LiteLLM is an innovative AI agent designed to streamline interactions with over 100 large language models (LLMs), providing a unified interface that simplifies the integration process for developers, data scientists, and businesses. This open-source toolkit is built to address the complexities of working with multiple LLMs by offering a robust set of features that enhance efficiency, scalability, and reliability.
Features
LiteLLM boasts a comprehensive set of features tailored to facilitate seamless integration and interaction with various large language models. Below is an overview of its key functionalities:
| Feature | Description |
|---|---|
| Unified API Interface | Consistent interaction with multiple LLMs through a single API, reducing the need for understanding individual APIs and authentication mechanisms. |
| Seamless Integration | Quick implementation in Python projects with minimal code, enabling rapid development and testing. |
| Support for Multiple Models | Flexibility to switch between various models such as GPT-3 and GPT-Neo without extensive code alterations. |
| Error Handling | Standardized error management that maps common exceptions to their OpenAI equivalents for easier troubleshooting. |
| Robust Retry and Fallback Logic | Automatic retries and fallback to other providers in case of errors, ensuring service continuity. |
| Load Balancing and Cost Tracking | Tools for managing resource allocation and tracking expenses related to multiple LLMs. |
| Observability Features | Includes logging and callback support for real-time monitoring and debugging capabilities. |
| Unified OpenAI Format | Ensures consistent output formatting across all models, simplifying data parsing and processing. |
Use Cases
LiteLLM can be utilized across a range of applications, including:
- Application Development: Integrate LLMs seamlessly into applications, enhancing development efficiency.
- Data Analysis: Utilize LLMs for tasks such as text generation and comprehension analysis.
- Customer Support Automation: Automate responses to common queries, improving customer service efficiency.
- Content Generation: Leverage advanced AI models to create high-quality written content.
- Research and Development: Facilitate experimentation with different LLMs, expediting R&D processes.
How to get started
To get started with LiteLLM, users can access the open-source toolkit via its official GitHub repository. The repository includes comprehensive API documentation and a Python SDK to assist with integration into existing projects. Developers are encouraged to explore the toolkit's features and functionalities by downloading it from GitHub and reviewing the documentation provided.
</section>
<section>
<h2>LiteLLM Pricing Overview</h2>
<p>The pricing for LiteLLM is structured around token-based costs, model-specific pricing, custom pricing options, and enterprise plans.</p>
<h3>Token-Based Pricing</h3>
<p>Costs are determined by the number of tokens processed in both input and output. Please refer to the <a href="https://litellm.com/pricing">LiteLLM API</a> for details on cost per token.</p>
<h3>Model-Specific Pricing</h3>
<p>Each model has its own pricing, available through the <a href="https://litellm.com/model-cost">model_cost function</a> in the LiteLLM API.</p>
<h3>Custom Pricing</h3>
<p>Users can define their own pricing by setting <code>input_cost_per_token</code> and <code>output_cost_per_token</code> in the <code>litellm_params</code>.</p>
<h3>Enterprise Plans</h3>
<ul>
<li><strong>Enterprise Basic</strong>: $500/month (cloud or self-hosted)</li>
<li><strong>Enterprise Premium</strong>: Custom pricing with enterprise support and custom SLAs</li>
</ul>
<h3>AWS Marketplace: LiteLLM LLM Gateway (Proxy Server)</h3>
<ul>
<li><strong>LiteLLM Enterprise</strong>: $30,000/year for all features under the LiteLLM Enterprise License</li>
</ul>