Who does what best in AI — 2026 map.
The AI world is modular. Models, retrieval, vector databases, orchestration, infrastructure, and governance are different layers — and different vendors win at different layers. Use this to shortlist fast, not to get lost in comparisons.
Core LLM providers
The “brains.” Everything else is how you connect brains to your data, tools, and rules.
General-purpose models and a broad developer ecosystem. The default starting point for most teams building with AI.
Strong reasoning and a safety-first posture. Best for high-stakes outputs where defensibility matters.
Multimodal research and deep ecosystem reach. Best when you need vision, audio, and text in one stack.
Retrieval & RAG stack
These tools connect LLMs to your documents so the model can look it up instead of guessing. The truth layer.
RAG pipelines, loaders, indexing, and evaluation. Start here when your problem is “my knowledge is messy.”
Orchestration layer for retrieval and tool use. The glue between your data, your model, and your workflows.
Retrieval-first AI search with citations. Use for fast research and claim validation — not internal knowledge management.
Vector databases
Where embeddings live. Used for semantic search, RAG retrieval, and similarity matching at query time.
Managed vector database built for production scale. Best when you need reliability and speed without managing infrastructure.
Open-source vector database with strong hybrid search. Best for privacy-first stacks and custom deployments.
Fast vector search engine, open-source and managed. Best when raw retrieval performance is the top priority.
Related pages: Data, RAG & Discovery Tools →
Agents & orchestration
Tool use and multi-step workflows. This is where “chat” becomes “do the job.” Guardrails required.
Multi-agent task execution framework. Define roles, assign tasks, and coordinate agents on complex workflows.
Self-hosted workflow automation with AI integration. Full control, data sovereignty, and advanced logic for ops builders.
No-code automation across thousands of apps. Fast to set up, broad integrations, best for teams without engineering resources.
Infrastructure & compute
Where the horsepower comes from — GPUs, cloud, and managed AI services at scale.
GPU compute and AI acceleration. The hardware layer most AI infrastructure depends on, from training to inference.
Cloud infrastructure and managed AI services with the broadest toolset. Best for teams already in the AWS ecosystem.
Enterprise AI with strong governance integration. Best for regulated environments and teams in the Microsoft stack.
Governance & compliance
Risk controls, audits, and policy layers for regulated environments. Not optional when decisions affect people.
Model risk and governance tooling for enterprise AI programs. Auditable, policy-driven, and built for regulated industries.
AI risk frameworks and implementation advisory. Use when you need external governance expertise and a structured rollout.
Policy-driven AI oversight and accountability tooling. Use when you need a dedicated governance layer across multiple models.
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