BerriAI/litellm - GitHub vs Dify
Side-by-side comparison · Updated October 2026
| Description | LiteLLM is an AI gateway and Python SDK from Berrie AI Incorporated, published in the BerriAI GitHub repository. The SDK provides a common interface for model calls inside Python applications. The proxy gateway centralizes access for a team, with virtual keys, model routing, spend tracking, budgets and an administration interface. The official documentation lists support for more than 100 model providers. Supported endpoints and features vary by integration, so verify your model’s streaming, tool-calling, image, audio or embedding requirements. The router supports retries, fallbacks and load balancing; observability integrations can send request data to tools such as Langfuse, LangSmith and OpenTelemetry. LiteLLM also provides an MCP gateway. It can connect upstream servers using Streamable HTTP, SSE or stdio, expose tools through a fixed gateway endpoint, and scope access by key, team or organization. This requires configuring the upstream servers and authentication; the gateway does not automatically grant access to third-party tools. Agent-to-agent integrations are documented separately. The open-source offering has no software license fee for self-hosting. Code outside the enterprise directory is MIT-licensed, while enterprise code has separate terms. Enterprise pricing is quoted by annual gateway request capacity, deployment architecture and support needs, rather than a per-token license charge. Model-provider charges and infrastructure costs still apply. Enterprise adds controls and support such as SSO, SCIM, audit logs and service-level agreements. Compare New API for another self-hosted gateway with provider-channel management and usage accounting. Evaluate a representative workload, inspect request logging and secret handling, test budget and failure behavior, and decide whether SDK integration or a shared gateway best fits your application. | Dify is a tool for buyers evaluating whether it fits a specific AI workflow. Dify is an open-source platform for developing large language model (LLM) applications. It provides capabilities for building agents, orchestrating AI workflows, model management, and RAG (Retrieval Augmented Generation). The platform is more production-ready than LangChain. The capabilities to test first are Dify Orchestration Studio, RAG Pipeline, Prompt IDE, Enterprise LLMOps, BaaS Solution. Those details matter because they determine whether Dify can reduce manual work, replace tool switching, or produce reliable output without constant cleanup. Best-fit users include AI Developers, Enterprise Teams, Prompt Engineers, Data Scientists. A useful pilot should include a normal task, an edge case, and a recovery test so the team can see what happens when the first attempt is incomplete. Pricing is listed as Freemium, with plan information currently shown as Sandbox Plan, Professional Plan. Confirm current limits, credits, seats, cancellation rules, and commercial terms on the official website before relying on this listing for budget decisions. Before adopting Dify, compare it with adjacent tools in the same category. Measure setup time, output quality, data handling, collaboration controls, exports, and whether non-technical users can repeat the workflow without heavy prompting. The strongest buying signal is not feature count; it is whether Dify consistently completes the exact job the buyer needs with fewer manual handoffs. If sensitive customer, financial, or internal data is involved, review privacy and retention policies before production use. A final buying check for Dify should include a hands-on trial with real inputs, not only vendor screenshots or directory copy. Document the prompt, source files, output, cleanup time, and any errors so the team can compare Dify against another option on equal terms. If the product will be used by a team, test permissions, workspace sharing, exports, notifications, and whether results stay consistent across multiple users. For regulated or customer-facing work, review security claims, data retention, admin controls, and support response expectations before a wider rollout. This page should help narrow the shortlist, but the final decision should come from a practical workflow test and current pricing details from the official website. Evaluate Dify with the exact browser, files, integrations, or collaboration process the team expects to use every week, because small setup gaps often become major adoption blockers. If Dify replaces an existing workflow, capture the baseline time and quality first, then compare the new process after at least several repeated attempts rather than a single successful demo. Check how easy it is to stop using Dify: exports, account cancellation, data removal, and migration paths matter when a tool becomes part of daily work. |
| Category | Developer Tools | No-Code |
| Rating | No reviews | No reviews |
| Pricing | Open Source | Freemium |
| Starting Price | N/A | $59/mo |
| Plans | — |
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| Use Cases |
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| Tags | AI gatewayPython SDKLLM routingMCP gatewayvirtual keys | open-sourceplatformdevelopinglarge language modelLLM |
| Features | ||
| Python SDK for direct application integration | ||
| Shared AI proxy gateway and administration UI | ||
| More than 100 documented model-provider integrations | ||
| Virtual keys, users, teams, budgets and rate limits | ||
| Spend tracking and observability integrations | ||
| Router retries, fallbacks and load balancing | ||
| MCP gateway for Streamable HTTP, SSE and stdio upstreams | ||
| Key, team and organization MCP permissions | ||
| Separate enterprise identity, audit and support features | ||
| Dify Orchestration Studio | ||
| RAG Pipeline | ||
| Prompt IDE | ||
| Enterprise LLMOps | ||
| BaaS Solution | ||
| LLM Agent | ||
| Workflow orchestration | ||
| Production-ready | ||
| User-friendly | ||
| LangSmith and Langfuse integration | ||
| View BerriAI/litellm - GitHub | View Dify | |
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