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Antigravity 2.0 vs Liner Developer Platform

Bottom line

Antigravity 2.0 has a free plan plus paid upgrades, while Liner Developer Platform is free. Liner Developer Platform is open source and Antigravity 2.0 is not. You can run Liner Developer Platform on your own machine or server; Antigravity 2.0 is hosted by the vendor.

Which one should you try?

Antigravity 2.0

Freemium

Best for Developers building with AI agents

Visit Antigravity 2.0 See full review

Liner Developer Platform

Free

Best for Users with no ML or coding background

Visit Liner Developer Platform See full review

Pricing, capabilities and platforms side by side. Prices are as of the "Pricing checked" date in the table.

Antigravity 2.0

Antigravity 2.0

The vendor describes Project IDX as a browser-based IDE where agents handle multi-step coding tasks end-to-end: writing code, executing it, observing what breaks in a live preview, and self-correcting before handing back control. Multi-model support means you are not locked to a single provider when one model handles your stack better than another. The free tier exists but carries usage caps that surface quickly on longer agentic runs — teams hitting those caps mid-task face a hard stop, not a graceful queue. Browser-based architecture removes local setup friction but also removes offline access and the deep editor customization that engineers who have spent years tuning their environment tend to miss.

Liner Developer Platform

Liner Developer Platform

Liner is a free desktop application for Windows and macOS that trains image, text, audio, video, and object detection models without writing code. You import labeled data, press train, and the tool selects an architecture and runs the job locally on your CPU — the vendor states training completes within minutes in most cases. Your data never leaves the machine, which matters for projects under privacy or compliance constraints. The export targets edge and mobile platforms, so the output is usable beyond the desktop. The ceiling arrives fast: there are no knobs to turn on architecture, no API to call from a pipeline, and no route to production at scale without rebuilding elsewhere.

AttributeAntigravity 2.0Liner Developer Platform
PricingFreemiumFree
Price$0-$200/month—
Pricing checkedJun 11, 2026Not checked yet
Free trialNot statedNot stated
Open sourceNoYes
Has APIYesNo
Self-hosted optionNoYes
PlatformsmacOS, Windows, Linux, Web-basedWindows, macOS
Released2025-11—
Pros
  • Self-verifying execution loop — the agent runs code, observes live browser output, and revises without waiting for you to relay what broke, which means you stop being the error-relay between your AI tool and your test environment.
  • Multi-model support in a single environment, so switching the underlying model when one handles your framework better is a configuration change rather than a tool migration.
  • Browser-based access with no local setup, which means onboarding a new developer or spinning up a fresh environment takes minutes rather than an afternoon of dependency resolution.
  • Multi-agent task splitting lets separate agents handle discrete parts of a complex task in parallel, cutting the wall-clock time on multi-step workflows that a single-agent loop would process serially.
  • API access means the agentic core can be called from external pipelines, so teams integrating AI into CI or build systems are not forced to use only the browser interface.
  • Trains classifiers and object detectors on a CPU without a GPU, so teams without dedicated ML hardware are not blocked from running a first experiment.
  • All training stays on the local machine and no data is sent to the cloud, which means projects under data residency or privacy constraints can use it without a legal review of third-party data processing.
  • Automatic model selection removes the architecture decision entirely, so a domain expert with labeled data can reach a trained model without an ML engineer involved.
  • Built-in dataset library gives a starting point when you do not yet have your own labeled data, cutting the time from install to first trained model.
  • Edge-optimized model export means the output can run on mobile or embedded devices, so the prototype does not die at the desktop boundary.
Cons
  • Free tier usage caps terminate agentic runs mid-task when a multi-step job exceeds the allotment — there is no graceful queue, the session stops, and teams restart manually or upgrade to a paid tier before they have fully evaluated whether the tool fits.
  • No self-hosted option and no offline access: teams with data residency requirements, air-gapped environments, or security policies restricting cloud-only tooling cannot use this at all, and those teams move to locally-deployable alternatives rather than filing exception requests.
  • Browser-based execution means editor customization stops at what Google exposes in the interface — developers who depend on a specific plugin, language server configuration, or terminal workflow find the ceiling fast, and the path forward is maintaining a second local environment for the tasks IDX cannot handle.
  • Complex conditional branching across more than a few agents strains the multi-agent coordination layer; community reports describe tasks with deep dependency chains producing inconsistent results, and teams handling those workflows add manual checkpoints that undercut the automation they bought the tool to achieve.
  • Liner exposes no controls over model architecture, hyperparameters, or training configuration — when your dataset produces a weak model, there is nothing to adjust inside the tool, and the only path forward is moving to a framework like PyTorch or TensorFlow where you control the training loop.
  • There is no API, no CLI, and no programmatic interface of any kind, so the tool cannot be embedded in a training pipeline, triggered by new data arrivals, or integrated into any automated workflow — teams that need reproducible, scheduled retraining abandon Liner entirely at that point.
  • The vendor page does not specify which export formats or inference runtimes are supported, which means you cannot confirm deployment compatibility with your target environment until after you have trained the model — a costly discovery late in a prototype cycle.
  • The tool is described as a beta download with community support as the only listed support channel, so production-blocking issues have no escalation path and no SLA — teams with deadline commitments tied to model delivery treat this as disqualifying.

Made up your mind?

Antigravity 2.0

Freemium

Best for Developers building with AI agents

Visit Antigravity 2.0 See full review

Liner Developer Platform

Free

Best for Users with no ML or coding background

Visit Liner Developer Platform See full review

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Specs come from each tool's own site and docs (how we test). AIDiveForge is editorially independent.

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