BerriAI/litellm - GitHub vs Deepnote AI
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. | Deepnote AI is a cutting-edge, cloud-based platform transforming data science workflows by integrating AI into interactive notebooks. It empowers data professionals by providing context-aware AI support, enhancing productivity and accessibility for both experts and non-experts. Key features include AI-powered code completion, natural language-driven code generation, and autonomous task management. It supports various use cases like data analysis and machine learning model development, making it a versatile tool for data engineering and educational purposes. Deepnote AI integrates with numerous databases and cloud services, ensuring a broad application scope, and stands out with its robust privacy controls and seamless AI integration. |
| Category | Developer Tools | Data Management |
| Rating | No reviews | No reviews |
| Pricing | Open Source | Freemium |
| Starting Price | N/A | Free |
| Plans | — |
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| Use Cases |
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| Tags | AI gatewayPython SDKLLM routingMCP gatewayvirtual keys | cloud-based platformdata scienceinteractive notebooksAI-powered code completionnatural language-driven code generation |
| 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 | ||
| Seamless AI integration within notebooks for contextual assistance. | ||
| AI-powered code completion and suggestions using Codeium. | ||
| Auto-generation of entire data notebooks from natural language prompts. | ||
| Natural language-driven code generation to convert analytical goals into executable code. | ||
| Code explanation and debugging features for concise code understanding and error resolution. | ||
| Assistance with data visualization creation and suggestion of relevant visualizations from data analysis. | ||
| Generation of SQL queries from natural language descriptions. | ||
| Features dedicated to editing, explaining, and fixing existing code. | ||
| Prioritization of security and privacy with RBAC and more. | ||
| Integration with various databases and cloud services for broad compatibility. | ||
| View BerriAI/litellm - GitHub | View Deepnote AI | |
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