BerriAI/litellm - GitHub vs Protection Guard
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. | Prediction Guard addresses AI challenges with rapidly deployed large language models (LLMs) in secure and private environments, supplemented by extensive safeguards. Their service targets enterprise-level needs by ensuring high AI accuracy and reliability. Key features include security checks for new vulnerabilities, privacy filters for hiding personal information, output validations to eliminate errors and offensive content, and data protections compliant with regulations such as HIPAA. By doing so, Prediction Guard seeks to surpass industry standards and offer robust, scalable solutions that preempt AI 'brokenness.' |
| Category | Developer Tools | AI Assistant |
| Rating | No reviews | No reviews |
| Pricing | Open Source | Pricing unavailable |
| Starting Price | N/A | N/A |
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| Tags | AI gatewayPython SDKLLM routingMCP gatewayvirtual keys | AIlanguage modelssecurityprivacycompliance |
| 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 | ||
| Secure, private LLM environments | ||
| Scalable model endpoints | ||
| Security checks for new vulnerabilities | ||
| Privacy filters for PII masking | ||
| Output validations to prevent hallucinations | ||
| Compliance with HIPAA and BAA | ||
| High AI accuracy and reliability | ||
| Robust safeguards | ||
| Seamlessly integrated infrastructure | ||
| Reduced AI budget expenditures | ||
| View BerriAI/litellm - GitHub | View Protection Guard | |
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