Beam vs BerriAI/litellm - GitHub
Side-by-side comparison · Updated October 2026
| Description | Beam offers serverless infrastructure designed for Generative AI, enabling users to run GPU inference and training jobs efficiently. With features like autoscaling, fast cloud storage with storage volumes, and simple deployment commands, Beam simplifies the process of managing and scaling AI applications. The platform boasts fast cold start times, easy local debugging, and seamless integration with CI/CD pipelines to ensure smooth and reliable operations. Trusted by thousands of developers, Beam prioritizes performance, control, and reliability, making it an ideal solution for modern AI-driven projects. | 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 | Cloud Platforms for AI | Developer Tools |
| Rating | No reviews | No reviews |
| Pricing | Freemium | Open Source |
| Starting Price | $89/mo | N/A |
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| Tags | serverlessinfrastructureGPU inferencetraining jobsautoscaling | AI gatewayPython SDKLLM routingMCP gatewayvirtual keys |
| Features | ||
| Autoscaling to hundreds of GPUs | ||
| Fast cold start times | ||
| Serverless inference and training | ||
| Simple deployment commands | ||
| Built-in authentication and metrics | ||
| High-performance cloud storage | ||
| Local debugging capabilities | ||
| CI/CD pipeline integration | ||
| Active Slack community support | ||
| Developer-friendly environment | ||
| 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 Beam | View BerriAI/litellm - GitHub | |
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