AI Tools Landscape

The products that implement each building block, grouped by block. Every name here sits at the product level of the taxonomy: a choice made after the capability, the pattern and the components are clear.

79 of 79 · reviewed 16 October 2026

Foundation models

Building block 1 · component

Model providers

Frontier models consumed through an API.

Open-weight models

Models whose weights can be downloaded and run anywhere.

Cloud model platforms

Many models behind one cloud contract, with hosting and controls.

Model serving

Run models on your own infrastructure.

Fine-tuning

Adapt a model to a task or a style.

RAG and knowledge

Building block 2 · pattern, built from these components

Vector databases

Store embeddings and find passages by meaning.

Embedding models

Turn text into vectors for retrieval.

RAG frameworks

Wire retrieval, prompts and models together.

Document parsing

Turn PDFs, scans and pages into clean text for indexing.

AI agents

Building block 3 · pattern; a running agent is a component

Agent frameworks

Code libraries for building agents that plan and use tools.

Managed agent platforms

Build and run agents inside a cloud or business platform.

MCP servers and tools

Building block 4 · a standard, and the components that implement it

The MCP standard and SDKs

The specification, and the libraries for building MCP servers and clients.

MCP servers

Ready-made servers that expose a system’s functions as tools.

AI and API gateways

One controlled front door to models and tools: routing, keys, limits and logs.

Workflow orchestration

Building block 5 · component, usually bought as a platform

Workflow automation

Triggers, steps, approvals and AI calls across systems.

Durable workflows

Long-running processes that survive failures and retries.

Intelligent automation

Robotic process automation with AI steps inside.

Data platforms

The data layer under every block

Data and AI platforms

Trusted data for analytics, machine learning and retrieval.

Trust and operations

Across every block: guardrails, evaluation and observability

Guardrails and AI security

Check what goes into and comes out of a model.

Evaluation and testing

Measure answer quality before and after go-live.

LLMOps and observability

Trace requests, cost and quality in production.

Examples, not recommendations: no ranking, and no claim beyond what each product is. Links open each vendor’s or project’s own site. Products are renamed and acquired often; the names were checked on 16 October 2026.