Tech Stack
Building an AI startup is not about finding one “perfect” tool or model.
Real products are built by connecting multiple layers - data, pipelines, models, infrastructure, and user experience - into a stack that works reliably from experimentation all the way to production.
That is why thinking in terms of an end-to-end system matters far more than chasing individual tools.
Below is a clean breakdown of the AI tools tech stack for startups, explaining what each layer does and why it exists:
- Data Storage:
AWS S3, Google BigQuery, Azure Data Lake — Store raw and structured data reliably for training, analytics, and experimentation.
- Data Processing:
Apache Airflow, Apache Spark, dbt — Orchestrate pipelines, transform data, and prepare clean datasets for ML workflows.
- ML Frameworks:
TensorFlow, PyTorch, Scikit-learn — Build, train, and experiment with machine learning models.
- NLP / Vision:
Hugging Face Transformers, OpenCV, MLflow — Power language and vision use cases while tracking experiments and models.
- Model Serving:
TensorFlow Serving, TorchServe, Flask APIs — Expose trained models as scalable, production-ready APIs.
- Cloud AI Platforms:
AWS SageMaker, Google Vertex AI, Azure ML — Manage training, deployment, and scaling without heavy infrastructure overhead.
- Monitoring:
ELK Stack, Grafana, Prometheus — Track system health, model performance, logs, and drift in production.
- Frontend / UI:
React, Node.js, FastAPI, Streamlit — Turn AI models into usable products and interactive experiences.
- MLOps / CI-CD:
Kubeflow, GitHub Actions — Automate training, testing, and deployments for repeatable and reliable releases.
- Collaboration & Versioning:
DVC, GitHub - Version data, models, and code so teams can move fast without breaking things.
You don’t need every tool on day one.
What matters is understanding how each layer fits into the system so your AI product can scale smoothly as you grow.
