AI Tools 99 vs BerriAI/litellm - GitHub
Side-by-side comparison · Updated October 2026
| Description | AI Tools 99 is a revolutionary platform aimed at democratizing access to AI technologies by simplifying the deployment and customization of open-source AI models. Its user-friendly interface and cost-effective, scalable GPU resources allow users without extensive technical knowledge to effectively engage with AI for tasks like data analysis, automation, and creative projects. The platform supports a pay-per-use pricing structure, billing per second of GPU runtime, which reduces costs for projects with fluctuating demands. With the ability to fine-tune models for specific needs, AI Tools 99 offers significant benefits to businesses, creatives, and researchers seeking an adaptable AI environment. | 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. |
| Category | AI Platform | Developer Tools |
| Rating | No reviews | No reviews |
| Pricing | Freemium | Open Source |
| Starting Price | Free | N/A |
| Plans |
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| Tags | AI platformopen-source AI modelsGPU resourcesdata analysisautomation | AI gatewayPython SDKLLM routingMCP gatewayvirtual keys |
| Features | ||
| Run and fine-tune open-source AI models | ||
| Pay-as-you-go pricing model with billing only for GPU runtime | ||
| User-friendly interface accessible for those with limited machine learning expertise | ||
| Provides a variety of popular AI models | ||
| Various pricing plans available, including free and paid tiers with scaling credits and features | ||
| Zero scaling during idle periods for cost-effectiveness | ||
| Option to recommend favorite models | ||
| 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 | ||
| View AI Tools 99 | View BerriAI/litellm - GitHub | |
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