Arcwise vs BerriAI/litellm - GitHub
Side-by-side comparison · Updated October 2026
| Description | Arcwise AI revolutionizes data analysis and reporting by integrating with Google Sheets and customizing AI models based on user data. From creating SQL queries to insightful visualizations, Arcwise AI simplifies complex tasks with no coding required, making data insights accesible to all. | 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 | data analysisreportingGoogle Sheets integrationAI modelsSQL queries | AI gatewayPython SDKLLM routingMCP gatewayvirtual keys |
| Features | ||
| Customizable AI models based on user data | ||
| Integration with Google Sheets | ||
| No coding or SQL knowledge required | ||
| Creation of charts and graphs for visualizations | ||
| Building of SQL queries, tables, and reports | ||
| Continuous improvement cycle with user feedback | ||
| Efficient data analysis with personalized insights | ||
| Support for various data warehouses and BI tools like Snowflake, BigQuery, Databricks | ||
| Easy installation with a Chrome extension | ||
| Limited beta version available | ||
| 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 Arcwise | View BerriAI/litellm - GitHub | |
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