Open Source vs. APIs: Pros, Cons & Everything in Between

Open Source vs. APIs: Pros, Cons & Everything in Between

Table of Contents:

1. Introduction
2. Factors to Consider: Open Source vs. Closed Source
3. Required Team Expertise
4. R&D Budget
5. Time to Market
6. Model Quality and Customization
7. Data Privacy
8. Inference Speed and Stability
9. Cost Efficiency at Scale
10. Comparison: Open Source vs. Closed Source Pricing
11. Getting Started with Open Source Models
12. Desi's Open Source Models
13. The Power of Infer LM
14. Conclusion

Introduction

In this article, we will explore the factors to consider when choosing between open source and closed source in building AI-based applications. We will discuss the required team expertise, R&D budget, time to market, model quality and customization, data privacy, inference speed and stability, cost efficiency at scale, and pricing comparison between open source and closed source models. Additionally, we will provide an overview of Desi's open source models and the benefits of using the Infer LM SDK for improved performance. Let's dive in!

Factors to Consider: Open Source vs. Closed Source

When deciding between open source and closed source, there are several factors to consider. These include the required team expertise, R&D budget, time to market, model quality and customization, data privacy, inference speed and stability, and cost efficiency at scale.

Required Team Expertise

Building with closed source models primarily involves integrating APIs, which requires software development skills. On the other HAND, open source models require choosing the right model, fine-tuning, customization, and deployment, which may be more complex and time-consuming. Closed source models offer streamlined API calls, while open source models require more expertise in selecting, fine-tuning, and maintaining the models.

R&D Budget

The initial cost for closed source models is relatively low, as it typically involves subscribing to a service and integrating APIs. However, open source models require the acquisition of expertise, data collection, fine-tuning, and deployment, which may involve additional costs. In the long term, open source models offer cost efficiency due to lower token pricing.

Time to Market

Closed source models have a faster time to market, as integrating APIs and getting a demo up and running is relatively easy. However, customization options are limited. Open source models require more time initially but offer the ability to customize and optimize the models later on.

Model Quality and Customization

Closed source models are often black boxes, with limited control over model size, content filters, and model updates. Open source models provide full control and customization options, allowing for the building of proprietary content filters, safety nets, and optimizing model quality.

Data Privacy

Most closed source models require data sharing at inference time, which may raise data privacy concerns. Open source models provide full control over where the model runs, allowing for deployment within a VPC and ensuring data privacy, making them ideal for handling sensitive data in domains like Healthcare and finance.

Inference Speed and Stability

In closed source models, model speed and stability are determined by the providers, with limited control over latency and workload scalability. Open source models require more effort to put into production but offer the ability to optimize latency and choose optimal inference settings for low latency or high throughput.

Cost Efficiency at Scale

Closed source models incur a cost per token, which can scale linearly, leading to higher costs at scale. Open source models offer cost efficiency, allowing for the use of smaller and faster models that better fit specific use cases, reducing costs significantly.

Comparison: Open Source vs. Closed Source Pricing

Comparing the pricing of open source models to closed source models, there is a significant cost reduction when using open source models, especially when running them on existing infrastructure with infer LM SDK. The cost per token is significantly lower, allowing for the use of smaller models that still offer high performance and cost efficiency.

Getting Started with Open Source Models

To start using open source models, define your task and measure your desired performance. Explore and fine-tune existing open source models, customize them to your needs, and test them on specific benchmarks. Consider techniques like reinforcement learning, Hyperparameter search, and fine-tuning with your own data or codebase to optimize the model's performance.

Desi's Open Source Models

Desi provides several open source models, including Des Coder 1B, DM 7B, and D Diffusion, which offer competitive performance compared to closed source counterparts. These models are available on hugging face, with Desi's unique Autak technology for efficient model generation and optimization.

The Power of Infer LM

When using Infer LM in conjunction with Desi's models, you can achieve significant improvements in tokens per Second and throughput. Infer LM offers optimized CUDA kernels, continuous batching, selective quantization, hybrid compilation, and optimized sampling techniques. The combination of Desi's models and Infer LM provides increased performance, reduced cost, and better latency control.

Conclusion

Choosing between open source and closed source models involves considering various factors such as team expertise, R&D budget, time to market, model quality and customization, data privacy, inference speed and stability, and cost efficiency at scale. Both options have their pros and cons, and the choice depends on the specific requirements of the AI-based application. Open source models offer greater control, customization, and cost efficiency, while closed source API models provide faster time to market. By leveraging Desi's open source models and Infer LM SDK, developers can build superior AI applications with improved performance and cost optimization.

Highlights:

• Factors to consider: Team expertise, R&D budget, time to market, model quality, data privacy, inference speed, and cost efficiency.
• Open source vs. closed source: Pros and cons of each approach.
• Desi's open source models: Des Coder 1B, DM 7B, D Diffusion, Autak technology.
• The power of Infer LM: Optimized CUDA kernels, continuous batching, selective quantization, hybrid compilation, optimized sampling.
• Conclusion: Choosing the right approach depends on specific requirements.

FAQ:

Q: Are the Infer LM SDK and Desi's open source models free to use? A: Yes, both the Infer LM SDK and Desi's open source models are available for free on platforms like hugging face.

Q: Can closed source models be customized? A: Closed source models offer limited customization options compared to open source models.

Q: How can open source models improve cost efficiency? A: Open source models allow for the use of smaller and faster models, resulting in reduced costs at scale.

Q: Can I fine-tune open source models to specific use cases? A: Yes, open source models can be fine-tuned to specific use cases, programming languages, or even internal codebases.

Q: Are Desi's open source models optimized for performance? A: Yes, Desi's open source models are optimized for performance using Autak technology and can be further enhanced with the Infer LM SDK.