We published our first enterprise AI stack overview in 2024. A lot has changed since then. Models are faster and cheaper. Compound AI patterns are mainstream. Orchestration tooling has matured. Agent frameworks are emerging but unproven. Here's our updated view of what the enterprise AI technology stack looks like in early 2026.
What You Need to Know
- The stack has stabilised around clear layers. The experimental chaos of 2023-2024 has given way to recognisable architectural patterns. This is good news for enterprises: the choices are clearer.
- Model costs have dropped 80-90% since 2024 for equivalent capability. This changes the economics of every AI application, making previously uneconomical use cases viable.
- Compound AI and multi-model are now default patterns, not advanced techniques. Orchestration tooling supports them out of the box.
- The biggest shift is operational. The stack now includes monitoring, evaluation, and governance as first-class concerns, not afterthoughts. This reflects the maturity of the market.
85-90%
reduction in inference costs for comparable capability since early 2024
Source: Compiled from Anthropic, OpenAI, and Google pricing data, 2024-2026
The Stack, Layer by Layer
Layer 1: Foundation Models
What's changed: The frontier has expanded. Claude 3.5 and its successors, GPT-4o and beyond, Gemini, and Llama 3+ all deliver enterprise-grade quality. The performance gap between providers has narrowed. No single model dominates all tasks.
What hasn't changed: Model choice still matters less than most enterprises think. The orchestration and application layers determine more of the outcome than the model itself.
What's new: Smaller, specialised models have become genuinely competitive for focused tasks. A fine-tuned 7B parameter model for document classification can match frontier models at 1/50th the cost. The economic case for multi-model has become overwhelming.
Our take: Use frontier models for complex reasoning and customer-facing applications. Use mid-tier models for extraction and summarisation. Use small/fine-tuned models for classification, routing, and high-volume tasks. The cost savings from right-sizing models across your workload are substantial.
Layer 2: Orchestration and Compound AI
What's changed: This layer barely existed as a product category in 2024. Now there are mature frameworks and platforms for building compound AI systems: task routing, model selection, tool integration, and multi-step workflow management.
Key patterns:
- Chain-of-thought orchestration: Breaking complex tasks into reasoning steps, each handled by the appropriate model or tool
- Tool use: Models invoking external tools (databases, calculators, APIs) as part of their reasoning
- Retrieval orchestration: Multi-step retrieval patterns (query decomposition, re-ranking, contextual retrieval) managed by the orchestration layer
- Guardrails and validation: Output checking, policy enforcement, and quality gates embedded in the orchestration pipeline
Our take: The orchestration layer is the most valuable piece of enterprise AI infrastructure. It's where task routing, cost optimisation, quality control, and model flexibility all live. Build or adopt a solid orchestration layer before investing in anything else.
Layer 3: Data and Knowledge
What's changed: Vector databases are commoditised. The differentiation has moved to data pipeline quality: how well you extract, chunk, embed, and index enterprise data. Knowledge graph integration is emerging as a complement to vector search, providing structured relationships that pure similarity search misses.
Key components:
- Document processing pipelines: Handling the messy reality of enterprise documents (scanned PDFs, handwritten notes, multi-format attachments)
- Vector databases: pgvector, Pinecone, Weaviate, and others. The choice matters less than the implementation quality.
- Hybrid search: Combining semantic (vector) search with keyword (BM25) search. Now standard practice for enterprise RAG.
- Knowledge graphs: Structured representations of entity relationships that complement vector search for complex queries.
Our take: Invest in data pipeline quality over database choice. A well-processed document in any vector database outperforms a poorly processed document in the "best" vector database.
Layer 4: Application and Interface
What's changed: AI-specific UI patterns have matured. Confidence indicators, source attribution, streaming responses, and human-in-the-loop review flows are now well-understood patterns with established component libraries.
The shift: Away from standalone AI applications (chatbots, analysis tools) toward embedded AI within existing enterprise workflows. AI as a capability layer in the systems people already use, not a separate destination.
Our take: The best AI interfaces are invisible. Users don't navigate to "the AI tool." They use their existing tools, which are now AI-enhanced. Embedded beats standalone for adoption every time.
Layer 5: Operations and Governance
What's changed: This is the biggest shift since 2024. Operations and governance have moved from afterthoughts to essential stack components. Monitoring, evaluation, audit, and compliance tooling is now expected, not optional.
Key components:
- Performance monitoring: Real-time tracking of accuracy, latency, cost, and throughput across all AI capabilities
- Evaluation frameworks: Automated and human-in-the-loop evaluation against domain-specific benchmarks
- Audit trails: End-to-end traceability from AI output to source data
- Cost management: Per-capability cost tracking and optimisation tools
Our take: Operations and governance separate production AI from experiments. If you're not investing in this layer, you're not running AI. You're running demos.
5
distinct layers in the mature enterprise AI stack, up from 3 recognisable layers in 2024
Source: RIVER Group, enterprise AI architecture analysis, 2026
What's Overhyped
Fully autonomous AI agents. Agent frameworks are interesting and improving, but autonomous agents in enterprise settings remain risky. The controllability, predictability, and auditability requirements of enterprise AI don't yet align well with fully autonomous behaviour. Compound AI with defined workflows outperforms autonomous agents for most enterprise tasks today.
Model fine-tuning for everyone. Fine-tuning has its place (specialised classification, domain-specific behaviour), but for most enterprise use cases, RAG with good prompt engineering delivers comparable results at lower cost and complexity. Fine-tune when you have clear evidence that RAG isn't sufficient, not as a default approach.
What's Underhyped
Data pipeline engineering. Still the unglamorous backbone that determines AI quality. Still underinvested relative to its impact.
Evaluation and testing. The discipline of systematically evaluating AI performance against domain-specific benchmarks. Most enterprises still rely on spot-checking rather than systematic evaluation.
Incremental improvement. The compound value of making each AI capability 5% better each quarter. Not a headline. But over two years, those increments transform the system.
The enterprise AI stack in 2026 looks more like a mature software architecture than the experimental patchwork of 2024. The exciting phase was fun; the mature phase is productive.
Mak Khan
Chief AI Officer
The cost reduction in inference is the headline, but the real story is the maturation of the operational layer. That's a sign of a maturing market.
John Li
Chief Technology Officer