Enterprises are under more pressure than ever to incorporate AI into their workflows. But most are stuck stitching together a stack that was never built to scale. Bespoke, custom-built stacks offer flexibility, but demand significant engineering effort to integrate, secure, and maintain. On the other end, cloud-supplied AI platforms (e.g., AWS SageMaker) add some efficiencies, but are slow to evolve, tightly coupled to their respective ecosystems, and often incompatible with the latest open-source breakthroughs.
That’s why today’s most successful AI teams are building with modular, open-source components. We designed Cake to help enterprises put this approach into practice by offering a secure, composable platform for rapidly building compliant, production-ready AI systems using open-source tooling.
This guide breaks down the key elements of a best-in-class enterprise AI/ML stack, organized around the four foundational pillars that power AI at scale:
Let’s get into it.
All AI models ultimately derive from data, making data engineering the foundation of any enterprise AI stack. Whether you're building traditional predictive models or implementing complex RAG systems, the quality and accessibility of your data infrastructure determine your AI capabilities.
Enterprise AI demands rigorous data governance—not just for compliance, but for maintaining the security and control that make AI initiatives viable at scale.
Today's AI applications demand storage solutions that can handle both traditional structured data and modern AI workloads like vector search and graph analytics, while maintaining enterprise security and control standards. The following storage technologies provide the foundation for secure, scalable AI data infrastructure:
The orchestration layer separates basic data movement from sophisticated AI workflows. These tools enable teams to build and manage the data pipelines that feed AI systems.
Modern AI applications, particularly in regulated industries, require specialized data processing capabilities. These tools help facilitate getting even complex data formats ready for AI.
With robust data engineering as the foundation, teams need the computational infrastructure to process and train on this data at enterprise scale. Data scientists need access to the latest tools and massive computational resources, and their experimental workloads can rapidly consume compute budgets in unpredictable ways. This isn't a criticism; it's the nature of pushing the boundaries of what's possible with AI.
The solution isn't restricting access; it's building infrastructure that can scale efficiently while maintaining cost controls and security.
Modern AI workloads demand distributed computing, whether for training large models, hyperparameter tuning, or inference at scale. These frameworks provide the computational power needed for enterprise AI while maintaining simplicity.
With AI workloads consuming significant compute resources, cost monitoring becomes critical for sustainable AI operations. These tools provide the visibility needed to balance innovation with budget accountability.
The key is balancing innovation velocity with cost efficiency. Teams need the freedom to experiment while maintaining budget accountability.
Once the data pipeline is in place, analytics turns raw outputs into real business value—delivering visualizations and dashboards that make insights accessible and actionable.
This pillar encompasses visualization tools that transform AI outputs into business insights and monitoring systems that ensure reliable AI operations.
IN DEPTH: AI analytics, powered by Cake
Effective visualization transforms AI model outputs into actionable business insights. These tools bridge the gap between AI capabilities and business decision-making.
The importance of monitoring in AI systems cannot be overstated. Poor monitoring doesn't just lead to degraded performance—it can lead to catastrophic business failures. The cautionary tale everyone in AI knows: Zillow lost over a billion dollars during the pandemic because they weren't monitoring drift in its real estate pricing models. When real estate prices shifted dramatically, their unmonitored models continued making decisions based on pre-pandemic patterns.
Basic infrastructure monitoring is still important, even as AI-specific monitoring tools emerge. These tools provide the fundamental observability needed for reliable AI operations.
AI applications require specialized monitoring that goes beyond traditional system metrics to include model behavior and data quality. These tools provide the AI-specific observability needed to prevent costly model failures.
Monitoring AI systems requires understanding both the technical performance and the business context. Models can appear to be functioning correctly from a technical perspective while making increasingly poor business decisions due to data drift or changing market conditions.
MLOps remains the backbone of enterprise predictive analytics—even as newer agentic and LLM-powered systems capture the spotlight.
Reproducible experiments and model management separate professional AI development from research prototypes. These platforms provide the foundation for systematic AI development and deployment.
BLOG: MLOps explained
The following frameworks provide the core engines for building and training AI models.
Hyperparameter tuning is one of the most successful applications of automation in AI development. These tools automate the complex process of finding optimal model configurations.
Model serving typically involves two components: inference servers (which manage deployment complexity) and model servers (which host the models themselves).
The supporting infrastructure for ML development requires specialized tools for managing features and datasets.
DVC brings version control to datasets and ML models, making experiments reproducible.
AI Operations (AKA AIOps) is the fastest-moving category in enterprise AI, encompassing everything from LLM orchestration to multi-agent systems. This is where the most innovation happens, and where enterprises can gain the most competitive advantage. The modular approach becomes essential here, as new frameworks and capabilities constantly emerge from the open-source community.
The foundation of any LLM application requires orchestration frameworks that can handle complex workflows and data integration. These frameworks provide the building blocks for sophisticated AI applications.
Agents represent the cutting edge of AI applications—autonomous systems that can reason, plan, and take actions to achieve complex goals. This rapidly evolving space demands access to the latest open-source innovations as they emerge.
Fine-tuning is one of the fastest-evolving areas in AI, with new techniques and optimizations constantly emerging from the open-source community. Staying current with these innovations is crucial.
Effective prompt engineering has evolved from art to science, with sophisticated tools for optimization and evaluation. These tools help teams systematically improve AI application performance.
Modern AI applications involve complex multi-step workflows that require sophisticated observability tools.
Consider a typical enterprise RAG system: a user prompt gets transformed, triggers a hybrid search across vector databases, returns results that need re-ranking, feeds context to an LLM, potentially involves chain-of-thought validation, and finally presents results to the user. Understanding where this complex workflow fails requires specialized tracing tools.
Building user interfaces for AI applications has become significantly easier with specialized tools designed for AI use cases. These platforms enable rapid deployment of AI applications without extensive frontend development.
While the four core pillars provide the foundation for any enterprise AI stack, real-world applications often require additional specialized tools that address industry-specific requirements.
Enterprise AI applications often require domain-specific tools that generic platforms cannot provide. This is where the modularity of open-source solutions becomes particularly valuable. Teams can integrate specialized tools for their industry while maintaining consistency across the rest of their stack.
The integration of AI into development tools has fundamentally changed how teams build AI applications. These development environments accelerate AI innovation while maintaining code quality.
Different industries and use cases require specialized tools that reflect deep domain expertise.
When debugging AI models in specialized domains, you often need to examine the raw data using industry-specific tools. A medical imaging model requires DICOM viewers; a financial model might need specialized document viewers; a manufacturing model might require CAD integration.
The future belongs to organizations that solve the integration complexity of these different tools in the AI stack without sacrificing innovation velocity. Success requires platforms that provide enterprise-grade security and governance while delivering immediate access to cutting-edge open-source capabilities. This isn't just about technology selection—it's about building competitive moats in markets where AI capabilities determine market position.
The question isn't whether open source will dominate enterprise AI infrastructure. The question is whether your organization will lead or follow in this transformation.
This is why Cake is the clear choice for organizations where AI represents a competitive advantage. Teams building cutting-edge applications like sophisticated RAG systems or agentic workflows require the flexibility and innovation speed that only best-in-class open source platforms provide.