LLM API Providers

This post provides an overview of the LLM API providers I use for my personal projects and research, detailing their key features and uses.

Google Gemini

The Gemini API by Google AI allows developers to integrate generative models into their applications, with support for multimodal inputs such as text, images, audio, and video.

Available Models

• Gemini 1.5 Flash: A balanced multimodal model suitable for diverse tasks, supporting inputs like audio, images, video, and text, with text-based outputs.
• Gemini 1.5 Flash-8B: Optimized for speed and cost efficiency, ideal for high-frequency tasks requiring lower computational resources.
• Gemini 1.5 Pro: Designed for complex reasoning tasks, handling multimodal inputs and providing enhanced performance for demanding applications.
• Gemini 1.0 Pro: Focused on natural language tasks, including multi-turn text and code chat, as well as code generation, accepting text inputs and generating text outputs.

Pricing

• Free Tier: Offers up to 1 million tokens of storage per hour for various models, providing essential access for content generation, initial testing, and small-scale projects.
• Paid Tier: Provides higher rate limits and access to advanced models like Gemini 1.5 Pro. For detailed pricing information, refer to the Gemini API Pricing page.

Getting Started

To use the Google Gemini API, the first step is to obtain an API key from Google AI Studio. This key is required to authenticate and access the API's features.

I am currently using the free tier for content generation tasks, such as summarizing content, which provides sufficient access for initial testing and smaller-scale applications.

Link(s):
Gemini API Documentation

Mistral AI

Mistral AI offers generative chat endpoints (using Mistral AI LLM models) and an embedding endpoint (Mistral-embed). The pricing is based on tokens used.

I've used the platform for the following projects:

• Retrieval-Augmented Generation (RAG) with LLM
• AI Agents

Link(s):
https://mistral.ai/news/la-plateforme/

OpenRouter

OpenRouter is a platform that allows users to access multiple LLM APIs through a unified interface, simplifying integration and usage. It supports a variety of models from different providers, enabling developers to switch between LLMs without significant code changes.

Key Features

• Unified API Access: OpenRouter provides a single API for accessing multiple LLMs, making it easier to experiment with different models.
• Model Flexibility: Developers can choose from a wide range of LLMs, including both open-source and proprietary models, depending on the project's needs.
• Token-Based Pricing: The pricing model is based on tokens, similar to other LLM providers, allowing for cost-effective usage and scaling as needed.

Getting Started

To start using OpenRouter, visit their website and sign up for an account. You can obtain an API key to start integrating with various LLMs through their unified platform.

I use OpenRouter primarily to test different models for research projects, as it provides an efficient way to compare performance across different LLMs.

Link(s):
OpenRouter Documentation

Groq

Groq provides hardware-accelerated solutions for running LLMs with high performance and efficiency. It offers a unique approach to model execution, focusing on reducing latency and increasing throughput, which makes it suitable for deploying LLMs in production environments.

Key Features

• High Performance: Groq's hardware accelerators are designed to optimize LLM workloads, delivering low latency and high throughput for intensive AI tasks.
• Scalability: The platform supports large-scale deployments, making it ideal for enterprise-level applications that require significant computational resources.
• Ease of Integration: Groq provides tools and APIs to simplify the integration of its hardware with popular machine learning frameworks, enabling developers to quickly adapt their models for improved performance.

Getting Started

To get started with Groq, visit their website and explore their documentation. You can contact their sales team to learn more about integrating Groq hardware into your AI infrastructure.

I've tried Groq's solutions for running inference on large models for some tests.

Link(s):
Groq Documentation

DeepSeek

DeepSeek is a high-performance LLM API provider offering scalable and cost-efficient solutions for natural language processing tasks. It supports conversational AI, code generation, and multimodal applications.

Available Models

• DeepSeek Chat: Optimized for multi-turn dialogues, delivering context-aware and coherent responses.
• DeepSeek Code: Specialized for code generation and debugging, supporting multiple programming languages.
• DeepSeek Vision: Integrates text and image inputs for tasks like visual question answering and image captioning.

Key Features

• Efficiency: Models are designed for low latency and high performance.
• Scalability: Suitable for both small-scale and enterprise-level applications.
• Customization: Options for fine-tuning models on proprietary data.

Pricing

• Free Tier: Limited access for testing and small-scale projects.
• Paid Tier: Enhanced access to premium models and increased token quotas. Details are available on the DeepSeek Pricing page.

Getting Started

Create an account on DeepSeek to generate an API key. The platform provides SDKs and documentation for easy integration.

I’ve used DeepSeek for code generation and RAG workflows.

Link(s):
DeepSeek Documentation

Enjoyed this post? Found it helpful? Feel free to leave a comment below to share your thoughts or ask questions. A GitHub account is required to join the discussion.