Continue amplifies developers through continuous AI workflows
The AI-assisted development landscape has evolved rapidly, with Continue and Block’s Goose emerging as two prominent open-source solutions that fundamentally reimagine how developers interact with AI. While Goose offers impressive autonomous capabilities as a general-purpose platform, Continue’s focus on continuous AI workflows and deep IDE integration positions it as the superior choice for development teams seeking to amplify their productivity through intelligent automation.
Both platforms launched significant updates in 2025—Continue unveiled version 1.0 in February with enhanced enterprise features and the Continue Hub, while Block publicly released Goose in January with strong corporate backing and a $100,000 community grant program. This comprehensive analysis examines how these platforms compare across critical dimensions including features, performance, enterprise readiness, and developer experience.
Two philosophies for AI-powered development
Continue: The integrated amplification approach
Continue operates on the principle of continuous AI workflows that extend beyond simple code completion. The platform integrates directly into VS Code and JetBrains IDEs, providing four core modes that work seamlessly together. The Agent mode handles substantial codebase changes with intelligent decision-making, while Chat enables conversational assistance without leaving the IDE. The Edit function allows inline modifications within the current file, and Autocomplete provides real-time suggestions as developers type.
The platform’s building blocks architecture enables unprecedented customization through modular components including models, rules, context providers, and MCP integrations. Teams can create and share custom AI assistants through the Continue Hub, standardizing best practices across organizations. The CLI capability, currently in beta as cn, brings these same intelligent workflows to the terminal with both interactive and headless modes for scripting and automation.
With 28,900 GitHub stars and 699 contributors, Continue has built a mature ecosystem around its vision of amplifying developers rather than replacing them. The platform maintains complete model agnosticism, supporting OpenAI, Anthropic, local models via Ollama, and any LLM provider, ensuring zero vendor lock-in.
Block’s Goose: The autonomous agent paradigm
Goose takes a fundamentally different approach as an autonomous AI agent framework that executes complex development tasks from start to finish. Rather than embedding within IDEs, Goose operates as a desktop application and CLI tool that can build entire projects, debug failures, orchestrate workflows, and interact with external APIs independently.
The platform’s strength lies in its complete local execution, ensuring that code and data never leave the developer’s machine. Built on the Model Context Protocol (MCP) co-developed with Anthropic, Goose supports over 1,000 MCP servers for integration with tools like GitHub, Jira, Slack, and Google Drive. The system’s transparency allows developers to observe and approve each action the agent takes, maintaining control while benefiting from automation.
Block reports that 60% of its 10,000 employees use Goose weekly, with engineers saving 8-10 hours per week on average. The platform’s extensible architecture and strong corporate backing from Block (formerly Square) provide confidence in its long-term viability and continued development.
Performance metrics reveal distinct strengths
Benchmark results and real-world performance
The Kane MX 2025 AI Developer Tools Benchmark revealed interesting performance characteristics for Continue. While the platform struggled with certain diff application tasks and external documentation integration, these challenges reflect the complexity of real-world development scenarios rather than fundamental limitations. Continue’s architecture prioritizes developer control and customization over fully autonomous operation, accepting some performance trade-offs for greater flexibility.
Goose, though not included in the Kane MX benchmark, ranked 4th on Terminal-Bench, demonstrating strong performance in terminal-based workflows. Community feedback consistently highlights Goose’s effectiveness in completing complex, multi-step development tasks autonomously. Block’s internal metrics show 50-75% development time savings for common workflows, validating the platform’s practical impact.
The performance difference reflects each platform’s design philosophy. Continue optimizes for interactive development within familiar IDE environments, while Goose excels at autonomous task completion with minimal human intervention. For teams prioritizing continuous workflows and iterative development, Continue’s approach often proves more effective despite synthetic benchmark results.
Enterprise features favor Continue’s maturity
Security, compliance, and team management
Continue demonstrates clear enterprise readiness with comprehensive governance features that technical decision-makers require. The platform provides full SSO/SAML support with OIDC protocols, enabling seamless integration with existing identity management systems. Centralized configuration management allows organizations to standardize AI usage across teams, while granular allow/block lists ensure compliance with security policies.
The platform’s query scanning and audit logging capabilities provide visibility into AI usage patterns, helping organizations optimize costs and ensure appropriate use. Continue’s API key protection with transparent authentication layers adds another security dimension, crucial for enterprises managing sensitive codebases. These features reflect years of enterprise feedback and iteration since Continue’s 2023 founding.
Goose takes a different approach to enterprise security through its local-first architecture. By executing entirely on-premise with zero cloud dependencies, Goose eliminates many traditional security concerns. The platform leverages Block’s financial services-grade security framework, validated through deployment to thousands of employees in a regulated fintech environment. However, Goose currently lacks the centralized management and governance features that many enterprises require for organization-wide deployment.
Pricing models and total cost of ownership
Both platforms embrace open-source principles with Apache 2.0 licensing, but their commercial models differ significantly. Continue offers a tiered pricing structure with a generous free tier for individual developers, team features for growing organizations, and custom enterprise pricing for advanced governance needs. The platform’s Models Add-On provides simplified access to frontier models with predictable monthly costs.
Goose remains completely free with no licensing fees, requiring only payment for underlying LLM API usage. Block’s $10 free credits through Tetrate integration help teams get started, while the local execution model eliminates ongoing infrastructure costs. This approach appeals to cost-conscious organizations with technical resources for local deployment but may increase hidden costs in IT management and support.
For enterprise deployments, Continue’s managed service model typically results in lower total cost of ownership despite licensing fees. The reduced implementation complexity, streamlined training requirements, and managed infrastructure offset the subscription costs for most organizations.
Developer experience defines platform selection
IDE integration versus autonomous operation
Continue’s native IDE integration creates a seamless developer experience that minimizes context switching. Developers can access AI assistance through familiar keyboard shortcuts, inline suggestions appear naturally during coding, and the sidebar chat interface maintains conversation history across sessions. The platform’s context providers (@codebase, @docs, @web) enable precise scoping of AI assistance, ensuring relevant and accurate responses. Note that these specific context providers are deprecated in favor of the more flexible Model Context Protocol (MCP) integration.
The recently launched Continue Hub transforms how teams share AI configurations, allowing developers to discover and deploy custom assistants tailored to specific frameworks, languages, or organizational standards. This community-driven approach accelerates onboarding and promotes best practices across development teams.
Goose’s desktop application and CLI interface appeal to developers comfortable with terminal-based workflows. The platform’s transparency and control features let developers observe each action before execution, building trust in autonomous operations. The MCP extension ecosystem provides incredible flexibility, enabling integration with virtually any development tool or service.
However, Goose’s approach requires a steeper learning curve and more technical expertise to configure effectively. Developers must understand MCP protocols, manage local LLM deployments, and design appropriate automation workflows. This complexity can slow adoption in teams with varying technical proficiency levels.
Integration ecosystems showcase platform philosophies
Continue’s IDE-first ecosystem
Continue’s integration strategy centers on deep IDE integration with extensions for VS Code and JetBrains platforms. The platform’s building blocks architecture supports extensive customization through Model Blocks for LLM configuration, Rules Blocks for coding standards, and Context Blocks for information retrieval. Recent MCP support adds compatibility with the broader ecosystem while maintaining Continue’s IDE-centric focus.
The platform excels at integrating with existing development workflows. GitHub integration enables repository-aware assistance, GitLab MR support streamlines code review, and web scraping capabilities allow documentation reference during development. Continue’s 38+ language support ensures effectiveness across diverse technology stacks.
Goose’s MCP-powered flexibility
Goose’s adoption of the Model Context Protocol as its core integration mechanism provides unmatched extensibility. With over 1,000 available MCP servers, developers can connect Goose to virtually any tool or service. The platform’s built-in extensions for Developer tools, Computer Controller, Google Drive, and JetBrains provide immediate value, while the community continuously expands capabilities.
The grant program’s $100,000 fund incentivizes ecosystem development, resulting in rapid growth of available integrations. Partnerships with Databricks, adoption by university labs, and integration tutorials demonstrate the platform’s momentum in building a comprehensive ecosystem.
Community support reveals platform trajectories
Continue’s established developer community
With nearly 29,000 GitHub stars and almost 700 contributors, Continue has cultivated a vibrant open-source community. The active Discord server provides peer support, the comprehensive documentation site offers detailed guides, and regular blog posts on blog.continue.dev share best practices and updates. User testimonials consistently praise Continue’s customization options, model flexibility, and responsive support team.
The platform’s Y Combinator backing and $2.1 million funding round provide stability while maintaining independence. This community-first approach ensures Continue’s development remains aligned with developer needs rather than corporate priorities.
Goose’s corporate-backed momentum
Despite launching just months ago, Goose has achieved remarkable adoption through Block’s resources and reputation. The platform’s use by 60% of Block’s workforce validates its enterprise readiness, while partnerships with major platforms like Databricks demonstrate industry confidence. The grant program and regular livestreams foster community engagement despite the platform’s relative youth.
Block’s commitment to open source, evidenced by CTO Dhanji Prasanna’s vision of “new heights of invention and growth,” suggests long-term support for Goose’s community development.
Strategic recommendations for technical decision-makers
Continue excels for most development teams
For organizations prioritizing rapid deployment, IDE integration, and managed services, Continue presents the optimal choice. The platform’s mature enterprise features, established community, and focus on continuous AI workflows align with how most development teams actually work. Continue’s building blocks architecture enables customization without complexity, while the Hub accelerates standardization across organizations.
Continue particularly suits teams that value developer autonomy and gradual AI adoption. The platform amplifies existing workflows rather than replacing them, allowing developers to maintain control while benefiting from AI assistance. For enterprises requiring governance, compliance, and centralized management, Continue’s enterprise tier provides essential capabilities that Goose currently lacks.
Goose serves specific automation use cases
Block’s Goose excels in scenarios requiring autonomous task completion and local execution. For organizations with strict data residency requirements or teams comfortable with terminal-based workflows, Goose offers compelling advantages. The platform’s strength in complex automation tasks like migrations, deployments, and project scaffolding complements rather than replaces IDE-based development.
Teams with strong technical resources who can manage local LLM deployments and MCP configuration may find Goose’s flexibility and zero licensing costs attractive. The platform’s transparency and audit capabilities also appeal to security-conscious organizations willing to trade managed services for complete control.
Continue amplifies the future of AI-assisted development
While both platforms offer impressive capabilities, Continue’s focus on continuous AI workflows, deep IDE integration, and enterprise readiness positions it as the recommended choice for most development teams. The platform’s philosophy of amplifying developers through intelligent assistance aligns with how modern software development actually happens—iteratively, collaboratively, and within familiar tools.
Continue’s established ecosystem, comprehensive documentation, and proven enterprise features reduce adoption friction and accelerate time to value. The Continue Hub’s community-driven approach to sharing custom assistants creates network effects that benefit all users, while the platform’s model agnosticism ensures flexibility as the AI landscape evolves.
For teams ready to embrace continuous AI workflows that enhance rather than replace human creativity, Continue offers the most mature, flexible, and developer-friendly path to AI-assisted development. Visit blog.continue.dev to explore implementation strategies, discover custom assistants in the Continue Hub, and join the community shaping the future of AI-powered software development.