Laguna by Poolside vs Ollama: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Laguna by Poolside and Ollama — 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.
Ollama
Ollama
A local-first runtime and tooling to run, manage, and integrate large language models on personal or self-hosted infrastructure.
Key features
- Local Model Runtime: Run and host large language models on a developer's machine or private server, enabling low-latency inference and data privacy compared with cloud-only offerings.
- API & CLI Management: Simple programmatic API and command-line tooling to create, start, stop, list, and manage models and chat sessions, streamlining development and deployment workflows.
- Model Library & Publishing: Includes a catalog of pre-built models and supports creating models via Modelfile and pushing/publishing models with namespace support for sharing or distribution.
- Web Search Augmentation: Built-in web search API to augment model context with up-to-date web results, reducing hallucinations and improving factual accuracy for time-sensitive queries.
- Cross-Platform Desktop App: Official desktop client (Windows/macOS/Linux) that connects to a local or remote Ollama server to provide a chat UI, message layout optimizations, and faster chat switching.
- SDKs & Community Integrations: Ecosystem libraries and community clients (examples in Elixir, .NET, Flutter) that simplify integration into applications and enable language-specific developer experiences.
- Performance Optimizations: Support for performance features like flash attention and BPE encoding improvements to accelerate inference and improve handling of tokenization edge cases.