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:
- Data engineering: Managing, processing, and governing the data that feeds AI
- Analytics: Making AI outputs actionable for real business decisions
- MLOps: Building, training, and serving traditional predictive models
- AIOps: Orchestrating LLMs, agents, and frontier AI applications
Let’s get into it.
1. Data engineering: Where AI excellence begins
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.
Data governance & quality: The security and control foundation
Enterprise AI demands rigorous data governance—not just for compliance, but for maintaining the security and control that make AI initiatives viable at scale.
- Unity Catalog and Data Hub lead the data governance space, providing the metadata management and access controls that enterprise security teams require.
- Great Expectations ensures data quality validation, preventing the garbage-in-garbage-out problem that has derailed countless AI projects.
- Amundsen provides data discovery and metadata management, offering another robust option for data governance and cataloging.
Modern data storage: Built for AI workloads
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:
- Apache Iceberg and Delta Lake provide the lakehouse architecture that combines data warehouse reliability with data lake flexibility, giving teams control over their data formats.
- PostgreSQL with PGVector has emerged as the pragmatic choice for vector databases, offering enterprise-grade security and the control that comes with open-source solutions.
- Neo4j and AWS Neptune power graph RAG systems, enabling AI applications to understand relationships and context while maintaining data governance controls.
Pipeline orchestration: From simple to complex
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.
- Airbyte handles straightforward data movement between systems.
- Apache Airflow orchestrates complex DAG-based workflows, which is essential for multi-step AI pipelines that combine data processing, model training, and inference.
- dbt transforms raw data into analysis-ready datasets, bridging the gap between data engineering and AI teams
- Kubeflow Pipelines
Specialized AI data processing
Modern AI applications, particularly in regulated industries, require specialized data processing capabilities. These tools help facilitate getting even complex data formats ready for AI.
- Docling extracts tables from documents, which is critical for financial RAG systems where structured data locked in PDFs drives decision-making.
- Faker generates synthetic data for testing and development, enabling AI teams to work with realistic datasets without compromising privacy.
Compute & infrastructure: Managing the resource-hungry reality
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.
Parallel computing: Scale without complexity
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.
- Ray has become the distributed computing framework of choice for ML workloads, offering seamless scaling from laptops to clusters.
- Apache Spark remains essential for large-scale data processing, particularly when integrating with existing data infrastructure.
- SkyPilot optimizes multi-cloud compute costs by automatically moving workloads to the most cost-effective cloud resources.
Cost management: Visibility and control
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.
- OpenCost provides Kubernetes cost monitoring and allocation, enabling teams to understand and optimize their AI infrastructure spending
The key is balancing innovation velocity with cost efficiency. Teams need the freedom to experiment while maintaining budget accountability.
2. Analytics: Making AI insights actionable
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
Visualization & analysis: Making AI insights accessible
Effective visualization transforms AI model outputs into actionable business insights. These tools bridge the gap between AI capabilities and business decision-making.
- TensorBoard provides training metrics visualization, essential for understanding model behavior during development.
- AutoViz automates exploratory data analysis, helping teams understand their data before building models.
- Apache Superset delivers business intelligence dashboarding, making AI insights accessible to non-technical stakeholders.
Monitoring & observability: Learning from billion-dollar mistakes
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.
Platform monitoring: The foundation layer
Basic infrastructure monitoring is still important, even as AI-specific monitoring tools emerge. These tools provide the fundamental observability needed for reliable AI operations.
- Prometheus provides metrics collection and alerting for the underlying infrastructure supporting AI workloads.
- Grafana delivers metrics dashboarding and visualization, giving teams visibility into system performance and resource utilization.
AI-specific monitoring: Beyond traditional metrics
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.
- Evidently specializes in data and model drift detection, helping teams identify when their models are operating outside their training distribution.
- NannyML provides comprehensive model performance monitoring, tracking not just technical metrics, but business impact.
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.
3. MLOps: The traditional predictive powerhouse
MLOps remains the backbone of enterprise predictive analytics—even as newer agentic and LLM-powered systems capture the spotlight.
Experiment tracking & model management: The scientific method for AI
Reproducible experiments and model management separate professional AI development from research prototypes. These platforms provide the foundation for systematic AI development and deployment.
- MLflow has become the standard for experiment tracking and model registries, offering the right balance of simplicity and functionality.
- ClearML is a complete MLOps platform for teams that want integrated experiment management, data versioning, and pipeline orchestration.
- Weights & Biases (W&B) excels at advanced visualization and collaborative experiment analysis, particularly valuable for complex deep learning projects.
BLOG: MLOps explained
Training frameworks: PyTorch's dominance
The following frameworks provide the core engines for building and training AI models.
- PyTorch works across both traditional ML and modern AI applications, offering the flexibility and performance that research and production teams demand.
- XGBoost remains the go-to choice for tabular data and structured prediction tasks, often outperforming neural networks on traditional business datasets.
AutoML & hyperparameter optimization: Automating model selection
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.
- Ray Tune provides scalable hyperparameter optimization that can distribute experiments across clusters.
- Optuna powers many AutoML tools behind the scenes, offering sophisticated optimization algorithms.
- PyCaret democratizes model selection by automating the comparison of different algorithms and hyperparameter configurations.
Model serving: Separating infrastructure from models
Model serving typically involves two components: inference servers (which manage deployment complexity) and model servers (which host the models themselves).
Inference servers for production management:
- Ray Serve handles complex deployment scenarios including A/B testing, canary deployments, and ensemble models.
- KServe provides Kubernetes-native model serving for teams already invested in container orchestration.
Model servers for core hosting:
- vLLM delivers high-performance LLM inference with optimized memory management
- Ollama enables local LLM serving for development and private deployments
- Triton offers NVIDIA's optimized inference server for GPU-accelerated models
Feature engineering & data versioning
The supporting infrastructure for ML development requires specialized tools for managing features and datasets.
- Feast provides feature store capabilities, ensuring consistent feature computation between training and serving.
DVC brings version control to datasets and ML models, making experiments reproducible.
4. AIOps: The cutting edge of agentic AI
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.
LLM orchestration: Building intelligent applications
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.
- LangChain has become the standard for LLM application development, offering extensive integrations and a mature ecosystem.
- LlamaIndex specializes in data-centric LLM applications, particularly excelling at RAG implementations.
- Pydantic provides the data validation and settings management that production LLM applications require.
- Flowise offers visual LLM workflow building for teams that prefer low-code approaches.
Agentic AI: The hottest thing in AI right now
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.
- LangGraph enables sophisticated multi-agent workflows, representing the latest advances in agent coordination and task decomposition.
- CrewAI provides collaborative agent frameworks that embody cutting-edge approaches to multi-agent cooperation.
- AutoGen facilitates multi-agent conversation frameworks, incorporating the newest research in agent communication and consensus.
- Pipecat and LiveKit power real-time voice and video agents, bringing the latest breakthroughs in conversational AI to production.
BLOG: A business guide to agentic AI
Fine-tuning & training: Customizing foundation models
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.
- Unsloth offers cutting-edge fine-tuning frameworks that dramatically reduce training time through the latest optimization techniques.
- TRL (Transformer Reinforcement Learning) enables access to the newest RLHF and advanced fine-tuning methodologies.
- DeepSpeed incorporates the latest advances in large-scale model training optimization.
- LoRA techniques provide access to state-of-the-art parameter-efficient fine-tuning methods.
- QLoRA combines the newest quantization approaches with LoRA for maximum efficiency.
Prompt engineering & evaluation: The science of AI communication
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.
- DSPy automates prompt optimization, using machine learning to improve prompts rather than relying on manual iteration.
- PromptFoo provides comprehensive prompt evaluation and testing capabilities.
- Deepchecks, Ragas, and DeepEval offer model evaluation frameworks specifically designed for LLM applications.
Tracing & observability: Understanding complex AI workflows
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.
- LangFuse provides comprehensive LLM application observability, tracking every step in complex AI workflows.
- Phoenix (Arize) offers ML observability and evaluation specifically designed for production AI applications.
User interfaces: From prototype to production
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.
- Open WebUI enables teams to build chat interfaces without complex React development, dramatically reducing the time from prototype to demo.
- Streamlit provides quick web app prototyping capabilities, perfect for internal tools and proof-of-concepts.
- Vercel supports full-stack web development for teams building production AI applications.
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.
Specialized & vertical tools: Domain expertise matters
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.
Development environments: AI-powered coding
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.
- Jupyter Notebooks remain the standard for interactive AI development, providing the exploratory environment that data scientists require.
- Google Colab offers cloud-based notebooks with built-in GPU access, democratizing access to powerful compute resources.
- Cursor and Inline are a new generation of AI-powered coding tools that integrate directly with notebook environments to accelerate development.
Domain-specific integration
Different industries and use cases require specialized tools that reflect deep domain expertise.
- OHIF provides DICOM viewers for medical imaging AI, enabling healthcare teams to debug computer vision models by examining the actual medical images.
- Label Studio offers comprehensive data annotation and labeling capabilities, essential for supervised learning projects across industries.
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 path forward
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.
