Dify is an open-source LLM app development platform that combines a visual workflow builder, RAG pipelines, agent tools, and an LLMOps backbone.
Dify is an open-source LLM app development platform that combines a visual workflow builder, RAG pipelines, agent tools, and an LLMOps backbone.
Dify is an open-source LLM application platform built around the idea that most production AI apps need more than a chat completion call. A typical Dify app is a visual workflow: input variables → retrieval over a knowledge base → tool calls → LLM step → output. The platform supports OpenAI, Anthropic, Google, AWS Bedrock, Azure, Mistral, local Ollama, and dozens of other model providers behind a single abstraction, so teams can swap models without rewriting the pipeline. Dify ships an agent runtime with function calling, scheduled jobs, a marketplace of plugins and connectors, an embedded RAG engine with parent-child chunking and rerankers, prompt versioning, observability dashboards, and team collaboration. The Cloud version offers Sandbox (Free), Professional at $59/month, and Team at $159/month, with Premium at $590/month for higher-volume orgs and a custom Enterprise tier. The self-hosted Community Edition remains free under the Dify Open Source License, which is the path most teams take when data residency or per-message costs matter.
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Strong category fit for teams building real LLM products rather than one-off prompts. The main caution: Pricing could not be verified from the fetched pricing HTML, so buyers should confirm current plan costs before budgeting.
Free
$59/month
$159/month
$590/month
Custom (or free OSS)
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Through late 2025 and into 2026, Dify has expanded its agent capabilities with deeper multi-agent orchestration, parallel branch execution in workflows, and an enlarged plugin marketplace covering more SaaS connectors and code-execution sandboxes. The platform has added support for the latest reasoning models from major providers (including Claude 4 family, GPT-5-class models, Gemini 2.x, and DeepSeek V3/R1), improved structured output and JSON-mode handling, and introduced richer evaluation and dataset tooling for systematic prompt and agent testing. RAG has been upgraded with stronger hybrid retrieval, parent-child chunking strategies, and broader file-format support. Deployment ergonomics have also improved with cleaner Helm charts and more granular role-based access control on Team and Enterprise tiers.
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