Every enterprise AI journey begins with a choice: build it yourself, buy a vendor's black-box solution, or adopt a flexible platform approach. On the surface, building in-house seems like the path to maximum control and cost savings. But the hidden costs of DIY AI—technical debt, talent scarcity, and the relentless pace of the field—often make it the most expensive option. Meanwhile, off-the-shelf solutions promise quick wins but lock you into rigid workflows that rarely fit your unique business logic.
The platform approach—a middle path that provides robust infrastructure and best-practice components while preserving customization—has emerged as the strategic winner for most mid-sized enterprises. This guide breaks down the real costs of each approach and explains why platforms like Smaltsoft's smalt core are designed to give you the best of both worlds.
1. 🛠️ The DIY Trap: Building Everything from Scratch
The allure of building AI in-house is strong. You have complete control over the architecture, the data never leaves your infrastructure, and you are not paying licensing fees to a vendor. But this path is deceptively expensive.
The Hidden Costs:
- Talent Acquisition: You need a team of ML engineers, data scientists, and MLOps specialists. These are among the most expensive and hardest-to-hire roles in tech. A senior ML engineer in a competitive market can command $200K+ annually.
- Infrastructure & Tooling: You will need to build or integrate: data pipelines, model training infrastructure (likely cloud GPUs), model registries, experiment tracking, deployment automation, monitoring dashboards, and A/B testing frameworks. Each of these is a project in itself.
- Constant Re-Engineering: The AI landscape evolves rapidly. The state-of-the-art model or technique from six months ago is often obsolete today. Your team will spend significant time just keeping up with the field, constantly re-platforming and rewriting code.
- Opportunity Cost: While your engineers are building infrastructure, they are not building features that directly serve your customers or solve your core business problems.
For a typical mid-sized enterprise, the true cost of a DIY AI initiative—factoring in salaries, infrastructure, and opportunity cost—easily exceeds $1M annually before a single model is in production.
2. 🔒 The Off-the-Shelf Illusion: Vendor Lock-In and Rigidity
On the opposite extreme, many vendors offer "AI in a box." These are fully managed solutions: you upload your data, click a few buttons, and out comes a model or a chatbot. The promise is simplicity and speed.
The Problems:
- One-Size-Fits-None: These solutions are built for the generic use case. If your business process is even slightly unique, you will find yourself fighting the tool, trying to force it to do something it was not designed for.
- Vendor Lock-In: Your data, your models, and often your workflows become deeply intertwined with the vendor's proprietary platform. Migrating away becomes prohibitively expensive.
- Black-Box Models: You often have no insight into how the model works, what data it was trained on, or why it made a specific decision. This is unacceptable for regulated industries or high-stakes applications.
- Cost Escalation: The initial pricing is attractive, but as you scale or need additional features, the costs grow rapidly. You are at the mercy of the vendor's pricing model.
Off-the-shelf solutions work well for commodity use cases (e.g., generic sentiment analysis), but they crumble when you need to solve a problem that is core to your competitive differentiation.
3. 🏝️ The Platform Approach: Flexibility Without the Overhead
A platform approach provides the infrastructure, best practices, and reusable components, but it does not dictate your business logic or lock you into a specific workflow. It is the "Lego set" for AI: you get the high-quality building blocks, and you assemble them to fit your unique needs.
What a Good AI Platform Provides:
- Pre-Built Infrastructure: Data connectors, model hosting, orchestration engines, monitoring, and logging. All the "plumbing" is handled.
- Best-Practice Patterns: The platform codifies proven architectural patterns (e.g., RAG, agent orchestration, fine-tuning workflows) so you do not reinvent the wheel.
- Model Agnosticism: A good platform is not tied to a single AI provider. It supports OpenAI, Anthropic, open-source models, and your own custom models. You are free to choose the best tool for each task.
- Customization Without Complexity: You can plug in your own business logic, integrate with your existing .NET systems, and customize the workflows without needing to understand or maintain the underlying infrastructure.
- Transparent & Auditable: You retain full visibility into what the system is doing. Logs, metrics, and decision trails are all accessible, which is critical for compliance.
The Cost Advantage:
With a platform, you avoid the massive upfront investment of DIY (no need to hire a full ML infrastructure team) and the rigidity and lock-in of off-the-shelf (you control your data and workflows). Your team can focus on solving business problems, not on managing Kubernetes clusters or debugging data pipelines.
4. 📊 Case Study: A Mid-Sized Manufacturer's Journey
A manufacturing company with 500 employees wanted to build an AI system to optimize their supply chain. They initially tried the DIY route, hiring two ML engineers. After 18 months and $500K in salaries and cloud costs, they had a prototype that worked for one specific scenario but was brittle and hard to maintain.
They then evaluated an off-the-shelf supply chain AI tool. It was fast to set up but could not handle their unique multi-site, multi-vendor workflow. Customization requests were either denied or quoted at exorbitant prices.
Finally, they adopted a platform approach (Smaltsoft's smalt core). Within three months, they had a production-ready system. The platform provided the RAG infrastructure, the agent orchestration, and the model integration. The company's .NET developers built the custom business rules and integrated it with their existing ERP. Total cost: ~$150K, including platform fees and internal developer time. The system has been in production for over a year, handling thousands of optimization decisions daily.
5. 🛡️ The Strategic Imperative: Focus on Your Competitive Advantage
The ultimate question is: where should your company's engineering talent focus? If you are a tech company whose core product is AI, then DIY makes sense. But if you are a manufacturer, a financial services firm, or a healthcare provider, your competitive advantage is not in building ML infrastructure—it is in applying AI to your unique domain expertise.
A platform allows you to leverage world-class AI infrastructure and best practices while focusing your team on what truly differentiates your business. It is the difference between building your own web server from scratch versus using a mature web framework like ASP.NET Core. The latter does not limit your creativity; it accelerates it.
At Smaltsoft, we have built smalt core specifically for mid-sized enterprises that need the power and flexibility of custom AI without the overhead of building everything in-house. It is a platform that respects your existing .NET investments, integrates seamlessly with Azure, and gives you the tools to move fast while maintaining control. This is the future of enterprise AI: strategic, sustainable, and focused on value, not infrastructure.