Mastra vs Dynamiq
A side-by-side comparison to help you choose the right tool.
80
Mastra scores higher overall (80/100)
But the best choice depends on your specific needs. Compare below.
| Feature | Mastra | Dynamiq |
|---|---|---|
| Our score | 80 | 72 |
| Pricing | Fully open source under MIT license. No cloud fees. Self-hosted on your own infrastructure. | Free tier available. Enterprise and on-premise plans priced via sales demo. |
| Free plan | Yes | Yes |
| Best for | TypeScript and Node.js developers who want a structured, production-ready agent framework, Teams building internal AI copilots or customer-facing assistants with full code control, Startups embedding AI capabilities into products that need evals and tracing from day one | Enterprise teams with data residency or compliance requirements (HIPAA, SOC 2, GDPR), Engineering teams that want a full-stack alternative to assembling LangChain, a vector DB, and deployment infra separately, Organizations that need on-premise or air-gapped AI deployment |
| Platforms | api, web | web, api, on-premise, aws, azure, gcp |
| API | Yes | Yes |
| Languages | en | en |
| Pros |
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| Cons |
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| Visit site | Get started |
Mastra
80
- Pricing
- Fully open source under MIT license. No cloud fees. Self-hosted on your own infrastructure.
- Free plan
- Yes
- Best for
- TypeScript and Node.js developers who want a structured, production-ready agent framework, Teams building internal AI copilots or customer-facing assistants with full code control, Startups embedding AI capabilities into products that need evals and tracing from day one
- Platforms
- api, web
- API
- Yes
- Languages
- en
Dynamiq
72
- Pricing
- Free tier available. Enterprise and on-premise plans priced via sales demo.
- Free plan
- Yes
- Best for
- Enterprise teams with data residency or compliance requirements (HIPAA, SOC 2, GDPR), Engineering teams that want a full-stack alternative to assembling LangChain, a vector DB, and deployment infra separately, Organizations that need on-premise or air-gapped AI deployment
- Platforms
- web, api, on-premise, aws, azure, gcp
- API
- Yes
- Languages
- en
80Choose Mastra if:
- You are TypeScript and Node.js developers who want a structured, production-ready agent framework
- You are Teams building internal AI copilots or customer-facing assistants with full code control
- You are Startups embedding AI capabilities into products that need evals and tracing from day one
- You want to start free
72Choose Dynamiq if:
- You are Enterprise teams with data residency or compliance requirements (HIPAA, SOC 2, GDPR)
- You are Engineering teams that want a full-stack alternative to assembling LangChain, a vector DB, and deployment infra separately
- You are Organizations that need on-premise or air-gapped AI deployment
- You want to start free
FAQ
- What is the difference between Mastra and Dynamiq?
- Mastra is open-source typescript framework for building production-ready ai agents and multi-step workflows, with a local studio ui, typed zod schemas, built-in evals, and support for suspend/resume human-in-the-loop flows. Dynamiq is end-to-end platform for building, deploying, and monitoring ai agents and genai workflows with a visual canvas, rag pipelines, llm fine-tuning, and on-premise deployment for enterprise teams.
- Which is cheaper, Mastra or Dynamiq?
- Mastra: Fully open source under MIT license. No cloud fees. Self-hosted on your own infrastructure.. Dynamiq: Free tier available. Enterprise and on-premise plans priced via sales demo.. Mastra has a free plan. Dynamiq has a free plan.
- Who is Mastra best for?
- Mastra is best for TypeScript and Node.js developers who want a structured, production-ready agent framework, Teams building internal AI copilots or customer-facing assistants with full code control, Startups embedding AI capabilities into products that need evals and tracing from day one.
- Who is Dynamiq best for?
- Dynamiq is best for Enterprise teams with data residency or compliance requirements (HIPAA, SOC 2, GDPR), Engineering teams that want a full-stack alternative to assembling LangChain, a vector DB, and deployment infra separately, Organizations that need on-premise or air-gapped AI deployment.