Antigravity 2.0: licensing, quota, and Gemini CLI migration without surprises
A validated guide to Antigravity 2.0: four surfaces, shared quota, compute-based limits, individual plans, Google Cloud enterprise use, and Gemini CLI migration.
Fabiano Brito
CEO & Founder
Antigravity 2.0 is no longer just “an AI editor.” It is a family of surfaces for agentic development: desktop app, IDE, CLI, and SDK. The most important budget change is not visual. It is operational: consumption is governed through shared quota and compute-based limits, so every model choice, reasoning mode, and tool call matters.
The confusion comes from three changes arriving together. Google split the experience into different surfaces. AI plans now describe limits in compute terms rather than only prompt counts. And the Gemini CLI transition to Antigravity CLI put a date on the calendar: June 18, 2026 for individual users to move off the legacy path.
This article uses the supplied Medium post as a topic reference, not as a primary source. Factual claims are grounded in Google announcements, documentation, or official pages.
The four Antigravity 2.0 surfaces
The common mistake is looking for “Antigravity” as one window. The architecture is now more modular: one surface coordinates, one edits, one automates, and one embeds agents into owned systems.
Antigravity Desktop
A hub for tracking agents, sessions, and long-running work. It matters once the workflow grows beyond “chat inside the editor.”
Antigravity IDE
The code editor surface for refactors, diffs, tests, debugging, and contextual chat next to the repository.
Antigravity CLI agy
A terminal interface for scripts, automation, and Gemini CLI migration flows without keeping the IDE open.
Antigravity SDK
A layer for building custom agents and connecting them to enterprise infrastructure with identity, logs, deployment, and data controls.
That separation is healthy for engineering teams. IDE, orchestration, terminal automation, and corporate runtime are different concerns. Combining them used to feel simple, but it hid cost and permission boundaries.
Antigravity 2.0 should not be bought as an editor; it should be governed as an agent execution surface.
What changed in quota: compute is now the practical unit
The sensitive part is consumption. Google’s AI plans describe a move toward compute-based limits, refreshed over time up to a weekly ceiling. In practice, the question changes from “how many prompts do I have?” to “how much work is this prompt asking the system to do?”.
A simple question on a lightweight model is not the same as a task that reads a repository, plans steps, calls tools, generates a patch, and re-evaluates tests. For FinOps, long prompts, agent loops, and high reasoning modes need explicit rules.
| Decision | Quota impact | Recommended policy |
|---|---|---|
| Default model | More capable models tend to consume more compute per task. | Use economical models for triage, search, and simple edits; escalate only when the task requires it. |
| Reasoning mode | Higher reasoning costs more because the model plans and verifies more before answering. | Reserve it for difficult bugs, architecture, incidents, and critical migrations. |
| Attached context | More files, logs, and history increase the processed volume. | Attach the smallest sufficient context and prefer targeted repository search. |
| Agent loops | Each iteration can call the model, a tool, a test, and another analysis pass. | Set step limits, human checkpoints, and a budget per task. |
The operational point is simple: shared quota punishes invisible waste. If a cheap automation runs in an expensive mode all day, it burns budget that should be reserved for truly complex work.
Individual plans vs. enterprise operations
For individuals and small teams, individual plans may work: the user buys capacity, uses it in the editor, and manages their own pace. The problem appears when a company tries to manage dozens or hundreds of standalone subscriptions. Cost stops being “a monthly fee” and becomes governance: identity, audit, data policy, billing, and access control.
Individual use
Good for testing, learning, prototypes, and solo developers. Risk is lower because the scope of people and data is small.
- Control
- User account
- Billing
- Subscription
Technical team
Requires model policy, usage limits, review of AI-assisted code, and a line between prototype and production.
- Control
- Team policy
- Billing
- Cost center
Regulated enterprise
Needs corporate identity, enterprise terms, audit trail, data controls, and consolidated cloud billing.
- Control
- IAM / Cloud Identity
- Billing
- Google Cloud
For enterprise use, the right question is not “which plan is cheaper?”. It is “which access model reduces risk and lets us measure cost by product, squad, or workflow?”. Coding agents touch intellectual property, secrets, dependencies, customer data, and pipelines.
Migrating from Gemini CLI to agy
The most concrete transition is from Gemini CLI to Antigravity CLI. Google published a specific notice for individual users: migration to agy needs to happen by June 18, 2026. After that date, the legacy flow is no longer the supported path for that profile.
The plugin migration command is direct:
agy plugin import geminiDo not treat this as merely “swapping a binary.” Treat it as an engineering tool migration. Inventory scripts, aliases, hooks, plugins, environment variables, permissions, and internal docs that still call the old CLI.
Search scripts, READMEs, local CI, shell aliases, and project templates for Gemini CLI usage.
Run agy plugin import gemini and validate whether critical workflows still read context, call tools, and generate patches.
After migration, monitor usage by task type; do not assume the old default has the same operational cost.
Define when to use IDE, desktop, CLI, and SDK so each developer does not invent their own cost and risk pattern.
The practical trick: route work by cost and risk
The best Antigravity 2.0 practice is not “always use the strongest model.” It is a simple routing matrix: cheap task in economical mode; uncertain task with more context; critical task with high reasoning and human review.
| Task | Initial setup | When to escalate |
|---|---|---|
| Explain a file or function | Economical model, minimal context, no long loop. | When it affects architecture or security. |
| Local refactor | IDE with small diff, unit tests, and step limit. | When it changes public contracts, schema, or authentication. |
| Intermittent bug | Medium reasoning with selected logs. | Use high reasoning when hypotheses conflict and error cost is high. |
| Large migration | Plan split into stages, human checkpoints, and explicit budget. | Escalate model and context by stage, not across the whole repository at once. |
This policy reduces waste without blocking productivity. Developers keep using AI in their normal flow, while the company avoids letting trivial tasks drain quota needed for incidents, architecture, and critical delivery.
Adoption checklist
Before rolling out Antigravity 2.0 to a full team, settle five decisions:
Use IDE for code, desktop for orchestration, CLI for automation, and SDK for internal products.
Document which model and reasoning mode to use for triage, simple edits, debugging, and architecture.
Classify what may enter prompts: public code, private code, logs, personal data, and secrets.
Track consumption by team, task type, model, and delivered outcome.
Every agent-generated change needs tests and review proportional to risk.
Conclusion
Antigravity 2.0 is good news for engineering, but demanding news for governance. It separates work surfaces, improves the terminal path with agy, and brings development agents closer to real enterprise workflows. In exchange, it forces companies to stop improvising: shared quota, compute, reasoning, and code access need explicit policy.
Treat it as a personal subscription and cost will appear later. Treat it as an agent execution platform and you can capture productivity without giving up control.
Want coding agents without losing governance?
We help teams design usage policy, CLI migration, consumption observability, and safe engineering-agent pipelines.
Also read
- Antigravity and Managed Agents: facts for technical teams
- Agents CLI: Google turns your editor into an ADK specialist
- Google AI Ultra in 2026: what changes for AI budgets
- AI agent security: prompt injection in production
Primary sources: Google I/O 2026 developer highlights; Transitioning Gemini CLI to Antigravity CLI; Google AI subscriptions; Google Cloud generative AI security and privacy terms.