{
  "id": "nutanix",
  "name": "Nutanix Cloud Platform with Enterprise AI",
  "subtitle": "Mapped to the 4+1 Layer AI Infrastructure Model",
  "version": "v1.1 - Interface-Portability Reconciliation",
  "date": "June 20, 2026",
  "source": ".NEXT 2026 (Apr 2026), .NEXT 2025, NAI 2.7 (May 2026), AOS 7.5 / AHV 11 / Prism Central pc.7.5, NUS 5.3, NDB v2.10, NKP 2.17, Nutanix Bible, NVIDIA vGPU product-support matrix for AHV, published 4+1 model. v1.1 (instrument reconciliation): 1A NUS Objects Retained→Delegated, resolving the internal split with NKP (2A) — proprietary implementation behind a multi-vendor standard is Delegated, not Retained.",
  "status": "complete",
  "summary": {
    "title": "Summary Finding",
    "paragraphs": [
      "Nutanix occupies the same structural position as VMware — an HCI / private-cloud platform, an abstraction layer running on commodity x86 it does not manufacture — and against the 4+1 model it produces a strikingly similar profile: strongest at infrastructure orchestration (Layer 2A), competent across the data and runtime layers, and gapped at data pipelines (Layer 1C) and the reasoning plane (Layer 2C). Its deepest IP and authority sit in the orchestration control plane: Prism, AOS, and AHV.",
      "Two divergences lift Nutanix above a simple VMware echo. It owns its data foundation — Nutanix Unified Storage (file, object, block) plus Data Lens compliance governance — where VMware is bring-your-own-storage; this raises Layer 1A above VMware's, though it remains compliance governance, not the AI-metadata catalog Dell's MetadataIQ provides. And Nutanix Enterprise AI (NAI) is a genuinely good, less-NVIDIA-locked model-serving product: multi-source models (NVIDIA NIM, Hugging Face, or custom upload), a vLLM default engine, a CPU-only inference path, and portability across any CNCF-certified Kubernetes — arguably a cleaner standalone serving product than VMware's Model Runtime.",
      "The capture mechanism here is decoupled and invisible — the reassuring kind. The edges are open: commodity x86 hardware is Retained (swap Dell for HPE or Lenovo without rebuilding), NKP is unforked upstream Kubernetes (manifests lift to any conformant cluster), NAI runs on any cloud's Kubernetes and is not NIM-locked, and the models are open. The buyer feels unconstrained. Yet the value that actually accumulates — AOS/Prism orchestration opinions, Acropolis Dynamic Scheduling placement logic, NAI's serving and governance configuration, Data Lens policy — is captive to Nutanix. Eight of eighteen scored components are Ceded, concentrated exactly where operational opinions accumulate. Open hardware and open models do not make the control plane portable.",
      "Where the stack thins is identical to VMware. Layer 1C (data pipelines) is a gap: Time Machine and cross-cloud snapshots are storage mobility, not AI pipelines — there is no lineage and no cost-aware movement. Layer 2C (the reasoning plane) is a gap: Agent Gateway is a real, generally available agentic-governance control point (RBAC, rate-limiting, audit including MCP-call audit, health-based failover, capacity load-balancing), but governance and operational routing are not placement reasoning. There is no per-request, content/cost/quality model selection, and the data-side feeders a reasoning plane would query — an AI-metadata catalog at Layer 1A, lineage at Layer 1C — are absent, so data-relative placement is structurally out of reach. The forward 'Nutanix Agentic AI' full stack is Early Access (GA targeted second half of 2026) and does not move the cell.",
      "One Layer 0 caveat bounds the on-platform AI story: AHV virtualizes only vGPU-class accelerators (L40S, H100, L4, and a single Blackwell SKU — the RTX PRO 6000 Server Edition), not HGX training systems (B200/GB200), which remain NVIDIA bare-metal. Nutanix's on-platform AI is inference and fine-tune scale, not frontier training. The June 2026 'NVIDIA Certification' headline is a storage certification — Nutanix Unified Storage feeding external GPU servers over Spectrum-X and GPUDirect — not GPUs running on Nutanix.",
      "The buyer's trade is the VMware trade with a Nutanix accent: the lowest-friction on-ramp to private AI for the Nutanix (and VMware-refugee) installed base — same console, same operational model, no new vendor, plus real owned storage and a serving plane that is not captive to NVIDIA — in exchange for ceding the orchestration and serving control plane to Nutanix, and accepting that data pipelines and the reasoning plane remain the enterprise's own responsibility. Nutanix runs the private-AI platform. It does not yet govern the agents on top of it."
    ]
  },
  "layers": [
    {
      "id": "layer0",
      "label": "Layer 0",
      "title": "Compute & Network Fabric",
      "purpose": "Raw compute, networking, and acceleration fabric",
      "status": "moderate",
      "statusLabel": "Hardware-Agnostic Abstraction",
      "nvidia": [
        {
          "component": "NVIDIA vGPU on AHV",
          "detail": "GA NVIDIA vGPU with live migration on AHV. Supported accelerators are vGPU-class: L40S, H100, L4, and a single Blackwell SKU — the RTX PRO 6000 Server Edition (via vGPU 19.0). The runtime and drivers are NVIDIA's; Nutanix integrates and schedules them."
        },
        {
          "component": "HGX Training GPUs Not Virtualizable on AHV",
          "detail": "B200/GB200/GH200 HGX systems are NOT virtualizable on AHV — NVIDIA bare-metal only. This caps Nutanix's on-platform AI at inference and fine-tune scale, not frontier training. The single Blackwell SKU on AHV is server-graphics class, not an HGX training GPU."
        },
        {
          "component": "NUS NVIDIA Certification (Storage Path)",
          "detail": "The June 2026 NVIDIA Certification is a STORAGE certification — Nutanix Unified Storage feeding EXTERNAL HGX/x86 GPU servers via Spectrum-X / Spectrum-4 / BlueField-3 and GPUDirect over RDMA. It is not GPUs running on Nutanix, and does not extend the AHV GPU support matrix."
        }
      ],
      "gap": "Like VMware, Nutanix is an abstraction layer, not physical infrastructure — it manufactures no silicon and no networking. The buyer runs AHV on the commodity x86 they already buy (NX appliances, or OEM platforms from Dell, HPE, Lenovo, Cisco UCS, Fujitsu, plus software-only on Supermicro/ProLiant/PowerEdge), with NC2 bare-metal on AWS/Azure. GPU passthrough and GA NVIDIA vGPU with live migration give them accelerated VMs under the Prism console they already operate.\n\nThe calibration is identical to VMware Layer 0 (moderate): both Retain hardware vendor choice and Cede the hypervisor and software-defined networking. Nutanix's AI ceiling is, however, lower than VMware's — VCF 9.1 virtualizes Blackwell HGX with NVSwitch and GPUDirect RDMA for distributed inference, whereas AHV tops out at vGPU-class accelerators. That keeps Nutanix firmly at moderate, well below Dell and Cisco (strong), which own or specify compute and networking hardware.",
      "borrowedJudgment": "Multi-directional, same shape as VMware. Nutanix borrows GPU silicon judgment from NVIDIA (same as everyone) and hardware engineering judgment from OEM partners (Dell, HPE, Lenovo, Cisco build the servers), but retains the abstraction layer — AOS, AHV, Prism, and Flow. The enterprise retains OEM choice (commodity x86), delegates the GPU runtime to NVIDIA, and cedes the AHV/AOS control plane and the Flow software-defined network.",
      "notes": "AHV is KVM/QEMU/libvirt/OVS-derived, hardened by Nutanix; the control plane (AOS, Prism) is proprietary. AHV MIG-backed vGPU could not be confirmed from open sources (login-gated compatibility matrix) — does not affect the cell. AMD Instinct support is an announced roadmap item (strategic partnership Feb 2026; first platform targeted late 2026) — not GA, logged as a watch-list item.",
      "components": [
        {
          "component": "AOS + AHV (Distributed Storage OS + Hypervisor)",
          "detail": "AHV (KVM-derived) plus the proprietary AOS distributed storage OS, managed in Prism. GPU passthrough and GA NVIDIA vGPU with live migration. AHV/AOS storage policies, VM configuration, and Prism opinions are captive — they cannot be lifted to ESXi or Hyper-V without rebuilding. Proprietary Nutanix platform — opinions captive, no open exit.",
          "dapm": "Ceded"
        },
        {
          "component": "Multi-Vendor Hardware Support",
          "detail": "Runs on NX appliances and OEM platforms (Dell, HPE, Lenovo, Cisco UCS, Fujitsu) plus software-only on Supermicro/ProLiant/PowerEdge. Commodity x86 substrate — the enterprise swaps OEM without rebuilding workloads. The same hardware-agnostic position VMware occupies.",
          "dapm": "Retained"
        },
        {
          "component": "NVIDIA GPU Integration (vGPU on AHV)",
          "detail": "GA NVIDIA vGPU with live migration and GPU passthrough; L40S/H100/L4/RTX PRO 6000 Server Edition. The GPU runtime is NVIDIA's behind a Nutanix-managed integration — substitutable in principle, delegated in practice.",
          "dapm": "Delegated"
        },
        {
          "component": "Flow Virtual Networking / Flow Network Security",
          "detail": "AHV software-defined networking: virtual routers and overlays (Flow Virtual Networking) plus distributed stateful microsegmentation (Flow Network Security, now with a Next-Gen policy model). Proprietary SDN — the network policy opinions do not port. Proprietary Nutanix platform — opinions captive, no open exit.",
          "dapm": "Ceded"
        }
      ]
    },
    {
      "id": "layer1a",
      "label": "Layer 1A",
      "title": "Data Storage & Governance",
      "purpose": "Durable, governed data foundation — the Governance Catalog that Layer 2C queries",
      "status": "moderate",
      "statusLabel": "Platform Storage + Compliance Governance, Not AI-Native",
      "nvidia": [
        {
          "component": "No Direct NVIDIA Layer 1A Governance Dependency",
          "detail": "NVIDIA provides nothing in the governance layer. Its only touch is the NUS storage data path (Spectrum-X / GPUDirect over RDMA) feeding external GPU servers — data-plane performance plumbing, not a Layer 1A governance catalog."
        }
      ],
      "gap": "Unlike VMware (bring-your-own-storage), Nutanix actually owns the data foundation. Nutanix Unified Storage (NUS 5.3) provides file (SMB/NFS), S3-compatible object, and block (iSCSI, NVMe-oF/TCP) storage on AOS, all managed in Prism, and Data Lens adds genuine compliance governance — access auditing, GDPR/HIPAA/SOX reporting, permission and risk analytics, ransomware detect-and-block — that VMware lacks at the platform layer.\n\nBut this is governance-as-audit, not AI-native governance. Data Lens classifies on metadata, permissions, and access patterns; there is no AI/ML semantic content classification, no AI metadata enrichment, and no data lineage of the kind Dell's MetadataIQ or HPE's Data Fabric provide. The 4+1 model defines Layer 1A as the governance catalog Layer 2C queries; Nutanix has a storage platform with compliance governance, not a queryable AI-metadata catalog. The result is stronger than VMware's vSAN-plus-external-arrays story but short of Dell's AI-specific metadata depth — moderate, at the top of the band.",
      "borrowedJudgment": "Low. NUS and Data Lens are Nutanix IP — the storage platform and its governance are owned, not borrowed. The limitation is not authority but kind: the governance is compliance and audit, not the AI-metadata catalog a reasoning plane would query. NVIDIA contributes only the RDMA data path, not governance logic.",
      "notes": "S3-over-RDMA is a roadmap item (later 2026); NetApp ONTAP integration is targeted second half of 2026 — both logged as watch-list, neither scored. The NUS object interface is split out as Delegated because S3 object opinions lift to any S3 platform — a proprietary implementation behind a multi-vendor standard — mirroring the Dell ObjectScale treatment.",
      "components": [
        {
          "component": "NUS — Files & Volumes",
          "detail": "Unified file (SMB/NFS) and block (iSCSI, NVMe-oF/TCP) storage on AOS, managed in Prism. File and block platform opinions — tiering, performance policy, AOS integration — are captive and cannot be lifted to PowerScale or VAST without rebuilding. Proprietary Nutanix platform — opinions captive, no open exit.",
          "dapm": "Ceded"
        },
        {
          "component": "NUS — Objects (S3 interface)",
          "detail": "S3-compatible object store on AOS. Bucket, lifecycle, and policy opinions lift to any S3 platform without rebuilding — the consumed interface is a genuine multi-vendor standard. NUS Objects is a proprietary implementation behind that standard, so the object opinions are portable and the call is Delegated (Retained is reserved for an open substrate the enterprise operates, e.g. Ceph); the same basis as NKP at Layer 2A.",
          "dapm": "Delegated"
        },
        {
          "component": "Data Lens (Governance)",
          "detail": "Compliance and audit governance: access auditing, GDPR/HIPAA/SOX reporting, permission/risk analytics, ransomware detect-and-block. Data Lens 2.0 adds on-prem/air-gapped operation. Governs on metadata and access patterns, not AI/ML semantic content. Proprietary Nutanix platform — opinions captive, no open exit.",
          "dapm": "Ceded"
        }
      ]
    },
    {
      "id": "layer1b",
      "label": "Layer 1B",
      "title": "Context Management & Retrieval",
      "purpose": "Low-latency retrieval for RAG — vector/hybrid search, context windows",
      "status": "moderate",
      "statusLabel": "Foundational RAG via Managed Postgres",
      "nvidia": [
        {
          "component": "NVIDIA AI Enterprise + NeMo Retriever (Accelerated Path)",
          "detail": "The accelerated-retrieval path layers NVIDIA AI Enterprise and NeMo Retriever over NUS. The same non-differentiating stack is available on Dell, HPE, and VMware — NVIDIA is the accelerator, not the retrieval owner."
        }
      ],
      "gap": "A Nutanix buyer who wants RAG has one genuinely shipping, owned path: NDB-managed PostgreSQL with pgvector (GA since NDB v2.7, Jan 2025; current v2.10), which Nutanix markets explicitly as turning Postgres into a vector database for RAG, with lifecycle automation (provision, patch, clone, HA). For moderate-scale enterprise RAG on data already kept in Postgres, that is a real, low-friction capability.\n\nThe retrieval intelligence, however, is not Nutanix's. pgvector is OSS; the heavier AI-native retrieval path Nutanix points customers to is third-party Milvus plus NVIDIA AI Enterprise layered on NUS. Critically, NUS itself has no native vector database, no vector search, and no RAG retrieval in the storage layer — the AI-ready-data messaging is data-plane plumbing, not retrieval. There is no retrieval-quality observability (recall@k, latency percentiles) a Layer 2C could consume — the same universal gap noted in Dell and VMware. This calibrates almost exactly to VMware Layer 1B (moderate): VMware's owned vector path is also pgvector-on-Postgres, the same pragmatic, performance-ceilinged choice, short of Dell (Elasticsearch hybrid search + MetadataIQ) and VAST (native InsightEngine).",
      "borrowedJudgment": "Moderate. The retrieval opinions live in stock OSS PostgreSQL + pgvector; NDB provides lifecycle automation around a portable open core. A pg_dump/restore moves the vector schema, embeddings, index definitions, and queries to any other Postgres — RDS, Azure, self-hosted — so the retrieval layer lifts out. Milvus is likewise OSS and swappable. Nutanix's contribution is operation, not retrieval intelligence.",
      "notes": "pgvector index types (HNSW/IVFFlat) under NDB management are not confirmed from open sources — does not affect the cell. The KServe serving substrate is industry-attested but not officially named by Nutanix (vLLM is the documented engine).",
      "components": [
        {
          "component": "Vector Search via NDB-Managed PostgreSQL (pgvector)",
          "detail": "GA. NDB v2.10 automates the lifecycle of PostgreSQL with the pgvector extension, framed by Nutanix as a vector DB for RAG. pgvector is OSS with real alternatives; the consumed interface is the standard Postgres wire protocol, so retrieval opinions lift to any Postgres platform without rebuilding. NDB operates the database — operation delegated, the open substrate keeps the opinions portable.",
          "dapm": "Delegated"
        },
        {
          "component": "Milvus / NVIDIA AIDP on NUS (Layered Retrieval)",
          "detail": "The AI-native retrieval path layers third-party Milvus (OSS) and NVIDIA AI Enterprise on top of NUS. Nutanix provides the substrate, not the retrieval engine; Milvus is swappable.",
          "dapm": "Delegated"
        }
      ]
    },
    {
      "id": "layer1c",
      "label": "Layer 1C",
      "title": "Data Movement & Pipelines",
      "purpose": "Move/transform data — ETL/ELT, lineage, cost-aware movement, KV cache tiering",
      "status": "gap",
      "statusLabel": "Gap",
      "nvidia": [
        {
          "component": "No NVIDIA Layer 1C Dependency",
          "detail": "NVIDIA provides nothing at Nutanix Layer 1C. There is no GA CMX/KV-cache tiering on AHV; kvCache-aware routing in NAI is Tech Preview and is request-scheduling within an endpoint, not data-pipeline tiering. A near-empty NVIDIA column here is itself the finding — same as VMware Layer 1C."
        }
      ],
      "gap": "Within the Nutanix world, data mobility is genuinely good — but it is storage mobility, not AI pipelines. NDB Time Machine provides copy-data management (point-in-time recovery, thin clones); snapshots replicate across clusters and clouds (NC2); native DB replication (Oracle Data Guard, Postgres pglogical) moves data between instances.\n\nNone of that is a Layer 1C AI data pipeline. There is no dedicated ETL/ELT product, no CDC pipeline, no feature engineering, no lineage, no cost-aware movement, and no KV-cache tiering. The closest thing to data movement is OSS pglogical, which is not Nutanix IP. The enterprise running Nutanix for AI must bring its own pipeline orchestration (Airflow, Kubeflow, Spark, or a commercial alternative) and run it on NKP. This is the same call as VMware Layer 1C (gap): both lack an equivalent to Dell's Dataloop-powered orchestration, HPE's Ezmeral, or VAST's DataEngine.\n\nThe gap has a downstream cost. A reasoning plane (Layer 2C) makes data-relative placement decisions — run compute where the data lives, weigh moving data versus moving compute — and to do so it needs the lineage and cost-aware-movement primitives this layer would provide. Their absence is one of the structural reasons a Nutanix Layer 2C cannot do data-relative placement.",
      "borrowedJudgment": "The enterprise retains responsibility for the function by default — no vendor has claimed it. Any data-pipeline judgment is borrowed from whatever tooling the enterprise deploys on NKP (Airflow, Kubeflow, commercial), not from Nutanix. Storage and DB mobility (Time Machine, snapshot replication, pglogical) exist but are adjacent to, not the same as, the AI-pipeline function this layer scores.",
      "notes": "Watch-list (dated, none scored): NetApp ONTAP integration targeted 2H 2026; S3-over-RDMA roadmap (later 2026); kvCache-aware routing in NAI 2.7 is Tech Preview (and is request-scheduling within an endpoint, not data-pipeline tiering). Time Machine and cross-cloud snapshots are described here as storage/DB mobility, not promoted to scored components — they do not clear the AI-pipeline bar.",
      "components": []
    },
    {
      "id": "layer2a",
      "label": "Layer 2A",
      "title": "Infrastructure Orchestration",
      "purpose": "GPU scheduling, quotas, RBAC, fair-share scheduling, utilization optimization",
      "status": "strong",
      "statusLabel": "Nutanix Heritage Strength",
      "nvidia": [
        {
          "component": "NVIDIA GPU Operator",
          "detail": "GPU lifecycle and scheduling on NKP via the NVIDIA GPU Operator (partner OSS) in the Kommander catalog. There is no Nutanix-owned GPU scheduler and no Run:ai equivalent — the GPU-aware layer is borrowed, the same dependency every on-prem vendor shares."
        },
        {
          "component": "NVIDIA vGPU Manager",
          "detail": "vGPU profiles and allocation on AHV. Multi-tenant GPU partitioning is managed through Prism, but the virtualization layer itself is NVIDIA's."
        }
      ],
      "gap": "Layer 2A is Nutanix's home turf and its deepest IP. Prism Central provides mature, multi-cluster fleet management with RBAC, quotas, and self-service; AOS with Acropolis Dynamic Scheduling (ADS) automates VM placement and rebalancing; and NKP 2.17 delivers Kubernetes. One operational model — the console the buyer already runs — now extended to AI workloads. For a VMware-refugee shop (Nutanix's core go-to-market), this is the lowest-friction orchestration on-ramp to AI available.\n\nThe calibration matches VMware Layer 2A (strong): both are HCI/private-cloud platforms whose deepest competency is unified infrastructure orchestration, and both carry the same NVIDIA GPU-scheduling dependency, which the layer treats as universal. Two honest qualifiers keep Nutanix a hair behind VMware: VMware's 2A remains the peak of the series on raw operational depth (100M+ cores, two decades, a single vSphere Supervisor managing VMs, containers, and AI from one plane), and Nutanix's unification is marginally less seamless — VMs via Prism/AHV and Kubernetes via a distinct NKP product rather than one supervisor. Still well ahead of Dell and Cisco 2A (moderate), where GPU orchestration is fully NVIDIA-owned.\n\nGPU-aware scheduling is the borrowed piece: NKP schedules GPUs via the NVIDIA GPU Operator, and there is no Nutanix-owned GPU scheduler. Policy-driven GPU scheduling (which workload gets which GPU on cost or compliance grounds) is NVIDIA's, not the platform's — the identical gap Dell and VMware carry.",
      "borrowedJudgment": "Low — the lowest of any Nutanix layer. Prism, AOS, and ADS are Nutanix IP. The primary borrowed piece is GPU-aware scheduling (NVIDIA GPU Operator), the same dependency every on-prem peer shares. NKP's consumed interface — unforked upstream Kubernetes — keeps container workloads portable to any conformant cluster.",
      "notes": "NKP is explicitly pure upstream CNCF Kubernetes with no proprietary API wrapper (D2iQ lineage: Konvoy/Kommander/NKP Insights are Nutanix IP; the runtime is unforked). NKP Metal (bare-metal Kubernetes) is Early Access with GA targeted 2H 2026 — logged as watch-list.",
      "components": [
        {
          "component": "Prism Central (Unified Fleet Management)",
          "detail": "Multi-cluster management, RBAC, quotas, self-service, and full-stack lifecycle across the Nutanix estate. The operational backbone — comparable to VMware's SDDC Manager and VCF Operations. Prism policies and management opinions do not port. Proprietary Nutanix platform — opinions captive, no open exit.",
          "dapm": "Ceded"
        },
        {
          "component": "AOS + Acropolis Dynamic Scheduling (ADS)",
          "detail": "Automated VM placement, load-balancing, and resource management across the cluster. The placement opinions are captive to AOS — a proprietary scheduler. Proprietary Nutanix platform — opinions captive, no open exit.",
          "dapm": "Ceded"
        },
        {
          "component": "Nutanix Kubernetes Platform (NKP)",
          "detail": "Turnkey Kubernetes built on pure upstream, CNCF-conformant Kubernetes (unforked, no proprietary API wrapper) via Cluster API. The consumed interface is the standard Kubernetes API, so manifests and workloads lift to EKS, AKS, or any conformant cluster without rebuilding. The Konvoy/Kommander management layer is Nutanix's; the consumed interface keeps the workloads portable.",
          "dapm": "Delegated"
        }
      ]
    },
    {
      "id": "layer2b",
      "label": "Layer 2B",
      "title": "Application Runtime & Execution",
      "purpose": "Model serving, agent execution, inference APIs, distributed inference",
      "status": "moderate",
      "statusLabel": "Platform-Native Serving, Agents Pre-GA",
      "nvidia": [
        {
          "component": "NVIDIA NIM (One Serving Option, Not a Lock)",
          "detail": "NIM is a model-serving option inside NAI — alongside Hugging Face and custom upload — not a requirement. NeMo/Nemotron are enabled and TensorRT-LLM is available via NIM. The finding is the de-emphasis: NAI is explicitly not NIM-locked."
        },
        {
          "component": "Lighter NVIDIA Dependency Than Peers",
          "detail": "A vLLM OSS default engine plus an Intel-AMX CPU-only inference path mean Nutanix's runtime is less NVIDIA-captive than Dell's all-NVIDIA NemoClaw/OpenShell 2B path. A genuine, scoreable differentiator."
        }
      ],
      "gap": "Nutanix Enterprise AI (NAI 2.7, GA May 2026) is Nutanix's strongest owned AI asset. It deploys LLMs as secure, RBAC-governed inference endpoints from NVIDIA NIM, Hugging Face, or a custom upload; offers a 74-model validated catalog, batch inference, a CPU-only inference path (Intel AMX), and vLLM as the default engine; and runs on any CNCF Kubernetes (EKS/AKS/GKE/bare-metal), not just Nutanix. As a standalone model-serving product it is arguably cleaner and less NVIDIA-locked than VMware's Model Runtime.\n\nTwo concerns. The inference runtime is borrowed — vLLM (OSS) and TensorRT-LLM via NIM (NVIDIA); NAI is the control and governance plane on top, and that plane's opinions are captive to NAI. And the agent-execution half of this layer is not GA: Layer 2B is model serving, agent execution, inference APIs, and distributed inference, but the native agent framework (NAI Labs – Agent), MCP servers, RAG pipeline, LoRA fine-tuning, and kvCache-aware routing are all Tech Preview in NAI 2.7. Per the GA-gate, none of those count toward the score.\n\nThe result lands at VMware Layer 2B (moderate), with a different composition: VMware's Model Runtime and Agent Builder are both GA, whereas Nutanix's serving is GA and arguably more open, but its agent execution is preview where VMware's is GA. Nutanix trades VMware's GA agent-builder for a more open, less NVIDIA-captive serving runtime. Both sit well above Dell 2B (entirely NVIDIA NemoClaw/OpenShell-dependent).",
      "borrowedJudgment": "Moderate. Nutanix owns the NAI serving and governance plane (its authority, captive to Nutanix), while the inference runtime is borrowed from vLLM (OSS) and NVIDIA (NIM) — the same structural runtime dependency Dell, HPE, and VMware carry, softened here by the OSS default engine and the CPU-only path. Agent execution is not yet GA, so there is no agent-runtime judgment to score.",
      "notes": "GA-gate watch-list (dated, none scored): native agent framework / NAI Labs – Agent (Tech Preview), MCP servers (TP), RAG pipeline (TP), LoRA fine-tuning (TP), kvCache-aware routing (TP) — all in NAI 2.7; and the 'Nutanix Agentic AI' full stack (Early Access, GA targeted 2H 2026). Palo Alto Prisma AIRS model scanning is GA in NAI 2.7 (scored at Layer 3).",
      "components": [
        {
          "component": "Nutanix Enterprise AI (NAI) — Model Serving & Governance",
          "detail": "GA. RBAC-governed inference endpoints; 74-model validated catalog; multi-source models (NVIDIA NIM, Hugging Face, custom upload); CPU-only inference via Intel AMX; batch inference; vLLM default engine. Runs on any CNCF Kubernetes. NAI is proprietary Nutanix software — the serving and governance opinions (catalog, endpoint configuration, RBAC policy) do not lift to another serving stack without rebuilding, and running it on EKS does not change that. Proprietary Nutanix platform — opinions captive, no open exit.",
          "dapm": "Ceded"
        },
        {
          "component": "Inference Runtime (vLLM / NVIDIA NIM / TensorRT-LLM)",
          "detail": "The model execution engines NAI orchestrates. vLLM is the documented OSS default; NIM (with TensorRT-LLM) is an optional path, not a lock. The consumed runtime is substitutable — applications call OpenAI-compatible endpoints — and is neither Nutanix-owned nor NVIDIA-mandatory.",
          "dapm": "Delegated"
        }
      ]
    },
    {
      "id": "layer2c",
      "label": "Layer 2C",
      "title": "Agentic Infrastructure — The Reasoning Plane",
      "purpose": "Policy-driven placement and resource coordination — the Autonomy Layer",
      "status": "gap",
      "statusLabel": "Emerging Signals — Governance Without Placement",
      "nvidia": [
        {
          "component": "No NVIDIA Layer 2C Dependency",
          "detail": "Agent Gateway is Nutanix IP. NVIDIA provides no agent governance, model routing, or placement reasoning in the Nutanix stack — the same pattern as Google's and Cisco's 2C, where the governance layer is vendor-owned, not NVIDIA-dependent."
        }
      ],
      "gap": "With Agent Gateway (GA in NAI 2.7), the buyer gets a single, governed control point for agentic traffic: a unified API in front of both cloud and self-hosted models, RBAC, per-token rate-limiting, full audit including MCP-call audit, plus health-based endpoint failover and capacity load-balancing. For an enterprise worried about ungoverned agent and model sprawl, that is a real, shipping governance surface.\n\nApplying the 'Routing Is Not Reasoning' test: Agent Gateway governance (RBAC, rate-limiting, audit, MCP audit) is access control — genuine intelligence-layer governance, but not placement. Health-based failover and capacity load-balancing are single-variable operational routing (is the endpoint up, is it at capacity?), not multi-variable policy-driven placement. Per-request model selection on content, cost, or quality does not exist; kvCache-aware routing is Tech Preview, and even that schedules to GPU workers within a single endpoint, not among models. None of it is the reasoning plane Layer 2C defines.\n\nThe gap is structural, not merely unbuilt. A reasoning plane makes data-relative placement decisions, and the data-side feeders it would query — an AI-metadata catalog at Layer 1A, lineage and cost-aware movement at Layer 1C — are absent. So even when the forward 'Nutanix Agentic AI' full stack reaches GA (Early Access today, targeted 2H 2026), its placement reasoning will be starved of data-side inputs. This calibrates to VMware Layer 2C (gap) — the closest decision-path peer — which likewise parks its agentic-governance signal short of placement reasoning. Nutanix has building blocks (governance, operational routing); it does not have a reasoning plane.",
      "borrowedJudgment": "Inverted: there is no Layer 2C to borrow. Agent Gateway is Nutanix IP with no NVIDIA dependency, so the governance and operational-routing functions Nutanix does provide carry low borrowed judgment — but the placement, model-routing, and reasoning functions are Absent, not borrowed. The enterprise must build custom 2C logic, bring a partner, or operate without it.",
      "notes": "Watch-list (dated, none scored): kvCache-aware routing (Tech Preview); the 'Nutanix Agentic AI' full stack (Early Access, GA targeted 2H 2026); native agent framework and MCP servers (Tech Preview). Agent Gateway is described here as a building block rather than scored — consistent with the gap-layer convention and with how the closest peer (VMware) treats its equivalent agentic-governance signal.",
      "components": []
    },
    {
      "id": "layer3",
      "label": "Layer 3 (+1)",
      "title": "AI Application Layer — The Value Plane",
      "purpose": "AI-powered business capabilities — business logic, workflow automation",
      "status": "moderate",
      "statusLabel": "Platform-Enabled, Not Platform-Provided",
      "nvidia": [
        {
          "component": "NVIDIA Model Ecosystem (Nemotron + NIM)",
          "detail": "Nemotron models and NIM containers are available through the NAI catalog. NVIDIA provides a slice of the model layer; Nutanix provides serving and governance. The same non-differentiating pattern as VMware Layer 3."
        }
      ],
      "gap": "Nutanix does not sell AI applications — it sells the platform to build and run them, plus a curated on-ramp to models. The buyer gets Hugging Face integration (deploy validated Hub models with their own token, GA), a 74-model validated catalog (Meta Llama, Google Gemma, NVIDIA Nemotron), an ISV AI Partner Program (DataRobot as flagship, plus Codeium, Instabase, Pryon, Lamini, UbiOps), and Palo Alto Prisma AIRS model-security scanning (GA in NAI 2.7). Applications are built by the enterprise's own teams on NAI, or sourced from partners.\n\nThis is the architecturally correct position for a platform vendor — the same one Dell and VMware occupy — so the concern is ecosystem depth, not capture. NAI is the platform-native enabler (Nutanix IP), but the application logic and models are partner, OSS, or enterprise-built. The ISV AI Partner Program is emerging and when-and-if-available — not at the curation depth of Dell's load-bearing ecosystem (OpenAI, Palantir, ServiceNow, 5,000+ deployments) or HPE's Unleash AI (26+ validated ISVs), and several models (Mistral, DeepSeek) are deployable but not officially validated.\n\nThe calibration is VMware Layer 3 (moderate): both provide platform-native AI services (VMware's Private AI Services, Nutanix's NAI) that enable Layer 3, with an emerging AI-specific ISV ecosystem. That platform-native tooling is exactly what distinguishes moderate (VMware, Nutanix) from partner (Dell, Cisco — pure ISV ecosystem with no platform AI services of their own).",
      "borrowedJudgment": "Distributed across partners and the enterprise's own development teams — the correct Layer 3 shape. NAI (the enabler) is Nutanix's; the applications and models are partner, OSS, or enterprise-built. The enterprise borrows application judgment from its chosen ISVs (DataRobot and the AI Partner Program) and model judgment from the open and partner catalog (Hugging Face, Llama, Gemma, Nemotron).",
      "notes": "Hugging Face is an engineering-collaboration partner (since May 2024); models deploy with the customer's HF token. Mistral and DeepSeek are deployable but carry Nutanix's own not-officially-validated disclaimer. Prisma AIRS (Palo Alto) model scanning reached GA in NAI 2.7.",
      "components": [
        {
          "component": "Nutanix Enterprise AI (Platform Enablement)",
          "detail": "The integrated model-serving and governance platform enterprises build AI applications on — the tools to BUILD applications, not the applications themselves. Mirrors VMware's Private AI Services as a Layer 3 enabler. Proprietary Nutanix platform — opinions captive, no open exit.",
          "dapm": "Ceded"
        },
        {
          "component": "ISV AI Partner Program",
          "detail": "DataRobot (flagship), Codeium, Instabase, Pryon, Lamini, UbiOps and others provide application logic, when-and-if-available. Substitutable partners — the enterprise can change ISVs without rebuilding the platform beneath them.",
          "dapm": "Delegated"
        },
        {
          "component": "Hugging Face + Validated Model Catalog",
          "detail": "74 validated models (Meta Llama, Google Gemma, NVIDIA Nemotron) deployed with the customer's Hugging Face token. Open and partner models, swappable — mirrors Dell's Enterprise Hub (Hugging Face) treatment.",
          "dapm": "Delegated"
        },
        {
          "component": "Palo Alto Prisma AIRS (Model Security Scanning)",
          "detail": "GA in NAI 2.7. Partner-provided model and prompt scanning integrated into the serving path. A substitutable partner security capability.",
          "dapm": "Delegated"
        }
      ]
    }
  ]
}