BerriAI/litellm - GitHub vs Laketool
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. | Laketool is an advanced AI experimentation platform designed to enable businesses to transform their data lakes into AI-driven insights. It allows users to run data analysis directly on their data lakes without the need for database maintenance, leveraging the power of parallel processing for fast results. With features like easy integration of AI models into business processes and the ability to update models effortlessly, Laketool is perfect for innovative and agile operations. Users can get started easily in three simple steps and take advantage of unique features like de-clouding, seamless team collaboration, and more. |
| Category | Developer Tools | Data Analytics |
| Rating | No reviews | No reviews |
| Pricing | Open Source | Pricing unavailable |
| Starting Price | N/A | N/A |
| Use Cases |
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| Tags | AI gatewayPython SDKLLM routingMCP gatewayvirtual keys | AI experimentationdata lakesdata analysisparallel processingbusiness |
| 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 | ||
| Run data analysis directly on data lakes without database maintenance | ||
| Automatically parallel processes for fast data analysis | ||
| Get AI-driven insights and predictions | ||
| Easy API integration of AI models into business processes | ||
| No additional cloud costs with de-clouding feature | ||
| Effortlessly update models based on new data in the data lake | ||
| Seamless team collaboration on AI projects | ||
| Accelerate innovation and drive business growth | ||
| User-friendly three-step setup process | ||
| Support available via blog and direct contact | ||
| View BerriAI/litellm - GitHub | View Laketool | |
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