AI Stack 2026: What You Actually Need

"You need an MLOps platform, a data lake, a feature store, a model registry, a vector database, LLMOps..." That's how a typical conversation with a vendor goes. In reality, most companies need far less.

Minimum Viable AI Stack

Before you buy yet another tool, check whether you have the fundamentals:

LAYER 1: DATA

Data Warehouse: Snowflake, BigQuery, or PostgreSQL for small companies.
ETL: Airbyte, Fivetran, or dbt for transformations.
Vector DB (if RAG): Pinecone, Weaviate, pgvector.

LAYER 2: MODELS

LLM API: OpenAI, Anthropic, Google.
Orchestration: LangChain, LlamaIndex, or your own code.
Prompts: Version them in Git, not in a spreadsheet.

LAYER 3: APPLICATION

Backend: FastAPI, Node.js, whatever you know.
Frontend: React, Vue, or integration with your existing app.
Auth & Security: Don't reinvent the wheel.

What you DON'T need at the start

Seriously, don't buy these before your first working pilot:

✗ MLOps platform: Git plus simple CI/CD is enough.
✗ Feature store: needed at scale, not at the start.
✗ Model registry: at the beginning you have 1-2 models.
✗ Enterprise AI platform: 6-figure cost, 12-month rollout.

"The best stack is the one you know. Don't learn Kubernetes just to ship a chatbot."

WORK WITH ME

This is what I do hands-on — advising on AI strategy and building agents that survive the demo.

AI consulting AI implementation

Build vs Buy: A decision framework

Every component of the stack is a build vs buy decision:

BUY (almost always)

• LLM: don't train your own language model.
• Infrastructure: cloud, not your own servers.
• Auth: Auth0, Clerk, not a homegrown solution.

BUILD (usually)

• Domain logic: nobody knows your business better.
• Legacy integrations: vendors don't support your 2005 ERP.
• Prompts and workflow: this is your IP.

DEPENDS

• Orchestration: LangChain vs your own code (depends on how complex your flow is).
• Vector DB: managed vs self-hosted (depends on scale).
• Monitoring: custom vs off-the-shelf tool (depends on team maturity).

Stack evolution

An AI stack grows along with your needs. Here's the typical path:

Phase 1 (Pilot): OpenAI API + a Python script + a spreadsheet to track results.
Phase 2 (MVP): you add a database, a simple frontend and basic monitoring.
Phase 3 (Production): CI/CD, proper logging, alerting, backups.
Phase 4 (Scale): now consider an MLOps platform, a feature store and the rest.

Real costs

For a mid-sized company (100-500 employees), a realistic AI stack can cost:

MONTHLY INFRASTRUCTURE COSTS

LLM API: 500-5,000 PLN (depends on volume).
Cloud (compute, storage): 1,000-3,000 PLN.
SaaS tools: 500-2,000 PLN.
Total: 2,000-10,000 PLN/month.

This is not a 6-figure budget. But it does require a 6-figure budget for the people who build and maintain it.

Where to start

The most expensive tool is the one you don't use. Start with the OpenAI API and a single Python script. Add the next tool only once a concrete problem actually starts to hurt.

I've deployed AI at mid-sized companies. That's where this comes from.