AI SaaS: Beyond OpenAI APIs

AI SaaS: Beyond OpenAI APIs

Table of Contents

1. Introduction
2. Hosted API Solutions
   1. Replicate
   2. Hugging Faces Inference Endpoint
   3. Together AI
   4. Coher
   5. Perplexity PX APA
   6. Anthropics
3. Open Source Projects for Deploying Large Language Models
   1. TT Generation Web UI
   2. Sky Pilot
   3. Light LLN
   4. Text Generation Inference
   5. VM
   6. MLC
4. Optimizing Latency and Deployment
5. Conclusion

Introduction

In this article, we will explore various solutions for building AI SAS applications using open AI GP APIs or other methods. Whether You are looking for paid solutions or open source projects, this guide will provide you with recommendations to make your development process easier and more versatile. We will cover both hosted API solutions and open source projects that allow you to deploy your own managed API endpoints. By the end of this article, you will have a range of options to choose from Based on your specific needs and preferences. Let's get started!

Hosted API Solutions

Replicate

Replicate is a popular solution for building AI SAS applications, particularly for image generation. It offers a wide range of examples and pricing information to help you make informed decisions about your project. While it is primarily focused on image generation, Replicate can also be used for text generation with good results.

Hugging Faces Inference Endpoint

Hugging Faces is well-known for hosting models and datasets, but they also offer an inference endpoint for deploying your own models. With just a few clicks, you can deploy your model and access various pricing options based on the machine you choose. While Hugging Faces may not be the cheapest option available, it is reliable and trusted by many developers.

Together AI

Together AI is a new offering that provides fine-tuning and inference capabilities. Their support for the Llama family of models makes them a suitable choice for developers working with Llama models. They have received positive feedback for their speed and cost-effectiveness compared to other solutions like GPT-3.5 Turbo.

Coher

Coher is a question-answering and text completion endpoint that offers a free plan during prototyping. While their models may not be as capable as some other options, Coher provides a cost-effective solution for testing and deploying your models.

Perplexity PX APA

Perplexity offers a fast and cost-efficient inference endpoint, with lower latency compared to other solutions like Replicate. They have benchmarked themselves against replicating and claim to offer faster speeds. If speed is critical for your application, you may consider exploring Perplexity PX APA.

Anthropics

Anthropics is a capable model that provides a Context window of over 100K tokens, making it suitable for longer text generation tasks. However, it is currently only available on a waiting list basis and has limited availability outside of the United States. If you can access Anthropics, it is a powerful option to consider.

Open Source Projects for Deploying Large Language Models

TT Generation Web UI

TT Generation Web UI by Uba is one of the oldest and most popular text generation web user interfaces. It not only provides a UI but also offers an API server, allowing you to host and deploy your models. It supports a wide range of models, including quantized models and X-LAMA for enhanced inference speed.

Sky Pilot

Sky Pilot is an open source project that allows you to run large language models on any cloud provider. It simplifies the process of launching and managing job clusters across multiple clouds, maximizing GPU availability and reducing costs. Sky Pilot supports AWS, Oracle, Google Cloud, Samsung, and more.

Light LLN

Light LLN is a powerful open source project that provides compatibility with over 100 APIs, including many popular language models. It offers a unified API structure, allowing you to switch between different providers seamlessly. Light LLN is a versatile solution that gives you the flexibility to use any existing models or APIs.

Text Generation Inference

Text Generation Inference, formerly an open source project by Hugging Faces, is now maintained by Gradient AI. It supports quantized models, C translator, and Chart completion in open AI format. It provides one-click Docker deployment and supports various cloud providers, making it a comprehensive solution for deploying large language models.

VM

VM is an open source project that boasts fast language model inference. It offers enhanced performance by optimizing paging and other crucial aspects of the inferencing process. VM supports a wide range of hugging face models and is renowned for its speed and reliability.

MLC

MLC is an open source project that allows you to run large language models on smartphones and handheld devices. While the deployment process may require additional configuration, MLC is known for its impressive speed and quantization capabilities. If you are looking to optimize inferencing on mobile devices, MLC is worth exploring.

Optimizing Latency and Deployment

To optimize latency and ensure smooth deployment of large language models, it is essential to consider factors like server performance and model compatibility. Tools like VM provide excellent speed, while open source projects like Sky Pilot offer flexibility across various cloud providers. Additionally, you can find helpful insights and recommendations in Haml's blog post on optimizing latency and deployment.

Conclusion

Building AI SAS applications necessitates considering a variety of hosted API solutions and open source projects. By following this guide, you can choose the most suitable option for your needs. Whether you prefer hosted solutions like Replicate, Hugging Faces, and Together AI, or open source projects like TT Generation Web UI, Sky Pilot, and VM, there are plenty of choices to explore. Optimizing latency and deployment requires careful consideration, but with the right tools and insights, you can build robust and efficient applications. Remember to evaluate costs, speed, and specific requirements before making your final decision. Happy building!

Highlights

• Explore various solutions for building AI SAS applications
• Hosted API solutions like Replicate and Hugging Faces Inference Endpoint
• Open source projects like TT Generation Web UI and Sky Pilot
• Optimize latency and deployment with tools like VM and MLC
• Consider factors like cost, speed, and flexibility in decision making

FAQ

Q: Are all the hosted API solutions Mentioned in this article paid services? A: While some hosted API solutions in this article may require payment, there are also free options available. For example, Coher offers a free plan during prototyping, and Hugging Faces Inference Endpoint provides a free tier for users with a Perplexity Pro subscription.

Q: Can I use these open source projects to deploy large language models for commercial purposes? A: Yes, most open source projects mentioned in this article can be used for commercial purposes. However, please check the specific licenses and terms of each project to ensure compliance.

Q: Which solution is recommended for optimizing inferencing on mobile devices? A: MLC is a recommended solution for optimizing inferencing on smartphones and handheld devices. It provides impressive speed and is capable of running large language models efficiently.

Q: Can I deploy multiple models using the Light LLN open source project? A: Yes, Light LLN supports compatibility with over 100 APIs, allowing you to deploy multiple models from different providers using a unified API structure.

Q: How can I ensure the fastest inferencing speed for my AI SAS applications? A: To optimize inferencing speed, consider using solutions like VM, Perplexity PX APA, or Together AI, which have demonstrated high performance and low latency. Additionally, carefully assessing factors like server performance and model compatibility can further enhance speed.

Q: What are some key considerations when choosing between hosted API solutions and open source projects? A: When choosing between hosted API solutions and open source projects, consider factors such as cost, speed, flexibility, and specific requirements of your project. Hosted solutions may offer convenience and support, while open source projects provide greater customization and control over the deployment process.