An AI stack can be effectively mapped by organizing vendors into distinct layers, starting with foundational models and labs. This approach helps in selecting the right model based on capability and cost, considering the Pareto frontier of performance versus price. Following this, agent frameworks and orchestration tools should be chosen based on how workflows chain together and state is managed. Grounding and retrieval layers involve data, vector databases, and knowledge graphs to provide context to models, with graph RAG being a complementary approach. Finally, layers for evaluation, observability, reliability, compute infrastructure, and developer tools ensure trustworthy AI deployment in production. AI
IMPACT Provides a framework for organizing and selecting AI vendors based on functional layers, aiding in system design and procurement.
RANK_REASON The item provides an opinionated guide on how to architect AI systems, rather than announcing a new product or research finding.
- Airbyte
- AMD
- Cohere
- Cohesity
- CrewAI
- Databricks
- Google DeepMind
- Honeycomb
- IBM
- LlamaIndex
- MongoDB
- Neo4j
- NVIDIA
- Oracle
- Snorkel AI
- Weaviate
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