DeepSeek-R1 vs. Llama 3.3 (70B): AI Chatbot on GenAI

Introduction

Choosing the right LLM is essential for building an effective AI chatbot. Some models specialize in deep reasoning, while others are better suited for retrieval-based responses.

To compare these differences, we deployed two AI chatbots using DeepSeek-R1 and Llama 3.3 (70B) on DigitalOcean’s GenAI Platform. We tested them against three common & real-world startup-related queries like What are the best DigitalOcean products for launching a SaaS quickly? , What is the fastest and most minimal setup for product-market fit? and What is the most cost-effective infrastructure for a startup?.

By the end of this tutorial, you’ll:

• Understand how different LLMs approach business-oriented tasks.
• Learn how to deploy AI chatbots on DigitalOcean’s GenAI Platform.
• Gain insights into cost-efficient and scalable AI-powered infrastructure.

This tutorial is valuable for founders, AI engineers, and SaaS developers who want to leverage LLM-powered chatbots for business strategy, infrastructure planning, and cost optimization.

Prerequisites

Before getting started, ensure you have the following:

• A DigitalOcean account with access to the GenAI Platform.
• Basic familiarity with LLMs and Retrieval-Augmented Generation (RAG).
• A knowledge base (structured in Markdown or HTML) for chatbot testing.

Data is Essential: Feeding Your Chatbot the Right Information

A chatbot’s performance is only as good as the data it’s trained on. Before we compare DeepSeek-R1 and Llama 3.3 (70B), we need a high-quality knowledge base (KB) for them to retrieve relevant answers.

Using DigitalOcean’s GenAI Platform to Crawl Data

DigitalOcean’s GenAI Platform provides an integrated web crawler that allows users to ingest external website data directly into the chatbot’s knowledge base. This ensures that the chatbot has access to relevant, up-to-date information from public websites.

How to Use the Web Crawler

1. Create a Knowledge Base: Navigate to the GenAI Platform from the control panel and create a new Knowledge Base.

2. Select Data Source: Click “Select Data Source” and choose “URL for web crawling” from the dropdown.

3. Enter Seed URL: Input the URL of the website you want to crawl.

4. Choose Crawling Rules: Select the appropriate crawling depth and strategy:

   • Scoped (Most Narrow) – Only crawls the seed URL.
   • URL and all linked pages in path (Narrow) – Crawls all pages linked within the URL path.
   • URL and all linked pages in domain (Broad) – Crawls all pages within the same domain.
   • Subdomains (Most Broad) – Crawls pages across all subdomains.

5. Confirm and Add Data Source: Click “Add selected data source” to start the crawling process.

Once added, GenAI Platform will automatically parse and index the content into an LLM-friendly format, making it accessible for chatbot queries. You can read more about How to Create, Index, List, and Delete Data Sources.

Step 1 - Deploying Two AI Chatbots on DigitalOcean

The DigitalOcean GenAI Platform provides a serverless environment for deploying AI applications without managing GPUs or infrastructure.

Create Two Chatbot Instances

1. Log in to your DigitalOcean account.
2. Navigate to the GenAI Platform and create a new chatbot instance.
3. Select DeepSeek-R1 as the first model.
4. Repeat the process to create another chatbot using Llama 3.3 (70B).

Upload a Knowledge Base

1. Use DigitalOcean’s web crawler or manually upload Markdown/HTML documents.
2. The platform automatically processes embeddings for document indexing.
3. Once uploaded, your chatbot is ready to retrieve and answer questions.

You can refer to this tutorial on Creating the Knowledge Base on GenAI Platform.

System Prompt and Configuration

Role & Purpose

You are an AI-powered DigitalOcean Product Advocate, specializing in providing accurate, structured, and reference-backed guidance on DigitalOcean products. Your primary role is to retrieve relevant product documentation and summarize key information based on user queries.

System Cofiguration

Explanation of Configuration

• Max Tokens (1600): Controls the maximum length of responses to ensure detailed yet concise answers.
• Temperature (0.2): Low randomness, making responses more deterministic and reliable.
• Top-P (0.5): Limits the response to the most likely words, reducing unexpected or creative outputs.
• K-Value (5): The model considers only the top 5 most likely words at each step, ensuring precise and relevant responses.

At this stage, you have two chatbots—one powered by DeepSeek-R1 and another by Llama 3.3 (70B).

Step 2 - Testing the Two Chatbots

We tested both chatbots with three key SaaS-related business questions to evaluate their ability to provide actionable insights.

Test Question 1: What are the Best DigitalOcean Products for Launching a SaaS?

Here is the prompt we used:

I have an idea to run a SaaS and I want to ship the product fast to the market. What are the products that I can use to build it up?

Summary of Findings

From the responses, DeepSeek-R1 excels in providing a structured, in-depth breakdown of how various DigitalOcean products can be used to build and deploy a SaaS. Its response aligns closely with DigitalOcean’s infrastructure offerings, making it particularly useful for users who need product-specific recommendations.

On the other hand, Llama 3.3 (70B) delivers a more generic overview, listing relevant DigitalOcean products without diving into infrastructure-specific details. While its response is more concise and easier to scan, it lacks the tailored insights needed for building a fully optimized SaaS architecture on DigitalOcean.

Key Takeaways

• If you need a detailed, structured response with DigitalOcean-specific insights, DeepSeek-R1 is the better choice.
• If you prefer a quick, high-level overview without extensive explanations, Llama 3.3 (70B) offers a lighter-weight alternative.
• For practical implementation, expanding the full DeepSeek-R1 response is recommended, as it outlines DigitalOcean’s SaaS-ready products in greater depth.

Test Question 2: Fastest and Minimum Setup for Product-Market Fit?

Here is the prompt we used:

"Just want to do product-market fit, what is the fastest and most minimal setup to do it?"

Summary

For users looking to validate their SaaS idea quickly, DeepSeek-R1 offers a more strategic, well-reasoned approach, explaining why each DigitalOcean product contributes to a rapid product-market fit. It connects infrastructure choices to SaaS development needs, making it especially useful for those who want to make informed decisions.

On the other hand, Llama 3.3 (70B) delivers a more to-the-point response, recommending DigitalOcean App Platform as the primary solution. While its suggestions are valid, it lacks the reasoning behind them, which might leave users wondering why certain DigitalOcean services are more optimal than others.

Takeaways

• DeepSeek-R1 is ideal if you want to understand the reasoning behind the infrastructure choices.
• Llama 3.3 (70B) provides a faster, lightweight answer but lacks detailed justifications.
• If speed of execution is the main priority, Llama’s response is a good starting point.
• For a more robust strategy that ensures scalability and reliability, DeepSeek-R1 is preferable.

Test Question 3: Most Cost-Effective SaaS Infrastructure?

Here is the prompt we used:

Give me the most economic proposal.

Summary of the response

In this final query, we observe a clear difference in reasoning depth between the two models. DeepSeek-R1 builds on the first two responses, offering a phased approach—starting with a minimal setup, transitioning to scalability, and then optimizing for cost-efficiency. It carefully analyzes DigitalOcean’s product offerings based on the trade-offs between ease of setup, scalability, and cost control.

Meanwhile, Llama 3.3 (70B) provides a more lightweight, serverless-focused answer, highlighting DigitalOcean Functions as a way to minimize infrastructure overhead. While valid, its response does not address long-term growth or transitioning to scalable architectures, which could be critical for startup founders evaluating cost-effective strategies.

Final Takeaways

• DeepSeek-R1 is ideal for startup founders who want to optimize their cloud spending across different growth stages.
• Llama 3.3 (70B) is useful for those who need a quick cost-saving approach but may overlook scalability.
• If your priority is immediate cost reduction, Llama’s response offers a simple, serverless approach.
• If you’re planning for long-term SaaS growth while keeping costs low, DeepSeek-R1 provides a more structured strategy.

Key Takeaways from Our AI Chatbot Experiment

Data Quality Determines AI Performance

One of the most fundamental insights from this experiment is that LLMs are only as good as the data they have access to. Even the most sophisticated AI models cannot compensate for low-quality, outdated, or poorly structured information.

Key Recommendation

Before deploying an AI chatbot, prioritize data quality through a structured ingestion process:

• Use well-structured formats like Markdown, JSON, or HTML for better retrieval and indexing.
• Automate data collection with tools like DigitalOcean’s GenAI Crawler or Crawl4AI for high-quality data extraction.
• Continuously update your knowledge base to prevent stale, misleading, or incomplete responses.

An AI model trained on unreliable data will produce unreliable results—regardless of its reasoning capability. Investing in data collection and maintenance is as crucial as selecting the right model.

Not Every Use Case Requires a Heavyweight Reasoning Model

While DeepSeek-R1 excels in reasoning and structured responses, it isn’t always the best choice for every application. Some use cases don’t require complex reasoning and benefit more from lighter, faster models like Llama 3.3 (70B).

Best Scenarios for Lightweight Models (Llama 3.3 or Similar)

For straightforward Q&A retrieval, fact-based, lookup-style queries, and speed-sensitive applications, a smaller, non-reasoning model like Llama 3.3 (70B) is more efficient. If your use case is primarily RAG-based (Retrieval-Augmented Generation) with minimal reasoning, a non-reasoning model is more cost-effective and faster.

When Should You Use a Reasoning Model Like DeepSeek-R1?

While Llama 3.3 provides fast, generic responses, DeepSeek-R1 excels at structured problem-solving and contextual adaptation. It goes beyond simple retrieval by offering step-by-step reasoning and decision-making.

Best Scenarios for DeepSeek-R1

DeepSeek-R1 excels in scenarios that demand structured analysis, iterative improvements, and contextual reasoning to inform decision-making. This is evident in its ability to facilitate strategic decision-making, such as determining the optimal DigitalOcean setup for scaling a SaaS. Additionally, it is adept at tackling multi-step problem-solving tasks, like transitioning from a monolithic app to a microservices architecture. Furthermore, when optimizing cloud infrastructure for cost, DeepSeek-R1 refines its response based on different constraints, demonstrating its capacity for reflective improvement.

Final Thoughts - The Trade-Off Between Speed, Complexity, and Cost

The choice between DeepSeek-R1 and Llama 3.3 (70B) depends on your specific needs for reasoning, response speed, and infrastructure cost:

In real-world applications, a hybrid approach might be ideal - Use Llama 3.3 (70B) for quick responses and DeepSeek-R1 for more in-depth analysis when needed.

Choosing the right LLM isn’t just about model size—it’s about understanding your use case, optimizing for efficiency, and balancing cost with complexity.

FAQs

1. What are the best use cases for DeepSeek-R1 and Llama 3.3 (70B)?

DeepSeek-R1 is best suited for scenarios requiring structured analysis, iterative improvements, and contextual reasoning to guide decision-making. On the other hand, Llama 3.3 (70B) is more efficient for use cases primarily RAG-based (Retrieval-Augmented Generation) with minimal reasoning.

2. How do I choose between DeepSeek-R1 and Llama 3.3 (70B)?

The choice between DeepSeek-R1 and Llama 3.3 (70B) depends on your specific needs for reasoning, response speed, and infrastructure cost. If you need fast, lightweight responses, Llama 3.3 (70B) is the better choice. If you need structured multi-step reasoning, DeepSeek-R1 is preferable.

3. Can I use a hybrid approach with both DeepSeek-R1 and Llama 3.3 (70B)?

In real-world applications, a hybrid approach might be ideal. You can use Llama 3.3 (70B) for quick responses and DeepSeek-R1 for more in-depth analysis when needed.

4. What are the key differences between DeepSeek-R1 and Llama 3.3 (70B)?

The key differences between DeepSeek-R1 and Llama 3.3 (70B) are summarized in the following table:

DeepSeek-R1 excels at structured problem-solving and contextual adaptation, offering step-by-step reasoning and decision-making. Llama 3.3 (70B) provides fast, generic responses and is more efficient for use cases primarily RAG-based with minimal reasoning.

5. What are the best scenarios for Llama 3.3 (70B)?

Llama 3.3 (70B) is best suited for straightforward Q&A retrieval, fact-based, lookup-style queries, and speed-sensitive applications.

6. What are the best scenarios for DeepSeek-R1?

DeepSeek-R1 is best suited for strategic decision-making, multi-step problem-solving, and reflective improvement.

Conclusion

The choice between DeepSeek-R1 and Llama 3.3 (70B) depends on your specific needs for reasoning, response speed, and infrastructure cost. Both models have their strengths and weaknesses, and a hybrid approach might be ideal for real-world applications. Remember, the right LLM isn’t just about model size—it’s about understanding your use case, optimizing for efficiency, and balancing cost with complexity.

Next Steps

1. Deploy Your AI Chatbot on DigitalOcean GenAI.
2. Explore DigitalOcean SaaS Hosting Solutions.