Google AI Studio vs Laguna by Poolside: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Google AI Studio and Laguna by Poolside — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Google AI Studio
Web-based platform from Google to build, fine-tune, prototype and deploy applications using Gemini and related multimodal models.
Key features
- Prompt-to-Production Workflow: Integrated UI and tooling to iterate on prompts, build prototype applets and move prototypes toward production-ready deployments with Gemini models.
- Multimodal Model Access: Native access to Gemini model capabilities including text, image, audio and video modalities and the Live API (audio/video streaming) for interactive multimodal experiences.
- Fine-Tuning and Custom Models: Ability to fine-tune base models for custom tasks and datasets (community reports indicate free fine-tuning options within Studio), enabling tailored performance for domain-specific use cases.
- Starter Applets and Local Development: Official starter applets (React-based) that run inside AI Studio and can be run locally by inserting a Gemini API key, accelerating building of map, video, and interactive demos.
- Function Calling and Tooling Integration: Support for function calling, code execution, and integrated Google search grounding to let models call external APIs (e.g., Maps Embed) and execute external actions.
- Media Generation & Plugins: Access to media generation (Imagen, Veo) and model features that produce or manipulate images, video, and other media formats for richer applications.
- Vertex AI Compatibility: Compatibility with Google Cloud Vertex AI for enterprise developers who need managed infrastructure, scaling, and enterprise-grade deployment options.
- Examples, Cookbook & SDKs: Official example repositories and SDK guides (Gemini cookbook) to demonstrate quickstarts, LiveAPI usage, and multi-feature integrations for developers.
- Interactive web IDE for prompting and testing Gemini models
- Fine-tuning and customization of base models (free fine-tuning options mentioned)
- Starter applets and templates (React-based) that run inside AI Studio
- Integration with Gemini API and Vertex AI APIs for training and deployment
- Support for function calling / invoking external APIs (e.g., Maps Embed API)
- Demonstrations of 2D and 3D spatial understanding and reasoning
- Local development workflow using environment (.env) files with Gemini API key
- Tooling for building AI agents and multi-component applications
- Works with regional Vertex AI deployments (EU / UK compatibility noted)
Best for
- Prompt engineering and rapid prototyping: Iteratively design and test prompts and conversational flows for Gemini, then package prototypes into small applets or demos.
- Custom fine-tuned models for domain tasks: Fine-tune Gemini models on proprietary datasets (text, images) to improve performance on customer support, legal summarization, or specialized classification.
- Multimodal interactive apps: Build applications that combine video/audio/image understanding with text reasoning (e.g., video event exploration, spatial mapping with embedded maps) using starter applets and LiveAPI.
- Tool-enabled assistants: Create assistants that execute functions, call external APIs (like Maps Embed), run code, and ground answers with Google search or other tools for accurate, actionable outputs.
- Media generation and content creation: Generate and edit images or short video snippets using integrated media models (Imagen, Veo) for marketing, creative workflows, or automated asset creation.
- Enterprise deployment via Vertex AI: Move prototypes from Studio into managed, scalable production deployments on Google Cloud Vertex AI for enterprise-grade reliability and compliance.
- Rapid prototyping of LLM-powered apps and agents
- Fine-tuning base models for domain-specific tasks
- Building spatially-aware applications (2D/3D reasoning, video event exploration)
- Integrating LLMs with external services (maps, embeds, other APIs) via function calling
- Educational tutorials and starter projects for developer onboarding
- Local development and testing of Gemini-powered frontend apps
Laguna by Poolside
Poolside
Poolside's family of open Mixture-of-Experts foundation models for agentic coding — XS.2 runs locally, M.1 reaches 72.5% on SWE-bench Verified.
Key features
- Two Model Sizes: Laguna XS.2 (33B total / 3B active) and Laguna M.1 (225B total / 23B active) target different latency and capability needs.
- Mixture-of-Experts Architecture: Routes each token through a subset of experts for efficiency at large scale.
- Local Deployment: XS.2 is small enough to run on a Mac with 36 GB of RAM via Ollama under an Apache 2.0 license.
- Strong SWE-bench Results: XS.2 hits 68.2% and M.1 reaches 72.5% on SWE-bench Verified.
- Bundled Coding Agent: Ships 'pool,' a lightweight terminal-based coding agent.
- Agent Client Protocol: Includes a dual ACP client-server used internally for agent RL training and evaluation.
Best for
- Local Agentic Coding: Running XS.2 on a laptop for private, offline code generation and editing.
- High-Capability Code Tasks: Using M.1 for harder, long-horizon software engineering work.
- Self-Hosted Deployments: Building on open weights to avoid third-party API dependencies.
- Research & Fine-Tuning: Adapting permissively licensed weights for custom coding workflows.
- Benchmarking: Evaluating agentic coding performance against SWE-bench Verified and Pro.