AI Query vs BerriAI/litellm - GitHub
Side-by-side comparison · Updated October 2026
| Description | AI Query is a transformative tool designed to streamline the SQL query generation process. By leveraging advanced AI, users can effortlessly translate simple English queries into efficient SQL code, aiding both novices and experts in managing and understanding their databases more effectively. Moreover, AI Query offers an array of features tailored to enhance user experience, including support for multiple database engines, an intuitive dashboard for schema definition, and a unique SQL to English translator to demystify complex SQL scripts. With its accessible pricing plans, AI Query positions itself as a valuable asset for a wide range of data management needs. | 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 | SQL | Developer Tools |
| Rating | No reviews | No reviews |
| Pricing | Paid | Open Source |
| Starting Price | $10/mo | N/A |
| Plans |
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| Tags | SQLNatural Language ProcessingData ManagementProductivity | AI gatewayPython SDKLLM routingMCP gatewayvirtual keys |
| Features | ||
| Effortless English to SQL translation | ||
| Support for multiple database engines | ||
| Intuitive dashboard for database schema definition | ||
| SQL to English translator for easier understanding | ||
| Monthly and yearly pricing plans | ||
| Error-free SQL generation | ||
| Streamlined data management and understanding | ||
| Facilitated collaboration with saving and sharing features | ||
| Continuous support for new database engines | ||
| Accessible learning tool for SQL beginners | ||
| 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 Query | View BerriAI/litellm - GitHub | |
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