Generative AI is gaining traction in multi-agent systems, and for good reason. These intelligent agents, working in tandem within multi-agent AI environments, can mimic human behavior to perform tasks autonomously, making them a valuable tool for many operational and business needs. The complexity of technology is a big challenge for companies looking to implement generative AI systems. Building a robust, efficient, and scalable generative AI tech stack that meets business goals and seamlessly integrates with existing systems is crucial to overcoming this hurdle. This article will explore the generative AI tech stack—what it is, why it matters, and how to build one for your business.
An efficient generative AI tech stack is key to accelerating innovation and enhancing the performance of multi-agent systems. Lamatic’s solution helps teams build and customize their generative AI tech stacks. Focusing on organization and modularity, our product enables faster development, better integration, and more robust performance.
What is a Generative AI Tech Stack? Defining the Generative AI Tech Stack
A generative AI tech stack collects tools, frameworks, and infrastructure required to:
- Develop
- Deploy
- Scale generative AI applications
It typically includes:
- Machine learning models
- Data pipelines
- Cloud computing resources
- APIs
- Monitoring tools
A well-structured tech stack ensures:
- Performance
- Scalability
- Responsible AI implementation
The AI Layer: A Crucial Component of Modern Application Stacks
The AI layer is an enabling component of the modern application tech stack. A technology or “tech” stack is a collection of tools that integrate across several layers of application or system infrastructure to facilitate the development and deployment of software applications. Simply put, a tech stack is a composition of tools that play nicely together.
Breaking Down the Generative AI Tech Stack: Key Layers and Their Functions
Tools and technologies within the tech stack are divided into layers covering application concerns such as managing user interface and experience and handling data storage and processing. Other layer-specific concerns are business processing logic, security, and communication methods between layers (e.g., REST, SOAP, GraphQL, WebSockets).
Understanding the AI Layer: How Generative AI Integrates into Modern Tech Stacks
Let’s break the tech stack down into its layers:
Application layer
This is the central part of software applications. It covers the:
- User interface (UI)
- User experience (UX)
- Front-end creation
- Application accessibility and more
Back End Layer
This is also known as the “server-side” and manages most of the application logic, including:
- Connecting to databases
- Setting up application programming interface (API)
- Application authentication
- Security
Data Layer
All information from user and system interaction with an application requires storage alongside any business data that assists in the application's function. The data layer includes tools that handle the storage, retrieval, backup, and management of data that moves across the tech stack.
Operational Layer
Applications must be deployed into a production environment, where considerations become crucial like:
- Maintenance
- Update schedule
- Automation
- Management
- Optimization
The tools and technologies in this layer fall under the umbrella of development operations or DevOps.
The AI Layer: Enhancing Traditional Tech Stacks with Machine Learning and Deep Learning
The above is not an exhaustive list or description of the layers. We just need you to have a picture of the traditional tech stack and its composition. With advances in machine learning, deep learning, and AI, the AI layer has come into play and now has a permanent position within modern applications.
The Emergence of the AI Layer
The AI layer is a new key part of the tech stack. It introduces intelligence across the stack through:
- Descriptive (data visualization)
- Generative (image and text generation) capabilities in the application layer
- Predictive analysis (behavior and trends) in the data layer
- Process automation and optimization in the operational layer
Even the backend layer, responsible for orchestrating user requests to appropriate resources, has benefited from including the AI layer through techniques such as semantic routing.
Semantic Routing and the Evolving Role of the AI Layer in Modern Tech Stacks
For completeness, semantic routing is the technique of distributing operations (network traffic, user requests) to receiving processes based on the meaning and intent of the task to be done and the receiving processor configurations and characteristics.
This approach transforms the allocation of user requests from a programmatic concern to one outsourced to LLMs. The effectiveness and importance of the AI layer in modern applications also reduce the roles and responsibilities of the application and data layers, which can blur the boundaries between them.
What is the Generative AI Stack?
In a nutshell, generative AI is а mix of various methods and technologies carefully combined to make artificial intelligence systems capable of creating new content or data. These systems undergo training on existing datasets, empowering them to generate unique outputs by tapping into patterns and structures acquired during their learning phase.
The generative AI tech stack is a detailed breakdown of the tools, technologies, and frameworks commonly employed in developing AI systems. This stack serves as the foundation, guiding the construction of generative AI, and plays a pivotal role in transforming theoretical concepts into tangible, innovative outputs.
The Layers of the Generative AI Tech Stack
The generative AI tech stack consists of application frameworks and a tooling ecosystem we can divide into four layers:
- Models
- Data
- Evaluation
- Deployment
Let’s explore each of these tech stack parts.
Application Frameworks for Generative AI Tech Stacks
Application frameworks contribute to the generative AI tech stack by assimilating innovations and organizing them into a streamlined programming model. These frameworks simplify the development process, allowing developers to quickly refine and improve their software in response to emerging ideas, user feedback, or evolving requirements.
Generative AI is a recently emerged technology, but there is already a wide array of proven frameworks:
- LangChain: LangChain is an open-source focal point for developers navigating the complexities of foundation models. It provides a collaborative space and resources for developers working on generative AI projects.
- Fixie: Fixie is an enterprise-grade platform for creating, deploying, and managing AI agents. It focuses on delivering robust solutions tailored to businesses’ needs and contributes to the seamless integration of generative AI technologies in various industries.
- Semantic Kernel: Developed by Microsoft, this framework enables developers to build applications that can interpret and process information with a deeper understanding of context.
- Vertex AI: Vertex AI is a Google Cloud product that provides a platform for quickly creating and deploying machine learning models.
- Griptape: Griptape is an open-source framework for building systems based on large language models (LLMs)—neural network models designed to understand and generate human-like language patterns on a large scale. This is part of natural language processing (NLP). It's convenient for building conversational apps or event-driven apps.
Models
The traditional method for developing AI models is to build them from the ground up. The past five years have witnessed the emergence of a revolutionary category known as foundation models (FMs). These FMs are the beating heart of generative AI technology, seamlessly performing human-like tasks such as:
- Crafting images
- Generating text
- Composing music
- Producing videos
Developing generative AI solutions introduces a dynamic interplay with multiple FMs, each offering distinct output:
- Quality
- Cost
- Latency and more features
Choosing the Right Generative AI Tech Stack: Proprietary Models, Open-Source Alternatives, and Custom Training
Developers have three options for their generative AI tech stack:
- Leveraging proprietary models from vendors like Open AI or Cohere
- Exploring open-source alternatives such as Stable Diffusion 2, Llama, or Falcon
- Opting to embark on the journey of training their models
Hosting services, fueled by innovations from companies like OctoML, now offer developers the flexibility to host models on servers and deploy them on edge devices and browsers. This significant leap enhances privacy and security, drastically reducing latency and operational costs.
Regarding training, developers are empowered to shape their language models using various emerging platforms. Several of these platforms have given rise to open-source models, providing developers with readily accessible and customizable solutions out of the box.
Data
Models, particularly LLMs, must be “fed” vast amounts of data to train them. To harness the potential of data, developers employ the following approaches to connect and implement this invaluable resource.
- Data loaders: Data loaders facilitate the efficient loading and processing of datasets, ensuring that the model has seamless access to the necessary information and acts as the bridge between raw data and the generative AI model.
Developers use data loaders to handle normalization, transformation, and batching tasks. These mechanisms optimize the model’s ability to learn from diverse datasets while maintaining consistency and efficiency.
- Vector databases: In the context of generative AI tech stack, vectors are crucial as they encapsulate the essential features of the data in a compressed form and play a pivotal role in storing and managing vector representations of data.
Vector databases are needed to efficiently retrieve and manipulate vectorized data during the training and inference phases. This ensures the model can quickly access relevant information, enhancing its ability to generate coherent and contextually relevant outputs.
- Context windows: They encapsulate the contextual information surrounding a specific data point, providing a framework for the model to understand relationships and patterns and define the scope of data that the generative AI model considers during its operations.
To select an optimal generative AI tech stack, developers define context windows to influence the model's understanding of sequential or relational data. This enables the model to generate outputs that exhibit a deeper understanding of the context in which they are applied.
Evaluation
The performance phase is critical for developers of large language models within generative AI technology. A delicate balance must be struck between model performance, inference cost, and latency. This requires:
- Setting clear metrics
- Creating meticulous test sets
- Engaging in manual and automated iterative testing
Measuring performance in LLM is no easy feat. LLMs produce uncertain outputs based on probabilities and statistical patterns learned during training. This creates uncertainty as the model doesn't produce deterministic results for a given input. Specific language tasks, such as generating diverse creative content, don't have a correct answer.
The model can produce different outputs for the same input, adding a layer of complexity to measuring its performance making the performance evaluation more complex. Developers have three techniques to handle the complexity of the evaluation layer: provided:
- Prompt engineering
- Experimentation
- Observability
The Critical Role of Prompt Engineering, Experimentation, and Observability in Generative AI
Prompt Engineering Tools
Prompt engineering creates accurate questions or instructions to direct generative AI models toward particular results. It is the essential connection between human input and machine responses.
In assessing generative AI, it's crucial to engineer prompts that:
- Shape model behavior
- Improve outputs
- Optimize prompts for desired outcomes
- Ensure effective communication between humans and AI
Tools for experimentations
Experimentation in the generative AI tech stack context involves ML engineers conducting methodical experiments to understand how adjustments impact model behavior and performance before releasing the project. They carefully:
- Track changes to prompts
- Hyperparameters
- Fine-tuning configurations
- Model architectures
Bridging the Gap Between Offline Evaluation and Real-world AI Performance
Evaluating models offline in controlled staging environments is crucial before implementing them in real-world scenarios. Engineers use benchmark datasets, human labelers, or even generative models (LLMs) to create diverse scenarios that help them evaluate and improve model responses. While offline methodologies provide some insights, they have limitations.
For instance, they may not wholly capture real-life situations’ ever-changing and unpredictable nature, and relying on benchmark datasets during offline evaluations might restrict the model's exposure to a small range of examples. This is where tools like Statsig come in. They allow for evaluating model performance in production, ensuring that models behave as expected during live user interactions.
Observability tools
Once an application is deployed in a production environment, the journey is still far from over. That is where the observability process begins. Observability involves collecting, analyzing, and visualizing data related to the application's behavior, performance, and interactions after the model has been deployed. Developers gain essential insights into how their AI models function in real-world scenarios when applied to generative AI technology.
Enhancing Real-Time AI Monitoring with Observability and Tools Like LangKit
This ongoing process is crucial for several reasons.
- Observability in generative AI technology connects developers' knowledge of how their applications perform with infrastructure data. This helps them understand intricate distributed cloud infrastructure systems such as Kubernetes.
- Tracking the model’s behavior over time is essential for identifying any deviations or unexpected patterns that may emerge. This real-time feedback loop enables proactive measures to address issues before they escalate, ensuring a smooth and reliable user experience.
Platforms like WhyLabs have introduced tools such as LangKit to facilitate this post-deployment monitoring and analysis. LangKit is designed to offer developers a comprehensive view of the quality of model outputs. It goes beyond basic metrics, providing insights that help developers understand the intricacies of model performance. LangKit serves as a safeguard against malicious usage patterns.
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How to Select the Right Generative AI Tech Stack
Start With Purpose: Why Do You Want Generative AI?
Before you choose any tools or technologies to build your generative AI stack, start with a defined purpose.
- What business problems do you want to solve?
- What are the end goals? What will success look like?
Even the most advanced technology stack will fail if you don’t have a defined goal. After all, generative AI isn’t about using technology for the sake of using it – it’s about addressing specific business issues, such as:
- Improving customer experiences
- Increasing supply chain resilience
- Reimagining content development
Aligning Technology Stacks with Business Goals
Knowing the “why” sets the foundation. Well-defined goals help you choose the right technology stack. This technology stack should facilitate essential metrics. For many, this may mean more sales, better customer interactions, or more efficient operations. Consider how each tool can help these particular KPIs when selecting. If speed to market is essential, pre-trained models may offer a faster path, whereas custom model development can produce insights tailored explicitly to complex problems.
Nail Down Key Use Cases: What Will AI Do?
Generative AI must integrate deeply with the company’s operational requirements to create value. Defining use cases helps identify the tools and architecture suited for tasks like automating content or forecasting customer behavior. Some applications can benefit from platforms like GPT-4 for sophisticated text generation or DALL-E for creative graphical content.
Leveraging Real-Time Data Processing for AI in Retail and Banking
TensorFlow and PyTorch are well suited for real-time predictive analytics and can impact retail and banking industries. Technologies such as Apache Kafka and Apache Flink are required for real-time data processing, allowing businesses to respond quickly to incoming data streams.
Knowing the nuances of these use cases will help you choose the right generative AI tech stack and indicate which areas could benefit from off-the-shelf solutions and which from bespoke development.
Think Data-First: What Data Will Fuel Generative AI?
Data is the backbone of generative AI, but quality outweighs quantity. The adage “garbage in, garbage out” remains true—success hinges on ensuring high-quality data at every stage, from collection to processing.
Optimizing Data Flow for Generative AI
BigQuery and Snowflake excel at handling structured data, enabling seamless data processing throughout the stack. More specialized tools, such as Hugging Face’s Transformers for NLP work or OpenAI’s CLIP for multimodal understanding, become crucial when working with unstructured data, such as the clutter of:
- Emails
- Social media posts
- Consumer feedback
It all comes down to ensuring your stack facilitates data flow rather than hinders it, giving your models the size and quality they need. This is precisely what we achieved for our client. We enhanced an email marketing client’s dataset with the following:
- AI-driven insights
- Boosting customer data with reviews and social media integrations
- Leading to more targeted emails
- Improved open rates
Plan for Growth and Scalability: Can Your AI Stack Evolve as Needed?
A generative AI tech stack must meet today’s demands and be scalable. As your product evolves, your generative AI will demand more data. It will grow in complexity and have to adapt to advancing technology. AWS and Google Cloud platforms can ensure your AI operations can proliferate, while tools like Apache Kafka and Databricks can offer the infrastructure to manage real-time data streams.
But it’s essential to strike a balance. While cloud platforms can proliferate, hybrid models that combine cloud and on-premises resources give certain businesses a more secure and affordable option. Containers like Kubernetes and Docker provide development teams the flexibility they want without compromising reliability, making them ideal for organizations looking to scale.
Budget with Forecast: What Will It Cost to Build and Maintain Your AI Stack?
Although the cost of deployment and testing is high, investing in generative AI can yield promising results. Generative AI models, huge language models (LLMs), demand enormous processing power and memory. As these models become more complex, high-performance hardware such as GPUs and TPUs is required, significantly increasing operational costs.
These costs can skyrocket when AI activities scale, especially when continuous retraining or deployment across multiple environments is required. For resource-constrained companies, balancing cost-effectiveness and model performance is even more crucial.
Choosing Between Third-Party Services and Open-Source LLMs: Cost vs. Scalability in Generative AI
A thorough assessment of computing needs and costs is necessary to avoid this. Companies should properly research their options. Third-party services like OpenAI offer faster installation and lower upfront costs, making them attractive for smaller applications or those just starting to use AI.
But there is a catch: as AI usage increases, these services can become more expensive, especially when working with large or complex models. Open-source LLMs like LLaMA or Meta’s OPT might be more cost-effective in the long run. They allow customization and control over deployment, providing greater flexibility and reducing operational costs. Organizations should align generative AI efforts with future needs and scalability.
Balance In-House Expertise with External Support: What Skills Do You Need?
The complex field of generative AI requires specific expertise. Companies must carefully assess the internal skills necessary to develop, deploy, and manage a complex AI stack. If there is a gap, third-party assistance or strategic alliances can fill it. External partners can provide comprehensive support, including maintenance, deployment, and development.
Companies can adapt to changing project requirements by scaling up or down their AI teams as needed. These partners can also provide training and skills development opportunities for internal teams to improve their AI skills.
Bridging the Talent Gap: How Generative AI Enhanced Email Marketing for a SaaS Platform
Here’s an example. A well-established SaaS platform in the US wanted to improve email open rates, reduce content creation time, and integrate AI into their email system. Lacking the in-house expertise, they partnered with us to bridge the talent gap. We collaborated with their team to deploy a generative AI solution using DALL-E and ChatGPT. DALL-E produced striking visuals, while ChatGPT generated engaging email copy. Their emails resonated with existing customers and caught the eye of potential ones, significantly boosting engagement.
Commit to Security and Compliance: How Will You Protect Data?
Generative AI needs strict security standards and compliance controls, mainly when working with sensitive data. For businesses in regulated industries, selecting a technology stack with built-in compliance controls is essential to avoid regulatory fines. It also helps maintain the trust of customers and stakeholders.
Healthcare or financial sector companies should choose platforms with strong, industry-aligned compliance standards for their applications. Strong security features, such as end-to-end encryption and secure access control, should be part of any AI stack worth its salt. Implementing solutions like AWS Key Management Service (KMS) for data security or Okta for access control can prevent costly breaches that could jeopardize trust and reputation.
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Start Building GenAI Apps for Free Today with Our Managed Generative AI Tech Stack
Lamatic offers a managed Generative AI Tech Stack. Our solution provides:
- Managed GenAI Middleware
- Custom GenAI API (GraphQL)
- Low Code Agent Builder
- Automated GenAI Workflow (CI/CD)
- GenOps (DevOps for GenAI)
- Edge deployment via Cloudflare workers
- Integrated Vector Database (Weaviate)
Lamatic empowers teams to rapidly implement GenAI solutions without accruing tech debt. Our platform automates workflows and ensures production-grade deployment on edge, enabling fast, efficient GenAI integration for products needing swift AI capabilities. Start building GenAI apps for free today with our managed generative AI tech stack.
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