Ana by TextQL vs BerriAI/litellm - GitHub
Side-by-side comparison · Updated October 2026
| Description | Ana is a privacy-first AI data analyst designed to analyze, summarize, and visualize your data efficiently. With Ana, users can upload any .csv file and ask data questions in plain English without the need for coding. The platform provides instant insights and visualizations, ensuring convenience and security. Ana prioritizes data privacy with enterprise-grade security protocols and guarantees that user data will not be sold, shared, or used to train AI models. Users can permanently delete their data at any time. | 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 | Data Analytics | Developer Tools |
| Rating | No reviews | No reviews |
| Pricing | Pricing unavailable | Open Source |
| Starting Price | N/A | N/A |
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| Tags | AI data analystdata analysisdata visualizationprivacysecurity | AI gatewayPython SDKLLM routingMCP gatewayvirtual keys |
| Features | ||
| Instant insights | ||
| Data privacy emphasized | ||
| No coding required | ||
| Enterprise-grade security | ||
| Visualizations in seconds | ||
| Supports CSV files | ||
| Permanent data deletion | ||
| Multifunctional for various roles | ||
| GDPR compliant | ||
| User friendly | ||
| 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 Ana by TextQL | View BerriAI/litellm - GitHub | |
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