Continue vs Rasa
Comprehensive side-by-side comparison — features, pricing, performance, and more.
Updated May 2026
Trust & Reliability
Rasa
Customers (per vendor):Autodesk, Groupe IMA, Swisscom +7
GDPR
Overall Winner: Rasa
7.5/10 vs Continue at 6.8/10
Leads in:
Customizability
Output Quality
Support
AI Comparison Summary
Continue
6.8
avg score
Rasa
7.5
avg score
Rasa leads overall.
Based on 6 dimensions
How scores are calculatedOverall Winner
Rasa
7.5 / 10
Continue
Best For
Automating code quality enforcement for development teams
Maintaining consistent engineering standards across projects
Streamlining pull request reviews with AI-suggested fixes
Designing and deploying custom AI agents for code analysis
Not Ideal For
Creative content generation (e.g., marketing copy, images)
Non-technical users without coding or CI/CD pipeline knowledge
General purpose AI chatbot interactions
Rasa
Best For
Building high-trust AI agents for customer support
Automating sales enablement processes
Enhancing operational efficiency with internal AI agents
Developing secure and compliant AI experiences for regulated industries
Not Ideal For
Non-technical users seeking a simple, out-of-the-box chatbot solution
Individuals or small teams without development resources for self-hosting
Users looking for a fully managed SaaS solution without any infrastructure involvement
* Verdict is based on our algorithmic scoring of publicly available data. Learn about our methodology
Continue is best for
✍️Writing & Content7.0💬Chat & Conversation7.0🤖Automation & Agents7.0
Rasa is best for
✍️Writing & Content8.0💬Chat & Conversation8.0🤖Automation & Agents8.0
Filter by your use case:
Pros & Cons
ContinuePros
- Enforces specific, human-defined engineering standards with AI
- Provides suggested fixes directly within GitHub pull requests
- Scales with development factory speed, checking every PR automatically
- Offers centralized management and control over AI agents for teams
- Open-source CLI and IDE extensions available for local use
Cons
- Pricing is usage-based, which can be unpredictable for high-volume use
- Requires defining checks in markdown, which might have a learning curve
- Focuses on code quality and enforcement, not broad creative generation
- Limited information on specific AI models used, beyond "frontier models"
RasaPros
- Provides full control over AI agent behavior and performance
- Supports deployment on-premise or in private cloud for data sovereignty
- Offers a flexible development experience for technical and business teams
- Enables faster responses and cost-efficient operations compared to LLM-first frameworks
- Includes enterprise-grade security features like PII data management and secrets management
- Open-source core allows for extensive customization and community support
Cons
- Requires technical expertise for advanced customization and deployment
- Enterprise features and premium support require a paid subscription
- No explicit mention of a visual drag-and-drop interface for non-technical users in the core platform
- Limited to conversational AI use cases, not a general-purpose AI platform
- No explicit mention of specific LLM models included, users bring their own
Weighted Score — Adjust What Matters to You
Pick a profile or drag sliders — scores and radar update instantly on the right.
Quick profiles:
Ease of Use5
Output Quality5
Value for Money5
Customizability5
Support & Ecosystem5
Integration Power5
Dimension Comparison
Continue
6.8
/ 10
Rasa
7.5
/ 10
Your pick
Dimension Breakdown
Ease of Use
AIHow easy it is to get started and use daily
Continue
6.0
Rasa
6.0
Output Quality
AIQuality and accuracy of generated results
Continue
7.0
Rasa
8.0▲ +1.0
Value for Money
AICost vs. features and output quality
Continue
6.0
Rasa
7.0▲ +1.0
Customizability
CalculatedAbility to tailor to specific needs
Continue
8.0
Rasa
9.0▲ +1.0
Support & Ecosystem
AIDocumentation, community, and support quality
Continue
7.0
Rasa
8.0▲ +1.0
Integration Power
CalculatedAbility to connect with other tools and workflows
Continue
7.0
Rasa
7.0
Calculated = derived from verified integration count and API/open-source signals. AI = LLM-assessed from public website content. Methodology
Task Performance
Continue
Task
Rasa
6.07.0
Code Completion
7.07.0
Bug Detection
5.08.0
Code Explanation
6.07.0
Refactoring
4.06.0
Test Generation
7.08.0
Multi Language Support
7.08.0
Context Understanding
5.07.0
Documentation Generation
6.07.0
Performance Optimization
7.08.0
Security Scanning
* Task scores (1–10) are algorithmically generated from publicly available data.
Data is sourced from public information and vendor websites. It may not always be up-to-date — verify pricing and features directly with the vendor before purchasing.Report inaccuracy
Frequently Asked Questions
Which is better, Continue or Rasa?
Both are strong options. Continue has a rating of 0.0/5 (0 reviews), while Rasa has a rating of 0.0/5 (0 reviews). The best choice depends on your specific use case, budget, and workflow requirements.
Is Continue cheaper than Rasa?
Continue starts at $3/month (freemium model). Rasa starts at free (open_source model). Use the Cost Calculator above for a detailed estimate.
What are the main differences between Continue and Rasa?
Continue — "Source-controlled AI checks on every pull request for software quality control.". Rasa — "Build AI agents you can trust to handle real world complexity". Key differences include their pricing models, platform support, integrations, and technical capabilities. See the Feature Comparison table above for a complete side-by-side breakdown.