Home /Resources /AI Tech Stack Guide
The Complete
AI Tech Stack Guide
Every layer of a production AI system explained — from raw data infrastructure to live application — with tool recommendations, honest trade-offs, and the exact stacks we deploy across 200+ enterprise projects.
Trusted by industry leaders worldwide
What Is an AI Tech Stack?
An AI tech stack is the complete collection of tools, platforms, and infrastructure that work together to power an AI system — from raw data at the bottom all the way to the live application your users interact with at the top.
Choosing the wrong tool at any layer creates compounding problems. A weak data pipeline makes great models impossible. Poor MLOps makes great models unreliable. Stack decisions made early are expensive to undo.
This guide covers all 7 layers with honest verdicts from 200+ production deployments — not vendor marketing.
Jump to the Layers ↓Every Layer. Honest Verdicts.
Click any layer to explore the tools, trade-offs, and exactly what Sabalynx recommends from real deployments.
The foundation everything else sits on. Your cloud provider determines GPU availability, regional compliance, cost structure, and how well every other tool integrates. This is the hardest decision to reverse — choose it deliberately.
The largest cloud ecosystem with the widest ML-specific service catalogue. SageMaker, Bedrock, Rekognition, and a deep partner network make AWS the safest default for most enterprise AI workloads.
Best choice for Microsoft-heavy organisations. Azure OpenAI Service gives managed GPT-4 access with enterprise data protection, and the Active Directory / Microsoft 365 integration is unmatched for governance.
Google’s own AI research flows directly into GCP. Vertex AI, TPU access, Gemini API, and BigQuery make it compelling for ML-heavy workloads and analytics-first organisations.
Required for organisations with strict data sovereignty rules — defence, regulated healthcare, certain financial regulators. NVIDIA DGX systems or private OpenShift clusters handle sensitive workloads on-site.
Specialist GPU cloud providers with H100/A100 access at lower cost than hyperscalers. Useful for burst training runs when AWS/GCP GPU capacity is constrained or costs prohibitive.
Getting data from source systems into your AI infrastructure reliably, at scale, and on schedule. Underinvestment here is the single most common reason AI projects fail — no matter how good the model, garbage data in means garbage predictions out.
Industry-standard workflow orchestrator for data pipelines. Define pipelines as Python DAGs, schedule them, monitor execution, and retry failed tasks automatically. Managed versions on AWS (MWAA), GCP (Cloud Composer), and Astronomer.
Transforms raw data inside your warehouse using SQL with version control, testing, and documentation. Turns ad hoc queries into production-grade data models that reliably feed AI features.
Distributed event streaming for real-time data pipelines. When your AI needs to react to events as they happen — fraud alerts, live recommendations, real-time monitoring — Kafka is the backbone.
Pre-built connectors that sync data from 300+ sources (Salesforce, Shopify, databases, SaaS APIs) into your warehouse automatically. Fivetran is managed enterprise; Airbyte is open-source self-hosted.
Fully managed ETL services from major cloud providers. Lower operational overhead than self-managed Airflow, tightly integrated with their respective cloud ecosystems.
Where your data lives between ingestion and model training or inference. The split between data lake (raw storage) and data warehouse (processed, queryable) is a critical architecture decision. Get this wrong and every query and training run becomes painful.
Cloud-native data warehouse with instant scaling, zero-copy data sharing, and deep BI integrations. Snowflake Cortex now adds built-in vector search and LLM functions — reducing stack complexity for AI workloads.
Unifies data lake and warehouse into a single lakehouse architecture on Delta Lake. MLflow integration makes it a strong end-to-end ML platform when combined with Unity Catalog for governance.
Serverless analytical warehouse with built-in ML functions (BQML) and Vertex AI integration. Exceptional performance on petabyte-scale queries — no infrastructure to manage.
Purpose-built vector databases for storing and searching embeddings. Essential infrastructure for RAG systems, semantic search, and recommendation engines that need fast similarity search at scale.
S3 as object storage data lake, Athena for serverless SQL queries on top. Simple, cheap, and massively scalable. Best for raw data landing zones before processing — not a replacement for a proper warehouse.
In-memory data store used as a feature store for low-latency serving of pre-computed ML features at inference time. When a model needs features in under 10ms — real-time fraud, live recommendations — Redis is the answer.
The difference between a model that works in a notebook and one that reliably serves predictions in production at scale. MLOps covers CI/CD for models, experiment tracking, model registry, monitoring, and automated retraining. Most AI projects underinvest here — and quietly pay the price months later.
Open-source platform for experiment tracking, model registry, and deployment. Log parameters, metrics, and artefacts across every training run. Compare experiments side-by-side. Promote models through staging to production with full lineage.
Fully managed ML platform covering the full lifecycle: data labelling, training, hyperparameter tuning, model hosting, A/B testing, and drift monitoring — all on AWS. Reduces infrastructure management for teams without dedicated MLOps engineers.
Premium experiment tracking and model visualisation platform. Best-in-class UI for comparing training runs, visualising metrics across hundreds of experiments, and team collaboration. Popular in deep learning and LLM fine-tuning.
Dedicated ML observability platforms for detecting data drift, model performance degradation, and data quality issues in production. Evidently is open-source; Arize is enterprise-managed with deeper alerting and root-cause analysis.
For organisations needing full control over model serving infrastructure. Kubernetes manages containerised model servers; KServe provides standardised inference APIs with auto-scaling, canary deployments, and multi-model serving.
The core machine learning libraries your data scientists use to build, train, and evaluate models. Framework choice affects performance, iteration speed, hiring, and long-term maintenance. Most mature stacks use multiple frameworks for different use cases.
The dominant deep learning framework in both research and production. Dynamic computation graphs, intuitive debugging, and the largest research community. Hugging Face Transformers is built on PyTorch, making LLM fine-tuning straightforward.
The gold standard for classical ML on tabular data. scikit-learn covers every traditional algorithm with a consistent API; XGBoost and LightGBM consistently outperform neural networks on structured business data.
The hub for pre-trained models and the Transformers library. 500,000+ models, straightforward fine-tuning, and Inference Endpoints for managed hosting. Essential for any NLP, LLM, or computer vision work on foundation models.
Orchestration frameworks for building LLM applications — chains, agents, RAG pipelines, memory systems, and tool use. LangChain is broader; LlamaIndex specialises in retrieval and indexing for RAG.
Google’s deep learning framework. Was dominant before PyTorch. Still widely used in production due to better mobile/edge deployment (TFLite) and high-throughput serving via TensorFlow Serving.
The intelligence layer. Whether you build custom models from scratch, fine-tune open-source foundation models, or call hosted API models, this is where capability meets cost. The right answer depends on data sensitivity, latency, budget, and customisation requirements.
State-of-the-art general-purpose LLM with best-in-class reasoning, coding, and instruction following. GPT-4o adds multimodal capabilities (vision + text). Deploy via Azure OpenAI for enterprise data protection and compliance.
Excels at long-context document analysis (200K token window), nuanced writing, and safety-critical applications. Constitution-based training makes it a strong choice for regulated industries needing predictable, policy-aligned outputs.
Open-weight models deployable on your own infrastructure — no data leaves your environment. Llama 3 and Mistral models offer performance approaching GPT-4 on many tasks at a fraction of API cost at scale.
Google’s flagship multimodal model with a 1M token context window — the longest available. Strong for video understanding, very long document analysis, and deep GCP stack integration via Vertex AI.
Fine-tune domain-specific models on your proprietary data using LoRA or QLoRA. Best for consistent tone of voice, domain-specific terminology, and high-volume tasks where general models consistently underperform.
Model Selection Guide
| Requirement | Best Model | Why |
|---|---|---|
| Data must not leave org | Llama 3 / Mistral (self-hosted) | Full data sovereignty, no third-party API calls |
| Complex reasoning or coding | GPT-4o or Claude 3.5 Sonnet | Best-in-class on complex, multi-step tasks |
| Documents over 200K tokens | Gemini 1.5 Pro or Claude 3.5 | Largest context windows currently available |
| High-volume, cost-sensitive | GPT-4o mini / Haiku / open-source | 10–20x cheaper, sufficient for many production tasks |
| Domain-specific terminology | Fine-tuned open-source model | Better consistency, lower cost at scale once trained |
| Video understanding | Gemini 1.5 Pro | Only major model with strong video analysis support |
| Microsoft ecosystem already | GPT-4o via Azure OpenAI | Enterprise data protection, compliance guarantees |
The layer users actually see and interact with. AI value is only realised when it is embedded in workflows people use every day. This covers dashboards, chatbots, APIs powering downstream systems, and tooling that lets non-technical teams act on AI outputs.
Python-native framework for building data apps and AI interfaces in hours, not weeks. Data scientists ship interactive dashboards, model playgrounds, and internal tools without needing a frontend developer.
High-performance Python framework for building AI model APIs. Async support, automatic OpenAPI documentation, Pydantic validation, and near-Go performance make it the standard for production AI service endpoints.
Enterprise BI platforms increasingly embedding AI features — natural language queries, anomaly highlighting, predictive analytics. Best when AI insights need to live inside dashboards business teams already use daily.
Even faster than Streamlit for ML model demos. Three lines of Python to wrap any model with a web interface. Hugging Face Spaces hosts Gradio apps for free. Ideal for stakeholder demos and model evaluation UIs.
No-code workflow automation platforms with AI nodes for connecting LLM calls, databases, and business apps without engineering overhead. Useful for embedding AI into operational workflows for non-technical teams.
Three Proven Starting Points
Pick the tier that matches your budget, team size, and AI maturity. Each is a real starting point used on real engagements — not a theoretical ideal.
Stack Questions
Want a stack designed specifically for your situation? We do this in every free consultation.
Get Stack Advice →Want a Stack Designed
for Your Specific Needs?
This guide covers general recommendations. Your free consultation gives you a stack architecture designed around your actual data, team skills, compliance requirements, and budget — with tool selections we would stake our reputation on.