Dify vs Letta
Side-by-side comparison of two agent options that often come up together when people are choosing between self-hosted frameworks, managed assistants, and extensible AI tooling.
Open source139k stars
Dify
Production-ready platform for building and deploying agentic workflows with a visual interface
Open source22k stars
Letta
Platform for building stateful agents with advanced memory persistence
Category
Dify
Letta
Tagline
Production-ready platform for building and deploying agentic workflows with a visual interface
Platform for building stateful agents with advanced memory persistence
Deployment
Self-hosted / Managed cloud (Dify Cloud)
Self-Hosted
Pricing
Open source and self-hostable for free. Dify Cloud starts at $59/month for teams.
Usually affordable for individuals or small teams, with some recurring model or hosting costs.
Channels
Web, api, Slack, Teams
Telegram, Slack, Discord, WhatsApp, Signal
Open source
Yes
Yes
Privacy
Self-hosted deployment keeps data on your infrastructure. Dify Cloud sends data to Dify servers.
Good privacy posture for most teams, especially when self-hosted or carefully configured.
Dify pros
- Visual workflow builder lowers the barrier to building agentic apps.
- Production-ready with observability, versioning, and team collaboration.
- Supports RAG pipelines, tool calling, and multi-agent orchestration.
Letta pros
- Open source with transparent code and flexible deployment options.
- Strong privacy story for users who care where data runs.
- Good memory and persistence support for ongoing conversations or tasks.
Dify cons
- Heavier infrastructure than lightweight agent frameworks.
- Best suited for app builders, not researchers or coding agents.
- Managed cloud tier can get expensive at scale.
Letta cons
- Lower autonomy — designed more as a platform than an out-of-box assistant
- Setup requires understanding memory architecture concepts
- Python-only — no native TypeScript/JavaScript implementation
Dify gotchas
- Designed for building agent-powered apps, not for personal AI assistant use cases.
- Self-hosting requires Docker and some ops knowledge.
Letta gotchas
- You should expect ongoing hosting, uptime, and secret-management work if you deploy it for real users.
- Recurring subscription or model spend can matter more than the headline feature list.
Not sure which one fits you?
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