Google Labs vs Laguna by Poolside: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Google Labs and Laguna by Poolside — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Google Labs
Google's hub for discovering, trying, and learning about experimental AI tools, demos, and research from Google.
Key features
- Experiment Gallery: A curated collection of interactive AI experiments and demos that let users try prototype features in web-based experiences.
- Discoverability and Updates: Centralized listings and short descriptions that surface new research, tools, and technology updates from across Google's AI teams.
- Developer Links and Repositories: Directs users to associated code, GitHub repositories, or developer resources so engineers and researchers can inspect, reproduce, or extend experiments.
- Responsible AI Context: Presents information and guidance related to responsible use, safety considerations, and ethical context for showcased experiments.
- Hands-on Interaction: Web-accessible demos designed to let non-experts and practitioners interact with models and view outputs without local setup.
- Aggregation Across Teams: Brings together experiments from multiple Google groups and initiatives, making it easier to explore cross-team innovation in one place.
- Web-hosted experimental demos and interactive prototypes for exploring new ML capabilities
- Central discoverability portal linking to technical demos, documentation, and GitHub repositories
- Hands-on labs and codelabs covering Google Cloud integrations (Vertex AI, Dataplex, Cloud Storage, GKE)
- Educational lab content including step-by-step instructions, sample data, and code artifacts
- Links to GitHub projects and third-party apps (e.g., google-labs-jules, google-labs-code) for deeper integration or code access
- Some labs include infrastructure-as-code examples (Terraform) and command-line instructions for reproducibility
- Emphasis on responsible AI guidance and up-to-date experimental catalog
Best for
- Exploring New Capabilities: Try interactive demos to evaluate emerging Google AI features before adoption or integration into projects.
- Research Prototyping: Researchers review experiments and linked code to reproduce results, benchmark approaches, or spark new research directions.
- Developer Onboarding: Engineers follow linked repositories and resources to access sample code, reproduce experiments, and build integrations or prototypes.
- Teaching and Demonstration: Educators use web demos as classroom examples to illustrate modern AI techniques or to spark discussion about responsible AI.
- Product Discovery and Feedback: Product teams and early adopters interact with prototypes to provide feedback, inform product direction, or assess feasibility.
- Staying Informed: Practitioners and enthusiasts monitor Labs to keep up with Google's latest experiments, releases, and responsible AI guidance.
- Rapidly previewing and evaluating research prototypes and ML demos in a browser
- Learning and hands-on training via codelabs that demonstrate Google Cloud integrations
- Prototyping integrations that use Vertex AI, Cloud Storage, Dataplex, or GKE
- Exploring sample code and repos on GitHub to bootstrap production implementations
- Educators and learners using step-by-step labs to teach cloud and ML concepts
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.