Manpower, budget, and time!!
AI technology has been invaluable to businesses in all sectors. Over the past year, AI has become even more impactful.
According to Exploring Topics, over 250 million businesses around the world are using AI. One of the ways they are taking advantage of it is with generative AI technology.
When embarking on the generative AI journey, carefully assessing resources, expertise, budget, and timelines is paramount. Building an in-house model demands deep knowledge, hefty costs, and prolonged development, forcing organizations to make a critical choice: invest heavily in bespoke creation or leverage the speed and accessibility of pre-built solutions.
Before penning this blog, I reached out to Ragoth Sundararajan, Vice President of Advanced Analytics & Generative AI at Indium Software. While I was explaining my ideas, I came up with the research, and this is what he asked me: a set of questions.
“When we ask ‘build vs buy,’ we should clearly specify the premise. Which part of the Gen AI models are we considering? For example, the humongous pre-trained models like GPT or Llama – for most people, ‘build’ is not an option as the cost is prohibitive. There, we have to ‘buy’ if access to such models is not free. When you talk about ‘build,’ do you mean customization or fine-tuning on top of pre-trained LLM?”
He is right that the “build vs. buy” question in generative AI needs to be carefully framed. When it comes to humongous pre-trained models like GPT-3 or Llama, building simply isn’t feasible for most due to the enormous cost and expertise required. In these cases, buying or accessing pre-trained models through APIs is the only viable option. However, the conversation becomes more nuanced when considering customization and fine-tuning on top of these pre-trained models.
Here’s a more technical breakdown!
Gen AI Tech Specifics
- Foundational Model Selection: The choice of pre-trained model depends heavily on your specific needs and resources. GPT-3 and Jurassic-1 Jumbo are powerful but expensive, while smaller models like BLOOM and EleutherAI’s WuDao 2.0 offer more affordable alternatives with decent performance.
- Significance of RAG (Retrieval-Augmented Generation): RAG integrates retrieval techniques into the generation process, allowing models to access and leverage relevant information from external databases. This can significantly improve factual accuracy and task-specific performance. Imagine your AI as a detective, searching through a vast library of text and code for clues. RAG empowers it to do just that, weaving snippets from this library into its own creative tapestry. This approach is perfect when you need your AI to be factually accurate and grounded in real-world data.
- Implementation Complexities: Fine-tuning and customizing pre-trained models involve technical challenges. You’ll need expertise in deep learning frameworks like TensorFlow or PyTorch, access to powerful GPUs or TPUs, and potentially significant data resources for fine-tuning.
- Productionizing and LMOps: Moving a fine-tuned model to production requires robust infrastructure, monitoring, and operational processes. This includes version control, security measures, and continuous performance monitoring (LMOps) to ensure model stability and reliability.
- Prompt Engineering: Think of prompts as the whispers in your AI’s ear, guiding its creative journey. This approach involves crafting the perfect set of instructions, like a map leading to the creative treasure you seek. It’s a delicate art, but when mastered, it unlocks a world of possibilities, allowing you to direct your AI’s imagination with precision.
Build vs. Buy in Different Contexts
- Building custom pre-trained models: Only feasible for large organizations with deep pockets and expertise. Offers maximum control and customization but comes at a high cost.
- Fine-tuning pre-trained models: More accessible option for smaller teams and startups. Requires technical expertise but offers good balance of performance and cost. This classic approach is like adding a custom touch to a ready-made suit. You tweak the model’s internal parameters, like adjusting the collar or the lapels, to fit your specific needs. It’s a powerful and versatile tool, but requires a deep understanding of the model’s inner workings.
- Using pre-trained models through APIs: Easiest and fastest option, but limited customization and control. Costs can vary depending on usage.
Ultimately, the decision to build vs. buy depends on your specific needs, resources, and technical capabilities. If you require highly customized models for critical tasks, building might be justifiable despite the challenges. However, for most cases, fine-tuning pre-trained models or leveraging API access offers a more practical and cost-effective approach. Despite these hurdles, the potential for tailored solutions and proprietary technology underscores the allure of embarking on this transformative journey.
| Pros | Cons |
| Customization and control | Technical expertise required |
| Integration flexibility | Maintenance and upgrades |
| Intellectual property | High costs |
| Scalability | Time-to-market delay |
Buying a Generative AI platform
Opting for a pre-built platform offers rapid deployment and immediate access to a suite of functionalities, minimizing time-to-market and accelerating ROI. Additionally, it alleviates the burden of infrastructure development and specialized hiring, allowing businesses to allocate resources elsewhere. The assurance of ongoing support, maintenance, and data security provided by reputable vendors further underscores the appeal of this approach. However, limitations in customization and dependence on the vendor for updates and improvements pose potential drawbacks alongside the long-term cost implications of subscription fees.
Ultimately, the decision hinges on carefully balancing needs, resources, and risk tolerance. While pre-built solutions offer speed and convenience, custom-built models afford greater flexibility and control over tailored workflows. Businesses must carefully assess their priorities, considering scalability, long-term sustainability, and alignment with budgetary constraints. By thoroughly weighing the pros and cons of each approach, organizations can make an informed decision that best suits their unique circumstances and objectives.
| Pros | Cons |
| Rapid deployment and out-of-box functionality | Limited customization |
| Reduced development effort | Dependency on vendor |
| Support, maintenance, and reliability | Cost |
| Data and privacy security | Risk of vendor lock-in |
Additional considerations
- Hybrid approach: You can combine elements of both approaches by building a custom model on top of a pre-built platform. This can give you the best of both worlds – flexibility and speed.
- Open-source models: Consider using open-source LLMs as building blocks for your custom solution. This can be a cost-effective way to get started with generative AI.
- Partner with LLM experts: Seek expertise from specialized LLM consultancies to guide your journey and help you make the best decision for your organization.
But it’s not all sunshine and rainbows: Strategic decision-making
Customization vs. Go live
- Organizations seeking complete control and customization may lean towards building.
- Those prioritizing rapid deployment, cost-efficiency, and easier implementation may prefer buying.
Expertise and resource allocation:
- Building requires a dedicated team with specialized skills, which might divert resources from core competencies.
- Buying allows organizations to leverage the expertise of AI specialists without investing in an in-house team.
Risk mitigation:
- Organizations that have struggled with internal development or face uncertainties may find buying a more practical and risk-mitigating solution.
Scalability and future-proofing:
- Buying offers scalability with a pay-as-you-go approach, allowing organizations to handle increasing user demands effectively.
Striking the right balance
Navigating the “build vs. buy” conundrum for Generative AI tools hinges on a delicate balance between strategic objectives, resource constraints, and deployment timelines. Building grants unparalleled customization, which necessitates sizeable investments in expertise and infrastructure. Conversely, buying pre-built solutions boasts rapid deployment and seamless support, enabling quicker access to cutting-edge technology. Though purchasing often serves as the preferred path for organizations seeking swift adoption and efficient resource allocation, it does entail relinquishing some control over customization. Ultimately, the optimal choice arises from a meticulous assessment of specific needs, capabilities, and long-term vision.
Security, vendors, and your path to GenAI success!
Security and privacy considerations
Regardless of the chosen path, robust security measures and compliance with data protection regulations are paramount. Building a generative AI platform requires organizations to implement these measures independently, whereas reputable vendors prioritize data and privacy security in pre-built solutions.
The importance of choosing the right vendor
The success of a purchased generative AI platform hinges on selecting a reliable vendor with a proven track record. Ongoing support, updates, and alignment with technological trends are crucial factors. Rigorous research is necessary to identify a company that meets current needs and can sustain a long-lasting relationship.
Addressing unique requirements
While pre-built solutions offer out-of-the-box functionality, organizations with unique or specialized needs should carefully evaluate the customization limitations. Building may become a more attractive option if a solution cannot adequately align with specific requirements.
Looking to the future: Adapting to technological trends
Given the pace of technological advancements, organizations must choose solutions that remain aligned with evolving trends. Buying a generative AI platform service can offer continuous updates, ensuring that the architecture remains up-to-date.
Final thoughts: A strategic approach to Generative AI
Navigating the “build vs. buy” conundrum in Generative AI requires a nuanced approach. While pre-built LLM platforms offer rapid deployment and ongoing support, their limited customization might not suit your bespoke needs. Building your own LLM, with its unparalleled control and intellectual property potential, demands significant resources and expertise. For humongous pre-trained models like GPT or LaMDA, buying is often the only realistic option due to their prohibitive costs. Ultimately, the decision hinges on your specific goals: Do you prioritize fine-tuning and customization on top of an existing LLM, or rapid access to out-of-the-box functionality? Choose wisely, considering your resources, risk tolerance, and the ever-evolving landscape of Generative AI. Remember, your path is not just about technology; it’s about building a future powered by the magic of AI.
