Less Stack, More Ship
The AI tooling landscape has exploded. Every week brings new frameworks, platforms, and services. AI landscape maps now have so many logos they are unreadable at any reasonable zoom level. And the teams that are actually shipping production AI are using remarkably few tools.
Based on our work with production AI teams across industries, here is the stack that matters heading into 2026.
The Foundation Layer
Model access: Most production teams use two to three model providers. A primary frontier model from Anthropic, OpenAI, or Google for complex reasoning tasks, and a smaller, cheaper model for high-volume, simpler tasks. The trend toward model routing, automatically selecting the right model for each request based on complexity and cost constraints, is accelerating rapidly and becoming standard practice.
Orchestration: LangChain's early dominance has given way to lighter-weight alternatives and increasingly to custom orchestration code. The best teams use minimal frameworks, because every abstraction layer adds latency, complexity, and a point of failure that is hard to debug in production. If your orchestration framework is more complex than the AI logic it manages, you have an engineering problem masquerading as a tooling choice.
The Data Layer
Vector databases: The initial hype around specialized vector databases has settled considerably. Most teams are using vector search capabilities built into their existing databases. PostgreSQL with pgvector is remarkably popular and sufficient for the majority of enterprise workloads. Purpose-built vector databases still make sense at significant scale, but most enterprises are not at that scale yet, and premature optimization here wastes engineering time.
Data pipelines: The unsexy truth is that most enterprise AI data work is ETL: extracting data from source systems, transforming it into usable formats, and loading it into the systems that serve AI models. The tools that matter here are the same data engineering tools that mattered before the AI wave. Clean data in, good AI out. No amount of model sophistication compensates for broken pipelines.
The Operations Layer
Evaluation: This is the most underinvested layer and the one that matters most for production quality. Teams that build systematic evaluation frameworks, with automated testing of model outputs against defined criteria, ship better AI faster and with more confidence. Custom evaluation pipelines consistently outperform generic monitoring tools because they encode domain-specific quality standards.
Observability: Logging prompts, responses, latency, costs, and error rates for every AI interaction is non-negotiable for production systems. The specific tool matters less than the discipline of doing it comprehensively and reviewing the data regularly.
What Is Missing From Most Stacks
The biggest gap we see is not a missing tool. It is missing process. Teams that ship reliable AI have: automated evaluation in CI/CD, human review workflows for edge cases, cost monitoring with alerts and budgets, and a defined process for model updates and rollbacks. Tools enable this. But no tool replaces the discipline of building and maintaining it.