AI & DataDify
Build and deploy LLM-powered apps without writing backend infrastructure.
Dify is an open-source LLM application development platform that sits at the intersection of AI tooling and developer productivity. It is used by engineering teams at startups and mid-size companies who want to ship AI-powered features, internal tools, or customer-facing chatbots without building and maintaining the full orchestration stack themselves. The core problem Dify solves is the gap between a working prototype in a notebook and a production-grade AI application with proper prompt management, model routing, observability, and user access controls. Dify provides a visual prompt orchestration IDE, support for multiple LLM providers (OpenAI, Anthropic, Azure, local models), a built-in RAG pipeline for connecting your own documents and data sources, and an API layer you can call from any application. Teams that previously stitched together LangChain, vector databases, and custom API routes often find that Dify consolidates those layers into a single managed workflow.
Engineering and product teams at startups and growth-stage companies who are actively building or planning to build LLM-powered features, internal tools, or AI agents. The right moment to adopt Dify is when you have a working OpenAI API prototype but are starting to feel the pain of managing prompt versions, tracking costs, connecting retrieval, and wiring everything together in production.
20% off monthly Professional and Team plans for 6 months
Subject to partner eligibility criteria. Savings estimates reflect maximum potential value.
Difyin depth.
Visual Prompt Orchestration
Dify's drag-and-drop workflow editor lets you chain prompts, conditionals, tool calls, and data retrievals visually. This gives non-engineers on the team visibility into how an AI feature works without reading code, and lets engineers iterate on logic faster than in pure code.
Built-in RAG Pipeline
Dify handles document ingestion, chunking, embedding, and retrieval out of the box so you can ground your LLM on internal knowledge bases, support docs, or product data. You connect a data source, configure retrieval settings, and the pipeline is immediately available to any app you build on the platform.
Multi-Model Provider Support
Switch between OpenAI, Anthropic Claude, Mistral, Azure OpenAI, local Ollama models, and others from a single config panel. This prevents lock-in and lets you route different tasks to the most cost-effective or capable model for that job.
Observability and Logging
Every LLM call is logged with the full prompt, completion, token counts, latency, and user session context. This makes debugging hallucinations and optimizing prompts in production tractable in a way that raw API calls never are.
API and Webhook Layer
Every app you build in Dify automatically exposes a REST API, so your frontend or backend can call it without coupling to specific LLM provider SDKs. You can also publish apps as standalone chatbot UIs for internal use or customer deployment.
Dify integrates with OpenAI, Anthropic, Azure OpenAI, Hugging Face, Pinecone, Weaviate, PostgreSQL with pgvector, Slack, and custom HTTP tools via its tool plugin system. In a typical software team's stack it sits between the LLM provider layer and the product layer, replacing the custom glue code that teams otherwise write with LangChain or raw SDK calls.
Commonuse cases.
Building an internal knowledge assistant on company documentation
A team uploads their product docs, runbooks, and SOPs into Dify's knowledge base and wires them to a chat interface via the RAG pipeline. Support staff or new hires get accurate, cited answers to operational questions without hunting through wikis.
Shipping a customer-facing AI feature without managing LLM infrastructure
An engineering team uses Dify to orchestrate prompt chains and model calls behind a published REST API endpoint, then connects it to their product frontend. They skip building prompt versioning, token tracking, and fallback logic from scratch.
Iterating on prompt logic with non-technical stakeholders
Product managers or content teams use the visual workflow editor to adjust prompt behavior, test variations, and review logs without needing to touch code or open a terminal. Engineers stay focused on higher-leverage work while prompt quality improves faster.
Three stepsto activate.
Check eligibility
Each partner maintains independent qualification criteria. We assess your profile and determine which offers you qualify for.
Schedule a briefing
Book a call with our partnerships team to discuss your stack requirements and walk through the activation process.
Activate credits
Once approved by the partner, credits are deployed to your account. Timelines vary by partner.
BearPlex maintains partnerships with leading technology providers to facilitate access to exclusive programs for our clients. All offers are subject to each partner's independent eligibility requirements, approval processes, and terms of service. Savings figures represent maximum potential value and may vary based on qualification, usage, and partner-specific criteria. BearPlex acts as a facilitation partner and does not guarantee approval or specific credit amounts. Offer availability and terms may change at the partner's discretion.
