AssemblyAI vs BerriAI/litellm - GitHub
Side-by-side comparison · Updated October 2026
| Description | AssemblyAI provides comprehensive Speech-to-Text and Audio Intelligence services, including streaming transcription, key phrase detection, sentiment analysis, summarization, PII redaction, and more. With competitive pricing and the ability to cater to large-scale enterprise solutions, this platform stands as a leader in leveraging voice data for diverse applications. | 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 | Speech-To-Text | Developer Tools |
| Rating | No reviews | No reviews |
| Pricing | Paid | Open Source |
| Starting Price | $0.37 | N/A |
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| Tags | Speech-to-TextAudio Intelligencestreaming transcriptionkey phrase detectionsentiment analysis | AI gatewayPython SDKLLM routingMCP gatewayvirtual keys |
| Features | ||
| Pay-as-you-go pricing with savings on committed usage | ||
| Streaming speech-to-text with <600 ms latency | ||
| Support for 17+ languages and 1.1 million training hours | ||
| High transcription accuracy >90% | ||
| Sentiment analysis, summarization, and PII redaction | ||
| Customizable vocabulary and spelling | ||
| Comprehensive audio intelligence models | ||
| LeMUR for sophisticated insights from voice data | ||
| Enterprise-level scalability and support | ||
| EU Data Residency compliance | ||
| 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 AssemblyAI | View BerriAI/litellm - GitHub | |
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