There are many questions about which large language model (LLM) is best suited for your enterprise AI application. But GPT-4 vs Gemini vs Titan isn’t the only question you need to answer. An effective AI application should be built on a solid stack, emphasizing the question of which cloud provider is right for your AI application.Google Cloud vs Microsoft Azure vs AWS could turn out to be a very impactful decision.
Cloud platforms provide the infrastructure, scalability, and security needed to support an enterprise AI application. Each cloud provider offers different tools and services, integrates with different systems, and has different levels of customization, scalability, and cost-effectiveness.
Whether you are considering which cloud provider is right for your specific use case or you’re already locked into a provider, knowing which large language models are supported by each can help your AI application development.
LLMs, such as GPT-4, Gemini, and Titan, are hosted on cloud platforms, allowing businesses to easily integrate them into their operations without building the infrastructure from scratch. Cloud providers also simplify AI development and integration by offering pre-trained models, scalable computing resources, and integration tools.
Different cloud platforms offer different LLMs, so it’s important to align the strengths of each provider with your enterprise AI needs.
The three major cloud providers - Google Cloud, Microsoft Azure, and Amazon Web Services (AWS) - support different AI models that fit specific AI use cases. The cloud providers support their own “home grown” LLMs and certain partner models. For example, Google Cloud provides PaLM 2 and Gemini but also partners with Antrhopic, Meta, and Mistral to support their language models.
Here is an overview of the most popular LLMs offered by each cloud provider and models that are accessible across multiple providers and how they stack up in terms of capabilities.
Google Cloud offers two primary, proprietary AI models, PaLM 2 and Gemini.
Pathways Language Model 2 is Google’s flagship model, released in 2023 as a successor to the original PaLM. It is designed for natural language processing (NLP), multilingual applications, and complex problem-solving tasks. Its multilingual capabilities support over 100 languages, making it ideal for chatbots and other applications that need to handle different languages. The model is also well-suited for high-quality text generation, translations, summarization, Q&As, and code generation.
Gemini is Google’s next-generation, multimodal model developed in 2024. Multimodal means it is designed for tasks that integrate both language and vision inputs, making it better suited for broader AI applications. If your application involves images or data analysis, Gemini would be a better choice compared to PaLM 2. It also includes several deep learning enhancements that enhance performance for specialized applications and cross-functional AI tools. Newer models can sometimes mean higher costs, less documentation, and less third-party support, which may create limitations for some more complicated implementations. However, Google Cloud’s Vertex AI platform provides a fairly good ecosystem for Gemini.
Microsoft is an investor in OpenAI, developers of ChatGPT and the GPT LLMs. This partnership makes Microsoft Azure the exclusive cloud provider of OpenAI’s GPT models.
GPT-4 is the most recent and advanced iteration of the GPT model, developed in 2023. It improves on GPT-3 with enhanced contextual reasoning, complex language generation, and the ability to handle longer input contexts. It is a strong choice for generative AI and NLP tasks such as content generation, code assistance, and data analysis. GPT-4 can be resource-intensive and costly to run, especially for enterprises with limited budgets or smaller-scale applications.
GPT-3 lacks the advanced reasoning capabilities of GPT-4, but also comes with a lower price tag. This makes it ideal for basic AI applications with lighter computational needs and lower budgets. While slightly less advanced than GPT-4, GPT-3 has strong capabilities for conversational AI, customer service automation, and natural language understanding tasks. It is well-suited for chatbots, customer service, internal business tools, and content generation.
The main proprietary model supported by AWS is Amazon Titan. AWS Bedrock also supports additional models.
Amazon’s Titan models are designed to provide natural language generation and understanding within the AWS ecosystem. Titan excels at handling customizable AI workflows, giving enterprises the flexibility to adjust the model to specific needs, especially in areas like customer service automation, e-commerce personalization, and internal business process optimization. Titan is less proven in complex reasoning tasks compared to other models like GPT-4 or Gemini, making it less suitable for highly advanced applications.
Some LLMs, such as Claude and Cohere, are available across multiple cloud platforms, providing additional LLM options beyond your cloud provider’s proprietary models. While GPT is offered exclusively by Microsoft, it can be accessed on Azure and deployed in custom environments on platforms like AWS.
Claude, developed by Anthropic, is designed with a focus on AI safety and ethical use, making it an ideal solution for businesses in regulated industries such as finance or healthcare. Claude’s capabilities include conversational AI, content generation, and document analysis, but its core strength lies in AI safety features like producing reliable and non-harmful outputs. Claude is built to prioritize safe and transparent AI interactions, making it perfect for businesses where trust, compliance, and risk management are critical. Claude’s heavy focus on safety can sometimes lead to overly cautious or conservative outputs, limiting its creativity or flexibility in less regulated industries.
Cohere, developed in 2021, specializes in natural language understanding (NLU) and embedding models for semantic search, text classification, and information retrieval. The focus on natural language understanding means it is less proficient in generative AI capabilities compared to GPT, but is highly effective for use cases that require data extraction and processing large amounts of unstructured text data.
LLaMA, developed by Meta in 2023, is an open-source LLM focused on high performance with a lower computational footprint, making it cost-effective for businesses needing lightweight AI solutions. Because it is open-source, LLaMA is highly customizable. This makes it ideal for companies that need flexible AI for niche applications without the high costs of larger proprietary models. Being open-source, LLaMA requires more technical expertise for implementation, customization, and maintenance. This may be a barrier for enterprises lacking in-house AI expertise.
At Gigster, we generally recommend choosing a cloud provider that fits the requirements of your overall AI cloud stack and then choosing a large language model that works with your chosen cloud platform. The decision should be based on several key factors, including how well the cloud platform aligns with your existing infrastructure, ease of integration, privacy concerns, scalability, and your AI application budget.
When choosing between GPT-4, Gemini, and Cohere, the cloud provider you select can have as much of an impact as the AI model itself. By considering your business’s existing infrastructure, AI requirements, privacy needs, scalability, and budget, you can find the right combination of LLM and cloud platform to drive innovation and efficiency across your enterprise.
Gigster’s AI development experts can help choose the right cloud platform and LLM for your next application. Share your AI project now to get started.
