction. The pilot surfaces the gaps.

Fourth, we scale. Once the pilot is clean, we roll out to the broader engineering org with documented policies, training, and a support path.

Throughout, we keep the code and the prompts inside your network. Our enterprise private AI cluster is available as infrastructure for clients who do not want to run the GPUs themselves but also do not want to use a public cloud vendor's AI product. For clients who have their own data centers, we deploy on their hardware. Either way, the data does not flow to a third-party training pipeline.

A Short Comparison Table

Here is a quick summary of the three tools through the lens of regulated-business readiness.

• OpenCode. Open source terminal coding agent. Provider-agnostic. Safe to use today as long as you choose the model endpoint carefully. Ideal client for a self-hosted Gemma 4 backend. Works well under CMMC and HIPAA when paired with a local model.
• Google Antigravity. Agent-first IDE fork of VS Code. Powerful. Ships prompts and code to Google model endpoints under terms that are not yet enterprise-clean. Pilot for non-sensitive work. Keep out of regulated pipelines until Google publishes clearer commercial data-handling commitments and a supported Workspace sign-in path.
• Gemma 4. Open-weights model family from Google. Apache 2.0 license. Strong coding benchmarks. Runs on your own GPUs. The anchor of a safe AI coding stack for regulated businesses.

Going Deeper

If you want to compare against the broader landscape, start with our guide to AI for business. For the security posture around any of this, review how we approach cyber security, because the right AI coding stack is a subset of a broader defensible development environment. And if you are specifically thinking about a self-hosted model deployment, our page on the enterprise private AI cluster walks through the architecture we use for clients.

The Bottom Line

AI coding tools are here. Your developers are either using them already or will be by the end of the quarter. For a regulated business, the question is not whether to adopt them but how to adopt them without creating new exposure.

The good news is that the tooling finally supports a clean answer. OpenCode as the client, Gemma 4 as the engine, and a GPU host you control gives you a stack where the prompts and the code never leave your network, the license is Apache 2.0, the productivity gain is real, and the audit story is clean. Google Antigravity is worth watching and pilot-testing for unregulated work, but it is not yet the right fit for protected data.

If you want help mapping this to your specific environment, or if you want a hands-on pilot of the self-hosted stack we described, Petronella Technology Group can run the assessment, design the deployment, and prove the productivity gain with your team. We pair a formal CMMC and HIPAA evaluation with the technical build so the compliance narrative is written at the same time as the infrastructure, not bolted on six months later. Call us at (919) 348-4912 and Penny will route you to a real engineer, or reach out through our contact page and we will set up a conversation.

Petronella Technology Group is a CMMC-AB Registered Provider Organization (RPO #1449), BBB A+ rated since 2003, and has been helping Raleigh-area businesses secure their technology since the company was founded in 2002. Craig Petronella holds CMMC-RP, CCNA, CWNE, and DFE #604180 credentials. Our team is fully CMMC-RP certified.

Sources

• OpenCode homepage, opencode.ai
• OpenCode documentation, opencode.ai/docs
• OpenCode GitHub repository
• Google Developers Blog, Build with Google Antigravity
• Google AI Developers Forum, Antigravity privacy thread
• Google AI Developers Forum, Data privacy for commercial use
• Google Keyword blog, Gemma 4 announcement
• Google DeepMind Gemma 4 model page
• Google AI for Developers, Gemma 4 model card
• Gemma 4 Wiki, SWE-bench analysis
• CMMC-AB verification for Petronella RPO #1449