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Introduction
At Freeport Metrics, we have been at the forefront of AI application development since early 2023, creating solutions like user assistants that enhance the way users interact with software and data. However, with the market evolving at such a rapid pace, we felt it was important to take a step back and assess the current maturity, usability, and value offered by the leading solutions available. Both to evaluate how we could build them again using current tools and frameworks, but also to determine which ones have inevitably become commodities in the meantime. In this blog post, we have compiled our insights and findings, and we hope you find them as valuable as we have.
Market Dynamics and Investments
The generative AI industry is incredibly active, with new solutions being introduced regularly. Both established tech companies and startups are continually updating their products to include the latest AI technologies. Big players are heavily investing, showing their commitment to expanding the possibilities of AI. They not only drive rapid advancements but also make powerful AI tools more accessible to a wider audience, beyond just those with significant resources.
Research Objectives and AI-Driven App Composition
Our research started with identifying the key components of strong AI-driven applications. A comprehensive AI application usually combines foundational models with additional elements like:
- Vector storage for retrieval-augmented generation (RAG),
- Multi-agent orchestration,
- Security and confidentiality guardrails,
- Support for prompt engineering (e.g. prompt historicization),
- AI Test Harness,
- Voice or chat-based UI to improve functionality and user experience.
For those interested in diving deeper into the core and advanced aspects of AI applications, we suggest exploring our blog posts on RAG and multi-agent approaches.
Methodology and Research Scope
Our methodology surveyed a broad spectrum of AI tools, categorizing them based on their functionality, use cases, and key characteristics. By synthesizing information from the selected tools, we aimed to present a coherent and accessible overview of the current tooling landscape.
Use Cases & Overview of Categories
We structured the research into three categories:
- Selected Barebone LLMs (OpenAI’s ChatGPT, Goggle’s Gemini, Anthropic’s Claude) - basic large language models that provide fundamental interaction capabilities with minimal complexity. They offer user interfaces of ever-increasing functionalities and well-documented REST API services.
- Cloud Gen AI platforms (AWS Bedrock, Azure OpenAI, Google Vertex) - comprehensive suites provided and maintained by cloud tech giants, offering a wide array of AI development tools, including model training, deployment, and integration, suitable for enterprise-level applications and scalability.
- AI and Agent Development Frameworks (Semantic Kernel, LangChain) - provide development tools and libraries for creating and managing AI agents, enabling automation and complex task workflows with a focus on customization and flexibility.
Each category was analyzed through specific features and broad use cases. We developed a list of generic use cases by considering the software's usability for each group. This involved defining the users interacting with the application, their objectives, and the key features they would need to achieve their goals.
We examined various use cases, ranging from broad scenarios like “as a company, I want to enhance my existing system with AI” to more specific scenarios like “as an analyst, I want to ask questions and analyze data to create a report.” Through this approach, we identified numerous implementation scenarios, architectures, and common components that applications typically require. This enabled us to pinpoint the critical features needed for the tools we assessed.
Areas Where Each Category Excels
Depending on your use case, different categories of AI tools can be appropriate:
- Barebone LLMs are particularly well-suited for straightforward user interactions and exploring the capabilities of large language models that enable rapid simple solution development. Their simplicity makes them ideal for users who wish to experiment with AI responses and understand the foundational aspects of LLM functionality, via API or dedicated user interface, without the need for advanced setups or integrations.
- Cloud Gen AI platforms shine in the realm of enterprise-level AI development and deployment, primarily because of their extensive range of features and robust integration capabilities. They are designed to handle complex applications, offering tools for model training, data management, and scalability. Additionally, they cater to stringent security and compliance requirements, making them suitable for industries with rigorous data protection needs.
- AI Development Frameworks excel in creating and managing sophisticated AI applications, including agents that automate complex tasks and workflows. They provide extensive customization options and flexibility, enabling developers to design standalone deployable applications tailored to specific processes or environments. These frameworks are ideal for projects requiring advanced automation and dynamic interaction within varied settings.
Barebone LLMs
Barebone LLMs provide user-friendly interface applications and APIs for seamless integration. These versatile tools are perfect for those exploring the realm of generative AI. They can be used for straightforward tasks, such as content and code generation through their interfaces or creating basic chatbot assistants via API integration. However, integrating them into existing applications necessitates some coding effort. While they offer flexibility and customization options, this often increases development overhead. For simple prompt execution, these tools are adequate. However, if you need to implement more complex features, like building Retrieval-Augmented Generation (RAG) solutions, you might soon find yourself needing more advanced toolsets.
Example use cases
- Chatbots and Virtual Assistants: Develop conversational agents for customer service or personal assistance.
- Content Generation: Automate the creation of articles, social media posts, or marketing copy.
- Language Translation: Facilitate real-time or batch translation of text across multiple languages.
- Sentiment Analysis: Analyze customer feedback or social media content to gauge public sentiment.
- Code Generation and Debugging: Assist developers by generating code snippets or identifying bugs.
Pros:
- Simplicity and Ease of Use: They are straightforward to use with minimal setup.
- Direct LLM Interaction: They offer direct access to the LLM's capabilities, allowing for prompt experimentation and exploration of model responses.
- Flexibility: They allow development teams to freely adjust the approach to the architecture and requirements.
- Variety of Use: They allow access to multiple dedicated models via user interface as well as API.
Cons:
- Limited Functionality: They lack additional features present in Gen AI Platforms and Agent-enabling Frameworks.
- Customization Limitations: There are often limited options for fine-tuning or customizing the model for specific tasks.
- Increased Overheads: They require more overhead work, for example, prompt historicization and prompt safety handling.
Cloud Gen AI Platforms
These platforms provide a comprehensive suite of services and integrations that significantly reduce the amount of coding required, featuring user-friendly interfaces that simplify the integration of AI capabilities into various applications. They offer pre-trained models that facilitate easy deployment, enabling rapid application development while incorporating built-in safeguards to ensure reliability and compliance. Additionally, these platforms allow for model fine-tuning and support the use of private models, which enhances security and customization to meet specific organizational needs.
Example use cases
- Enterprise-Level AI Solutions: Develop large-scale AI applications that require significant computational resources and scalability.
- Data-Driven Applications: Build applications that leverage big data analytics, benefiting from cloud-based storage and processing power.
- Custom AI Model Deployment: Deploy custom-trained models for specific business needs, utilizing the cloud's flexibility and infrastructure.
- Multi-Model Integration: Integrate multiple AI models and services into cohesive applications, benefiting from diverse cloud offerings.
- Automated Machine Learning Pipelines: Streamline and automate ML workflows, from data preprocessing to model deployment and monitoring.
Pros:
- Broader Feature Set: They encompass various aspects of AI development and deployment.
- Customization and Scalability: These platforms often include tools for fine-tuning models, managing data pipelines, and scaling applications.
- Integration Capabilities: They are designed to integrate with other systems and services, making them suitable for enterprise-level applications.
- Safeguards: Built-in tooling for improving security, such as denied topics, wording filtering, sensitive content detection and more.
- Accessing pre-trained models: Platforms allow access to pre-configured and trained models dedicated to various use cases.
Cons:
- Complexity: They can be more complex to learn as they may require specific platform expertise.
- Vendor lock-in: After configuring multiple cloud services or fine-tuning the model, the migration can be costly, time-consuming, and difficult.
- Cost: Potentially high expenses, especially with extensive usage.
AI Development Frameworks
AI Development Frameworks are designed to facilitate the creation and integration of AI applications, supporting complex interactions with language models across various workflows. These frameworks provide the necessary tools and libraries to build, customize, and manage AI-driven functionalities, offering developers flexibility and control over the infrastructure and tools used. Available as both open-source solutions and commercial platforms, these frameworks enable the development of standalone, deployable applications. Tools like LangChain offer specialized capabilities, further enhancing the ability to tailor applications to specific needs.
Example use cases
- Custom Application Development: Create tailored AI applications that require specific functionalities not provided by out-of-the-box solutions.
- Workflow Automation: Automate complex workflows by integrating AI capabilities into existing business processes.
- Conversational Agents: Develop sophisticated chatbots and virtual assistants that can understand and process natural language.
- Data Processing Pipelines: Build systems that utilize AI for data extraction, transformation, and analysis in a highly customizable manner.
- Interactive AI Tools: Design interactive applications that leverage AI for enhanced user engagement and personalization.
Pros:
- Integration & Flexibility: Easily integrates with various APIs and data sources for expanded functionality. Offers extensive customization to suit specific application needs.
- Active Open Source Community: Majority of the products from this category are open source and are actively maintained and improved.
- Flexible Pricing: There are no requirements to use paid tools, the implementation can be limited to MIT license tooling.
- Cloud Integration: These tools can often be integrated into existing cloud infrastructure and can benefit from cloud-provided features.
Cons:
- Complexity: May require a steep learning curve for developers unfamiliar with AI and machine learning concepts.
- Development Time: Customization and development can be time-consuming compared to using pre-built solutions.
- Infrastructure Management: Required DevOps related overheads for managing the complex infrastructure.
Conclusion
In conclusion, the landscape of generative AI tools is diverse, each category offering distinct strengths tailored to different needs. Barebone LLMs provide powerful language processing capabilities with user-friendly interfaces and APIs, making them ideal for those seeking straightforward AI integration with minimal setup. AI Development Frameworks offer unparalleled flexibility and control, allowing developers to build highly customized and sophisticated applications by leveraging extensive libraries and tools. Finally, cloud generative AI platforms deliver scalable and robust infrastructure, enabling seamless integration of AI capabilities into broader cloud ecosystems.
As the market continues to evolve, selecting the right set of tools becomes crucial in leveraging generative AI to its fullest potential. This flexibility ensures that, regardless of budgetary constraints, there is a pathway to implementing powerful AI solutions.
When selecting a generative AI tool, it's essential to consider the specific requirements of your project, including the level of customization needed, the complexity of integration, and the scalability demands. By understanding the unique offerings of each category, you can make informed decisions that align with your strategic objectives as well as technological and business goals.
Contact us to discuss your use-case.
