Mastra vs Inngest
Detailed side-by-side comparison to help you choose the right tool
Mastra
🔴DeveloperAI agent framework
Mastra is a TypeScript-first AI agent framework and platform for building production agents with workflows, memory, MCP, evals, observability, and deployment.
Was this helpful?
Starting Price
FreeInngest
🔴DeveloperAI workflow infrastructure
a durable execution platform for AI agents, workflows, background jobs, endpoints, queues, state, scheduling, and observability.
Was this helpful?
Starting Price
FreeFeature Comparison
Scroll horizontally to compare details.
💡 Our Take
Choose Mastra when you want agent, memory, eval, and deployment primitives together. Choose Inngest when the main problem is durable event-driven workflow infrastructure across many app tasks.
Mastra - Pros & Cons
Pros
- ✓Strong TypeScript fit for product teams already building in Next.js, Express, Hono, or similar JavaScript stacks
- ✓Combines framework, memory, workflows, evals, observability, and deployment instead of forcing teams to assemble every production feature separately
- ✓Apache 2.0 open-source framework gives teams a free self-hosted starting point before adopting the hosted platform
- ✓Public pricing includes useful operational limits such as observability events, CPU hours, retention, egress, and memory token usage
- ✓MCP support makes Mastra easier to connect with the growing ecosystem of agent tools and external capabilities
Cons
- ✗Developer-first framework; non-technical teams looking for a visual bot builder will likely move faster with Dify or a no-code platform
- ✗Usage-based overages for observability events, CPU time, egress, retrieval storage, and memory tokens require monitoring in production
- ✗Python-heavy teams may prefer OpenAI Agents SDK, Pydantic AI, or LangGraph rather than adding TypeScript to the agent stack
- ✗Production success still depends on careful eval design, tool permissions, security review, and rollback planning
- ✗Enterprise-grade controls such as RBAC, audit logs, dedicated SLAs, and VPC-style deployment are custom-priced rather than included in Starter
Inngest - Pros & Cons
Pros
- ✓Clear category fit with specific workflows to test
- ✓Concrete public evidence or staging data for key features
- ✓Can be piloted with measurable tasks before rollout
- ✓Has relevant alternatives for a realistic bake-off
Cons
- ✗Human review is still required for high-risk or customer-facing work
- ✗Teams must verify data retention, export rights, permissions, and support terms
- ✗Results depend on representative inputs and disciplined review
Not sure which to pick?
🎯 Take our quiz →Price Drop Alerts
Get notified when AI tools lower their prices
Get weekly AI agent tool insights
Comparisons, new tool launches, and expert recommendations delivered to your inbox.