BerriAI/litellm - GitHub vs Octocom
Side-by-side comparison · Updated November 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. | Octocom is a customer-support and sales platform for consumer businesses. Its current agents handle customer-service questions, product recommendations and operational actions across website chat, email, phone and social channels. Teams can connect storefront, help-desk, shipping, returns and messaging systems, set action limits and escalation rules, and ground replies in catalog, order and policy data. In the official USD selector reviewed on October 28, 2026, Basic starts at $100 per month for 0–100 conversations and Advanced starts at $200 for 200–350. Higher conversation bands cost more, while Scale uses custom pricing above 5,000 conversations. Basic and Advanced offer a free-trial route. OpenTools did not deploy an agent or independently validate resolution, sales or performance claims. |
| Category | Developer Tools | E-commerce |
| Rating | No reviews | No reviews |
| Pricing | Open Source | Paid |
| Starting Price | N/A | $100/mo |
| Plans | — |
|
| Use Cases |
|
|
| Tags | AI gatewayPython SDKLLM routingMCP gatewayvirtual keys | customer support AIsales agentse-commerce supportshopping assistantvoice agent |
| 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 | ||
| AI customer-service and shopping-consultant agents | ||
| Website chat, email, phone, social and messaging channels | ||
| Grounding in catalog, order, policy and knowledge-base data | ||
| Actions for orders, refunds, returns, swaps and other connected workflows | ||
| Human handoff with conversation context | ||
| Brand voice, refusal rules, action limits and escalation controls | ||
| Integrations for storefronts, help desks, shipping, returns and messaging systems | ||
| Analytics for conversation volume, resolution, handoffs, topics and sentiment | ||
| API, webhook and custom-action access on applicable plans | ||
| View BerriAI/litellm - GitHub | View Octocom | |
Modify This Comparison
Also Compare
Explore more head-to-head comparisons with BerriAI/litellm - GitHub and Octocom.