AIBase vs BerriAI/litellm - GitHub

Side-by-side comparison · Updated October 2026

 AIBaseAIBaseBerriAI/litellm - GitHubBerriAI/litellm - GitHub
DescriptionAIBase is a transformative service designed for growth-stage businesses, aimed at creating strategic AI roadmaps to streamline operations, reduce costs, and gain industry dominance. With a choice of over 160+ hours of free video training or expert team assistance, AIBase ensures your AI strategy is developed within a week. Discover how to leverage the power of Generative AI and optimize business activities with AIBase's unique approach.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.
CategoryAI AssistantDeveloper Tools
RatingNo reviewsNo reviews
PricingFreemiumOpen Source
Starting PriceFreeN/A
Plans
  • Free — Free
—
Use Cases
  • Growth-stage businesses
  • Business leaders
  • DIY enthusiasts
  • Enterprises
  • Developers
  • Enterprises
  • Startups
  • Educational Institutions
Tags
AI strategybusiness growthoperationscost reductionindustry dominance
AI gatewayPython SDKLLM routingMCP gatewayvirtual keys
Features
Over 160+ hours of free video training
Expert team assistance for AI roadmap development
Guaranteed roadmap delivery within 1 week
Tailored for both DIY enthusiasts and businesses seeking consultation
Strategic frameworks to navigate the AI landscape
Generative AI applications to automate business activities
Responsive customer service with 24-hour response time
Suitable for growth-stage businesses and enterprises
Custom solutions blending with readily available AI tools
Resources aimed at overcoming potential AI complexities
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
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