linkgo

Cohere vs Laguna by Poolside: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of Cohere and Laguna by Poolside — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

Cohere logo

Cohere

Cohere

Freemium

Enterprise-grade language models, SDKs, and tooling for building private, secure, and customizable NLP applications and RAG systems.

Key features

  • Multi-language SDKs: Official SDKs and client libraries for Python, TypeScript, Java, and Go enabling easy integration of Cohere endpoints into existing applications and workflows.
  • Prebuilt RAG Components: Cohere Toolkit includes ready-made connectors and components for retrieval-augmented generation (RAG) pipelines, standardizing document formats and accelerating grounded chatbot construction.
  • Streaming Chat & Generate Endpoints: Support for streaming responses in chat and generation APIs to enable low-latency interactive user experiences and progressive output consumption.
  • Embeddings & Semantic Search: Managed embeddings service for creating vector representations of text used for semantic search, similarity matching, and retrieval to back RAG systems.
  • Enterprise Controls & Privacy: Features and positioning focused on private, secure, and customizable deployments suitable for enterprise governance, data protection, and internal-use cases.
  • Developer Experience & Examples: Extensive docs, code snippets, Jupyter notebooks, and sample connectors (quick-start connectors repo) to speed prototyping and production adoption across cloud providers.
  • Cross-cloud Deployment Support: Guidance and tooling to use Cohere models on external cloud platforms (AWS, Azure, OCI) or Cohere-hosted environments to meet enterprise infrastructure requirements.
  • Model Tooling & Parsing: Tools and SDKs (e.g., Compass and parsing helpers in repos) to assist in model parsing, structured output extraction, and integration into downstream systems.
  • HTTP/REST API with published OpenAPI spec (cohere-openapi.yaml)
  • Official SDKs: Python, TypeScript, Java, Go (golang) and community/unofficial SDKs (e.g., Ruby gem)
  • Cohere Toolkit: prebuilt components for building and deploying RAG applications
  • Chat and generate endpoints with named models (example model: command-a-03-2025)
  • Streaming support for chat via chatStream / streaming endpoints
  • Client libraries expose error classes (CohereError, CohereTimeoutError) and typed clients (e.g., CohereClientV2)
  • Developer resources: code snippets, Jupyter notebooks, sample apps and GitHub repos
  • Supports usage on external cloud providers (AWS, Azure, OCI) as well as Cohere platform
  • Open-source examples and SDKs hosted on GitHub (cohere-ai organization)

Best for

  • Knowledge-centered Chatbots: Build internal or customer-facing chat assistants that use connector-fed documents and embeddings to provide accurate, grounded answers using RAG.
  • Semantic Search & Discovery: Index and embed large corpora (documents, FAQs, product content) to enable semantic search and relevance-ranked retrieval across enterprise data.
  • Document Summarization & Insight Extraction: Summarize long-form documents, extract structured insights (entities, actions, highlights) to streamline reporting and decision workflows.
  • Automating Internal Workflows: Generate draft emails, policy summaries, or triage support tickets by integrating generation endpoints into business process automation tools.
  • Developer Rapid Prototyping: Use SDKs, sample notebooks, and the developer-experience repository to prototype and validate language features quickly before productionizing.
  • Custom Private Deployments: Deploy tailored models and configurations with enterprise privacy and security considerations for sensitive internal data and regulated industries.
  • Build conversational agents and chatbots using chat and streaming endpoints
  • Implement Retrieval-Augmented Generation (RAG) workflows with Cohere Toolkit components
  • Automate enterprise workflows and document understanding to turn fragmented data into insights
  • Prototype and deploy LLM-powered features across multi-cloud environments (AWS, Azure, OCI)
  • Integrate model inference into backend services using official SDKs (Python, TypeScript, Java, Go)
View Cohere details
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