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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 logo

Laguna by Poolside

Poolside

Free

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.
View Laguna by Poolside details
Ollama logo

Ollama

Ollama

Freemium

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.
  • Local model runtime for running large language models on-device or on private servers
  • HTTP/REST API for inference, model info and management operations
  • Command-line interface (ollama CLI) for creating, running and pushing models
  • Support for GPU-accelerated inference (GPU docs available)
  • Library of pre-built community models and ability to create/push custom models
  • Client SDKs and community libraries (examples: .NET, Elixir, R, Python/JS)
  • Desktop/mobile frontends that connect to an Ollama API endpoint (Flutter app available)
  • Local-first privacy and on-prem deployment; optional model hosting via Ollama account/registry
  • Portable Linux executable for desktop app; standard desktop data locations

Best for

  • Privacy-preserving chatbots: Deploy conversational agents that run fully on a user's machine or on private infrastructure to keep data local and reduce exposure to third-party cloud providers.
  • Application integration: Integrate Ollama as an inference backend for web, mobile, or desktop apps using available SDKs (e.g., .NET, Elixir) to serve completions, summaries, or assistants.
  • Custom model development and distribution: Create models with Modelfile, test locally, and push to a namespace to share or deploy across machines or teams.
  • Augmented research and knowledge assistants: Use the web search augmentation to provide up-to-date information in assistants, reducing hallucinations for queries requiring recent facts.
  • Embedded chat UIs and clients: Connect the Ollama desktop or community chat UIs to a local server for a fast, offline-capable chat experience integrated into product workflows.
  • Multi-model experimentation: Run and orchestrate interactions between different models (e.g., conversational pipelines or model-vs-model experiments) for research and prototype scenarios.
  • Embedding a local LLM backend for chat UIs and chatbots (desktop, web, mobile)
  • Summarization extensions and browser sidebar summarizers (e.g., SpaceLlama)
  • Video/text summarization services (e.g., YouTube summarizer integrations)
  • Research and development with private or offline LLM inference
  • Multi-model experiments (e.g., dual-model conversations)
  • Integrating LLMs into enterprise on-premise systems requiring data locality
View Ollama details