Laguna by Poolside vs scikit-learn: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Laguna by Poolside and scikit-learn — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
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.
scikit-learn
scikit-learn developers
Open-source Python library providing a consistent API for supervised and unsupervised machine learning, model selection, and preprocessing.
Key features
- Estimator API: A unified estimator interface (fit, predict, transform) across algorithms that simplifies swapping models, building pipelines, and writing generic code for training and inference.
- Extensive Algorithms: Implementations of common algorithms including linear models, SVMs, decision trees, random forests, gradient boosting, k-means, PCA, nearest neighbors, and more, optimized for ease of use and interoperability.
- Model Selection & Validation: Tools like GridSearchCV, RandomizedSearchCV, cross_val_score and a rich set of cross-validation splitters to perform robust hyperparameter tuning and evaluate model generalization.
- Pipelines & ColumnTransformer: Utilities to chain preprocessing and modeling steps into reproducible pipelines, include column-wise transforms, and ensure correct application of transforms during cross-validation and deployment.
- Preprocessing & Feature Engineering: Scalers, encoders, imputers, polynomial feature generators, and feature selection methods to prepare data for modeling and improve pipeline performance.
- Ensemble Methods & Meta-Estimators: Built-in ensemble learners (bagging, boosting, stacking) and meta-estimators for combining models or enhancing stability and performance.
- Sparse & Efficient Data Handling: Support for dense and sparse matrix representations, integration with NumPy/SciPy, and optimized implementations for large-scale datasets where applicable.
- Comprehensive Documentation & Examples: Extensive user guide, API reference, tutorials, and example notebooks that facilitate learning, reproducible research, and adoption in education and industry.
- Wide collection of supervised algorithms (e.g., linear models, SVMs, tree-based models, ensemble methods)
- Unsupervised learning algorithms (e.g., clustering, dimensionality reduction, manifold learning)
- Consistent Estimator API with fit/predict/transform methods
- Model selection utilities: cross-validation, grid/search CV, scoring metrics
- Preprocessing and feature engineering tools (scaling, imputation, encoding)
- Pipeline composition and model persistence utilities
- Built-in datasets and data loading helpers for quick experimentation
- Interoperability with NumPy, SciPy, pandas and Jupyter notebooks
- Installable via pip and conda-forge; source available on GitHub
- BSD-3-Clause open-source license
Best for
- Rapid prototyping of predictive models: Use scikit-learn’s consistent API and built-in algorithms to quickly iterate on classification or regression models for tasks like churn prediction or price forecasting.
- Model benchmarking and algorithm selection: Compare multiple algorithms and hyperparameter configurations with cross-validation and GridSearchCV/RandomizedSearchCV to identify the best-performing approach.
- Preprocessing pipelines for production: Build robust Pipelines and ColumnTransformer workflows for preprocessing (imputation, encoding, scaling) and model training that can be serialized and deployed.
- Clustering and segmentation: Apply k-means, DBSCAN, hierarchical clustering and dimensionality reduction (PCA, t-SNE wrappers) for customer segmentation, anomaly detection, or exploratory data analysis.
- Feature engineering and selection: Use transformers and feature selection methods to construct, evaluate, and select informative features for model improvement and interpretability.
- Education and research: Leverage clear documentation, example notebooks, and a stable API to teach machine learning concepts, reproduce experiments, and implement baseline models for academic studies.
- Prototyping and benchmarking classical ML models for tabular and structured data
- Teaching and learning ML concepts through consistent APIs and example notebooks
- Feature preprocessing and pipeline assembly for production workflows
- Model selection and evaluation using cross-validation and standardized metrics
- Comparative benchmarks across ML implementations using scikit-learn_bench and related tools