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: Versioned 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 + 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."

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 complexity)
• Vector DB - managed vs self-hosted (depends on scale)
• Monitoring - custom vs off-the-shelf tool (depends on 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 for tracking
Phase 2 (MVP): You add a database, a simple frontend, basic monitoring
Phase 3 (Production): CI/CD, proper logging, alerting, backup
Phase 4 (Scale): Now consider an MLOps platform, feature store, etc.

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.

Summary

The most expensive tool is the one you don't use. Start simple, add when it hurts. An AI stack is not an arms race - it's a pragmatic choice of tools that solve concrete problems.

This article is based on hands-on experience from AI deployments at mid-sized companies.