{
  "id": "qlik",
  "name": "Qlik (Cloud Analytics + Talend Cloud + Open Lakehouse)",
  "subtitle": "Mapped to the 4+1 Layer AI Infrastructure Model",
  "version": "v1.0 - Initial Assessment",
  "date": "July 7, 2026",
  "source": "help.qlik.com (Open Lakehouse architecture, Qlik MCP server, Qlik Predict, client-managed Qlik Sense), Qlik press releases (Open Lakehouse GA Sept 2025; agentic analytics GA + MCP Server Feb 2026; Qlik Connect 2026; agentic data engineering GA June 2026), Qlik Community FAQ and support articles, published 4+1 model",
  "status": "complete",
  "summary": {
    "title": "Summary Finding",
    "paragraphs": [
      "Qlik is a data integration and analytics software vendor that floats above Layer 0 (SaaS on AWS, and AWS-only) and is strongest at the two ends it has always owned: data movement (1C — the Attunity/Talend/Upsolver heartland) and the analytics value plane (Layer 3 — the application estate plus a GA detect-predict-act loop). The middle of the stack is uniformly moderate — storage/governance, retrieval, orchestration, and runtime are all real but product-bounded — and the reasoning plane is a gap by explicit posture. Authority sits in Qlik's opinion layer wherever Qlik ships one; the substrate never.",
      "This is the cleanest zero-NVIDIA row in the instrument: no GPU plane, no accelerator dependency, no NVIDIA software surface at any layer. Qlik's entire generative chain is instead double-stacked borrowed judgment — Qlik chooses the model, AWS serves it, Anthropic reasons — with no customer model choice anywhere. The enterprise that adopts Qlik's AI inherits two vendors' judgment above it and holds authority over neither.",
      "Two capture generations coexist in one platform, and the row's shape inverts Databricks': Qlik's newest surface is its most open and its oldest is its most captive. Open Lakehouse is the most Retained-friendly lakehouse posture in the series — Iceberg tables in the customer's own S3, cataloged in the customer's own AWS Glue, valid and readable if Qlik vanishes tomorrow. The legacy estate is classic coupled capture — QVD libraries and set-analysis logic that lift to nowhere. And the new decoupled ask sits between them: the Trust Score and data-product layer is Qlik asking the enterprise to cede trust itself to the platform, an ask made to feel free precisely because the bytes underneath stay visibly yours.",
      "Qlik's reasoning-plane answer is MCP-first: be a well-governed tool inside somebody else's agentic infrastructure rather than ship a thin gateway and call it a control plane. That scores as a capability gap and reads as architectural honesty — the 'bring any Layer 2C' invitation, made one layer up from where Dell makes it, with the same consequence: the reasoning plane stays the enterprise's to build, and the authority stays Retained by default.",
      "The DAPM profile makes the trade exact: of 20 scored components, 19 are Ceded and one is Delegated (the lakehouse storage interface — the single genuinely portable thing Qlik touches). The buyer gets best-in-class heterogeneous data movement and a complete, GA insight-to-action loop for the analytics domain, in exchange for ceding every opinion layer Qlik ships — pipelines, trust, retrieval, models, agents, apps — while retaining the most portable data substrate in the series and full default ownership of the reasoning plane Qlik declined to claim."
    ]
  },
  "layers": [
    {
      "id": "layer0",
      "label": "Layer 0",
      "shortName": "Compute",
      "title": "Compute & Network Fabric",
      "purpose": "Raw compute, networking, and acceleration fabric",
      "status": "gap",
      "statusLabel": "Not Qlik's Layer (By Design)",
      "nvidia": [
        {
          "component": "No NVIDIA Surface Anywhere in the Stack",
          "detail": "Effectively zero NVIDIA dependency across the entire Qlik estate — no GPU story at all. The associative engine is CPU-native, Qlik Predict is classical AutoML, and generative inference is mediated by Amazon Bedrock. The emptiest NVIDIA column in the series: Qlik's borrowed AI judgment flows to AWS and Anthropic, not NVIDIA."
        }
      ],
      "gap": "Layer 0 is not Qlik's layer, by design. The buyer never thinks about it, and that is the pitch: Qlik Cloud is SaaS on AWS. The one place Qlik touches infrastructure is Open Lakehouse, where compute runs on EC2 Spot instances inside the customer's own AWS VPC — the customer pays the EC2 bill and keeps data sovereignty while Qlik provisions and manages the cluster. Client-managed Qlik Sense still exists for regulated shops (May 2026 release shipping, EOS 2028), running on the customer's own Windows servers.\n\nThe consequence for the 4+1 model is the same as Databricks' and Palantir's: a Qlik adoption decision resolves no Layer 0 authority question — whatever capture exists at silicon and fabric belongs to AWS, a different row on this map. The asymmetry worth naming: Qlik Cloud is effectively AWS-only (Open Lakehouse requires EC2, S3, and Glue), so the Layer 0 question is not even multiple-choice. The VPC-resident lakehouse compute is a deployment topology, not a Layer 0 capability — the same reasoning that keeps Databricks' classic-clusters-in-your-VPC from moving its Layer 0 cell.",
      "borrowedJudgment": "Total at Layer 0, and irrelevant to the value proposition by design. Qlik inherits all silicon, networking, and acceleration judgment from AWS. The enterprise's Layer 0 authority position is set by AWS's row, and adopting Qlik does not, by itself, resolve it — it does, however, quietly commit the AI-compute question to AWS, because there is no non-AWS path.",
      "notes": "No documented non-AWS Qlik Cloud region was found (including the AWS European Sovereign Cloud collaboration), but the docs do not state AWS-only as policy — inference, flagged. Does not move the cell; it is a gap regardless.",
      "components": []
    },
    {
      "id": "layer1a",
      "label": "Layer 1A",
      "shortName": "Storage",
      "title": "Data Storage & Governance",
      "purpose": "Durable, governed data foundation — the Governance Catalog that Layer 2C queries",
      "status": "moderate",
      "statusLabel": "Open Lakehouse on Your Substrate; Captive Trust Layer",
      "nvidia": [
        {
          "component": "No NVIDIA Layer 1A Dependency",
          "detail": "Storage, catalog, quality, and trust scoring are Qlik IP or AWS services. NVIDIA contributes nothing to the governance layer."
        }
      ],
      "gap": "Two generations of storage story. The new one: Qlik Open Lakehouse (GA September 2025) writes Iceberg tables into the customer's own S3 buckets, cataloged in the customer's own AWS Glue catalog, in the customer's own AWS account. Adaptive Iceberg Optimization (the Upsolver engine) handles compaction, partitioning, and cleanup continuously; any engine — Snowflake, Athena, Trino, SageMaker — queries the same tables. Layered on top: data products with Trust Score (accuracy, timeliness, diversity, completeness), data quality rules and semantic types (the Talend heritage), end-to-end lineage, and, GA June 30, 2026, the Data Product Agent and Data Quality Agent.\n\nThe openness claim here is stronger than Databricks' — and mostly true. Databricks keeps data in your bucket but governs through its captive managed Unity Catalog; Qlik did not build a captive lakehouse catalog at all — the catalog is your Glue. If Qlik walks away, valid Iceberg tables remain in your account, readable by anything. What is captive is the trust layer: quality rules, semantic types, Trust Scores, data product definitions, and the lineage graph are Qlik opinions that do not lift. The intent is the instrument's concern regardless of field adoption: Qlik presents the Trust Score and data products as the governance surface — the ask is that the enterprise cede trust to the platform, and the openness of the substrate underneath is what makes that ask feel free. And the old capture surface still runs: enterprises with a decade of QVD/QVF extract libraries hold a proprietary analytic data layer readable only by the Qlik engine — the classic, coupled kind of capture, sitting right next to the new open one.\n\nCalibration: Databricks and Palantir are both strong at 1A because their governance is an authorization authority — Unity Catalog does RBAC, masking, and row-filtering at query time; the Ontology reconciles security at interaction time. Qlik's governance is curation and quality metadata (trust scoring, semantic types, data products), not an access-control authority — the lakehouse delegates access enforcement to AWS IAM and Glue. Storage capability is real but one year old and AWS-only. Partial-but-genuine on both halves of the layer: moderate. The inverse of the Databricks trade — less capability, materially less capture.",
      "borrowedJudgment": "Split by generation. The lakehouse substrate borrows AWS's judgment (S3, Glue, IAM) through a genuinely open interface the enterprise could repoint. The trust layer is where Qlik asks you to cede: quality rules, Trust Scores, data product definitions, and lineage are Ceded to Qlik and do not lift. The QVD estate is the oldest and heaviest cession in the row.",
      "notes": "Vendor follow-up: whether Open Lakehouse fine-grained access control integrates AWS Lake Formation or stops at IAM/Glue permissions — does not move the DAPM (the authority is AWS's either way) but sharpens the governance-is-not-authorization reasoning. Watch-list: Open Lakehouse streaming ingestion/transformations were announced with planned GA Q1 2026; delivery not yet confirmed in docs (see Layer 1C).",
      "components": [
        {
          "component": "Qlik Open Lakehouse (Iceberg on Customer S3 + Glue)",
          "detail": "GA Sept 2025. Iceberg tables written to the customer's own S3, cataloged in the customer's own AWS Glue catalog; interoperable with Snowflake, Athena, Trino, SageMaker. The consumed interface is a genuine multi-vendor standard — the tables lift to any Iceberg writer without rebuilding. Delegated (a standard interface consumed through Qlik's managed service; the customer owns the account but AWS operates the substrate and Qlik operates the writing layer).",
          "dapm": "Delegated"
        },
        {
          "component": "Adaptive Iceberg Optimization (Upsolver Engine)",
          "detail": "Continuous compaction, dynamic partitioning, and file cleanup. Proprietary optimizer with no open exit — but its output is standard Iceberg, so leaving means swapping optimizers, not rebuilding tables. Ceded, with a low blast radius the litmus itself concedes.",
          "dapm": "Ceded"
        },
        {
          "component": "Data Products + Trust Score",
          "detail": "Curated, governed datasets scored on accuracy, timeliness, diversity, and completeness; Data Product Agent GA June 30, 2026. The curation opinions — product definitions, the trust-scoring model, the lineage graph — are Qlik-captive. Proprietary Qlik platform — opinions captive, no open exit.",
          "dapm": "Ceded"
        },
        {
          "component": "Data Quality & Semantic Types (Talend Heritage)",
          "detail": "Validation rules, semantic types, and quality management threaded through Qlik Talend Cloud; Data Quality Agent GA June 30, 2026. Rules and validation logic do not lift to another quality platform. Proprietary Qlik platform — opinions captive, no open exit.",
          "dapm": "Ceded"
        },
        {
          "component": "QVD/QVF Associative Data Layer",
          "detail": "Proprietary extract and application data format readable only by the Qlik engine. The accumulated QVD estate — often a decade deep — is the longest-lived lock-in surface in the row: coupled, visible, classic capture. Proprietary Qlik platform — opinions captive, no open exit.",
          "dapm": "Ceded"
        }
      ]
    },
    {
      "id": "layer1b",
      "label": "Layer 1B",
      "shortName": "Retrieval",
      "title": "Context Management & Retrieval",
      "purpose": "Low-latency retrieval for RAG — vector/hybrid search, context windows",
      "status": "moderate",
      "statusLabel": "Turnkey RAG, Closed Box",
      "nvidia": [
        {
          "component": "No NVIDIA Layer 1B Dependency",
          "detail": "Retrieval runs on Qlik-operated OpenSearch; embeddings and generation are Amazon Bedrock-mediated. Whatever silicon serves them belongs to AWS's row."
        }
      ],
      "gap": "Qlik Answers (GA September 2024) is a turnkey RAG product: point it at unstructured sources, Qlik indexes them into knowledge bases, and assistants answer with citations — embeddable in your own apps via qlik-embed, and queryable by third-party assistants through the MCP Server (GA February 2026). On the structured side, the associative engine itself is the context provider: the agentic experience and MCP tools retrieve from Qlik apps and data products, and the engine's associative model — every value linked to every related value — is genuinely distinctive context for multi-step questioning. Its singular contribution is the exclusion primitive: 'what is NOT associated' is a native engine state, a signal neither SQL joins nor vector similarity provide natively. Zero retrieval infrastructure to build.\n\nThe grading line is not the managed abstraction — every managed offering on this map carries that cost. It is that Qlik's retrieval is a product feature, not an infrastructure service. Amazon Bedrock Knowledge Bases is equally managed, yet hands the customer the infrastructure interface: embedding model choice, index parameters, a retrieve API to build against. Qlik hands over none of that: no index management, no embedding or model choice (there is no BYO-model path for Answers), no retrieval API the enterprise's own AI applications can consume independent of the assistant abstraction. The entire stack is a closed box — Qlik-operated OpenSearch the customer never sees, an undocumented embedding model, and generation fixed to Amazon Bedrock (Claude), chosen by Qlik. A double-stacked cession: the retrieval opinions are Qlik's, and the reasoning core beneath them is AWS/Anthropic's, with the customer holding authority over neither.\n\nCalibration: Databricks, Palantir, and VAST are strong at 1B for general-purpose, governed retrieval the enterprise builds on — GA vector search APIs, permission-inheriting retrieval. Dell, VMware, and Nutanix are moderate with Delegated retrieval (Elastic, pgvector packaging). Qlik lands moderate from the opposite direction: real, GA, product-complete retrieval — but retrieval-as-product-feature rather than retrieval-as-infrastructure. Same altitude as the on-prem moderates, opposite DAPM texture.",
      "borrowedJudgment": "High, and double-stacked. The retrieval opinions (indexes, chunking, citation logic) are Ceded to Qlik; Qlik in turn borrows the generative core from AWS/Anthropic with no customer surface at either link. Databricks gives you model choice on top of its captive retrieval; Palantir makes the model explicitly swappable. Qlik gives you neither the retrieval layer nor the model menu.",
      "notes": "OpenSearch-as-vector-store comes from Qlik's evaluation-guide architecture docs (reasonably firm); the specific embedding model is undocumented. No GA bring-your-own-model path for Qlik Answers exists (the load-script LLM connectors are analytics sources, a different surface). Knowledge-base access control is Qlik-tenant-level (spaces, assistants), not source-ACL passthrough of the VAST/Databricks kind.",
      "components": [
        {
          "component": "Qlik Answers Knowledge Bases",
          "detail": "GA Sept 2024. Managed indexing, retrieval, and cited answers over unstructured sources. Proprietary managed RAG: indexes, chunking, and retrieval opinions do not lift; the OpenSearch underneath is invisible and not customer-operable. Leaving means rebuilding RAG elsewhere from the source documents. Proprietary Qlik platform — opinions captive, no open exit.",
          "dapm": "Ceded"
        },
        {
          "component": "Associative Engine as Agent Context (via MCP)",
          "detail": "The agentic experience and MCP Server retrieve from Qlik apps and data products; the exclusion primitive (non-association as a native engine state) is the singular capability no other row matches. MCP is a genuine multi-vendor interface, but the opinions that accumulate — Qlik app data models, associative logic — live behind the interface, not at it, and run only on the Qlik engine. Open access at the input layer, capture at the dependence layer. Standard plug, captive socket.",
          "dapm": "Ceded"
        },
        {
          "component": "Embedded Model Layer (Bedrock/Claude, Qlik-Selected)",
          "detail": "Generation and embeddings are Amazon Bedrock-mediated (Anthropic Claude), selected by Qlik with no customer model choice and no BYO endpoint. The generative judgment is inherited two vendors deep. Proprietary integration — no customer authority at either link.",
          "dapm": "Ceded"
        }
      ]
    },
    {
      "id": "layer1c",
      "label": "Layer 1C",
      "shortName": "Pipelines",
      "title": "Data Movement & Pipelines",
      "purpose": "Move/transform data — ETL/ELT, lineage, cost-aware movement, KV cache tiering",
      "status": "strong",
      "statusLabel": "Qlik's Heartland — CDC + Talend + Lakehouse Ingestion",
      "nvidia": [
        {
          "component": "No NVIDIA Layer 1C Dependency",
          "detail": "Pipelines run on CPU compute; no acceleration dependency at the movement layer."
        }
      ],
      "gap": "This is the layer Qlik built the company on — twice, by acquisition. The Attunity lineage gives it Qlik Replicate: log-based change data capture across hundreds of source/target pairs, arguably the category-defining CDC product, long-GA and massively deployed (client-managed or as the replication engine inside Qlik Talend Cloud). The Talend lineage gives it mature ETL/ELT: no-code-to-pro-code transformations, inline data quality, end-to-end lineage. The Upsolver lineage gives it the modern edge: high-throughput ingestion into Iceberg with continuous optimization. And as of June 30, 2026, agentic data engineering is GA — natural-language creation of pipelines and data products. The buyer gets any-source-to-any-target movement with quality and lineage threaded through, without owning a single pipeline server.\n\nQlik pipelines are structurally pass-through — the destination is always somebody else's system (your Snowflake, your Databricks, your S3/Iceberg). So the data always lands open, which makes the capture read as light. It is not: the accumulated opinions are hundreds of CDC task definitions, endpoint configurations, transformation logic, and quality rules, all in Qlik's proprietary frameworks. The open-source escape hatch is gone — Talend Open Studio was discontinued in January 2024; there is no OSS edition to fall back to. Leaving means rebuilding the movement layer on Fivetran, Debezium, or dbt — a standard but real migration whose weight scales with task count.\n\nCalibration: Databricks 1C is strong and the most mature data-engineering layer assessed; VAST is strong (DataEngine); Palantir and Dell are moderate. Qlik belongs in the strong cohort without stretch — the one layer where its maturity genuinely matches Databricks': Databricks leads on in-platform transformation (Spark, Photon), Qlik leads on heterogeneous movement (CDC breadth across other people's systems). Different centers, same altitude. What keeps the grade honest in 2026 rather than a 2019 reputation call is the GA record: Open Lakehouse shipped, agentic data engineering shipped, and CDC remains the widest-deployed piece of the whole Qlik estate.",
      "borrowedJudgment": "Low — the movement IP is Qlik's own (Attunity, Talend, and Upsolver acquisitions, all Qlik-owned). The enterprise inherits Qlik's judgment about replication, transformation, and ingestion, and cannot take the accumulated task definitions elsewhere. Decoupled pattern: the data lands open in the destination; the pipeline opinions stay captive in Qlik.",
      "notes": "Watch-list (dated): Open Lakehouse streaming ingestion and streaming transformations were announced (late 2025) with planned GA Q1 2026; delivery not yet confirmed in product docs — batch CDC/ingestion is long-GA and carries the cell regardless. Client-managed Talend Data Fabric believed still shipping in 2026 (minor, unconfirmed).",
      "components": [
        {
          "component": "CDC Replication (Qlik Replicate / QTC Ingestion)",
          "detail": "Log-based change data capture across hundreds of source/target pairs; long-GA, client-managed or as the managed replication engine in Qlik Talend Cloud. Task definitions, endpoint configs, and CDC opinions are proprietary; competitors exist but nothing lifts without rebuilding. Proprietary Qlik platform — opinions captive, no open exit.",
          "dapm": "Ceded"
        },
        {
          "component": "Transformations & Pipeline Framework (Talend Heritage)",
          "detail": "No-code-to-pro-code ETL/ELT in Qlik Talend Cloud and client-managed Talend Studio. Proprietary framework; the open-source edition (Talend Open Studio) was discontinued January 2024. SQL-based transformation logic is partially portable as SQL, but the pipeline layer around it does not lift. Proprietary Qlik platform — opinions captive, no open exit.",
          "dapm": "Ceded"
        },
        {
          "component": "Open Lakehouse Ingestion (Upsolver Engine)",
          "detail": "High-throughput ingestion into Iceberg with continuous optimization. Proprietary managed ingestion whose output is standard Iceberg — the same low-blast-radius Ceded as the 1A optimizer: leaving means swapping engines, not rebuilding tables.",
          "dapm": "Ceded"
        },
        {
          "component": "Agentic Data Engineering (Data Product Agent, Data Quality Agent)",
          "detail": "GA June 30, 2026. Natural-language creation and evolution of pipelines and data products. The agents and what they generate are Qlik-captive. Proprietary Qlik platform — opinions captive, no open exit.",
          "dapm": "Ceded"
        }
      ]
    },
    {
      "id": "layer2a",
      "label": "Layer 2A",
      "shortName": "Orchestration",
      "title": "Infrastructure Orchestration",
      "purpose": "GPU scheduling, quotas, RBAC, fair-share scheduling, utilization optimization",
      "status": "moderate",
      "statusLabel": "Managed Task & Cluster Orchestration; No GPU Plane",
      "nvidia": [
        {
          "component": "No GPU Plane to Depend On",
          "detail": "Layer 2A is where NVIDIA dependency concentrates for most of the map (Run:ai, GPU scheduling) — and Qlik has no GPU plane to be dependent in. The AI-compute authority question is wholly delegated to AWS/Bedrock, one row over."
        }
      ],
      "gap": "There is nothing to operate, and that is the offer. Qlik Cloud is fully managed SaaS — reload scheduling, task orchestration, and capacity management happen invisibly under a consumption model. Open Lakehouse extends this into the customer's own estate: Qlik auto-provisions and autoscales lakehouse clusters on EC2 Spot instances inside the customer's VPC, choosing spare-capacity instances to keep the compute bill down. Pipeline tasks, engine reloads, cluster lifecycle: zero orchestration burden. Client-managed Qlik Sense shops carry their own ops on their own Windows servers — retained responsibility, not a Qlik capability.\n\nTwo concerns. First, the standard one: this is proprietary orchestration the customer consumes without authority — no quotas surface, no fair-share policy, no infrastructure-as-code layer like Databricks' Asset Bundles. Second, the distinctive one: Open Lakehouse orchestration spends the customer's money under Qlik's judgment. The clusters run in your VPC on your EC2 bill, but the scaling and Spot strategy are Qlik's opinions — cost authority exercised on your account with, as far as the docs show, minimal knobs. And there is no GPU plane at all: no GPU scheduling, no accelerator awareness anywhere. Qlik has outsourced the entire AI-compute question to Amazon Bedrock, so the layer the 4+1 model watches most closely here simply has no Qlik surface.\n\nCalibration: Palantir 2A is moderate (Rubix — real, platform-scoped, Ceded orchestration the customer never configures), and Databricks 2A is moderate (owns workload orchestration, GPU pre-GA). Qlik sits in the same cohort by the same logic — orchestration exists, the vendor holds it, it is scoped to the vendor's own workloads — but at the thin end: Databricks has serverless compute on three clouds plus an IaC surface; Qlik has task scheduling and one cluster type on one cloud. What keeps this from being a gap is the real-dependence guardrail: the VPC lakehouse clusters run in your account, on your bill. Present-and-Ceded, per the cloud and Palantir convention.",
      "borrowedJudgment": "Moderate. The orchestration opinions (scheduling, scaling, Spot strategy) are Qlik's and Ceded; the capacity underneath is AWS's. The enterprise inherits both without a configuration surface — and in the lakehouse case, Qlik's operational judgment is exercised directly against the customer's own EC2 spend.",
      "notes": "Vendor follow-up (does not change the rating; important cost and operational question): can the customer pin instance types, cap scale, or opt out of Spot for lakehouse clusters, and what visibility exists into Qlik's scaling decisions against the customer's EC2 spend? Docs show auto-provisioning with a default single Spot instance and little else.",
      "components": [
        {
          "component": "Qlik Cloud Managed Compute (SaaS Task/Reload Orchestration)",
          "detail": "Fully managed reload scheduling, pipeline task orchestration, and capacity management under a consumption model. Proprietary scheduling and capacity opinions, no customer surface, no lift. Proprietary Qlik platform — opinions captive, no open exit.",
          "dapm": "Ceded"
        },
        {
          "component": "Open Lakehouse Cluster Orchestration (EC2 Spot in Customer VPC)",
          "detail": "Qlik provisions, scales, and cost-optimizes lakehouse clusters in the customer's own AWS account; the orchestration opinions are Qlik's even though the substrate and the bill are the customer's. Running in your VPC does not make it yours — self-deployable is not Retained, applied to topology.",
          "dapm": "Ceded"
        }
      ]
    },
    {
      "id": "layer2b",
      "label": "Layer 2B",
      "shortName": "Runtime",
      "title": "Application Runtime & Execution",
      "purpose": "Model serving, agent execution, inference APIs, distributed inference",
      "status": "moderate",
      "statusLabel": "Turnkey Agents & Automation; No Model Serving",
      "nvidia": [
        {
          "component": "No NVIDIA Runtime Dependency",
          "detail": "There is no inference infrastructure to be NVIDIA-dependent in; generation is outsourced to Amazon Bedrock wholesale. Unlike Dell — where NVIDIA owns the 2B runtime — Qlik's runtime absence points at AWS, not NVIDIA."
        }
      ],
      "gap": "Everything executes as a product feature, nothing as infrastructure. Qlik Predict (the former AutoML, long-GA) trains and deploys classical ML models no-code inside Qlik Cloud. The turnkey agents are GA and real: Discovery Agent monitoring for anomalies since February 2026, Predict Agent and Automate Agent since June 2026. Qlik Automate (the former Application Automation, long-GA) is the execution muscle — workflow automation with a large connector library, now triggerable by the Automate Agent, so insight-to-action genuinely executes end-to-end. Qlik Answers assistants are customer-configured (scoped to knowledge bases) and embeddable. For an analytics buyer, that is a complete loop: detect, predict, act — shipped, and among the non-hyperscaler rows arguably the most complete insight-to-execution story on the map.\n\nWhat Qlik does not ship is a runtime in the infrastructure sense. No model serving: you cannot serve an LLM or bring a model endpoint — generation is fixed Bedrock/Claude, per the 1B finding. No agent framework: you configure Qlik's agents; you cannot build and host your own on Qlik. The MCP Server points the other way — it makes Qlik a tool for agents whose runtime lives elsewhere (Claude, Copilot), an honest architectural admission that Qlik expects the agent runtime to be someone else's layer. What the enterprise accumulates here — Automate workflows, Predict models, agent configurations — is all captive, and none of it constitutes a runtime opinion that could ever move.\n\nCalibration: the strong bar at 2B is a general runtime — Databricks (any-model serving plus GA agent framework plus training) and Palantir (governed any-model agent runtime). Qlik clears none of that. The moderate cohort is Dell (runtime is NVIDIA's), Nutanix (agents in preview), VMware (foundational), VAST. Qlik fits moderate from its own angle: its agents are GA (ahead of Nutanix's preview) but they are product features, not a runtime; and the model-serving half of the layer is not offered at all, by design. Real shipped dependence keeps it out of gap; the missing runtime keeps it well out of strong.",
      "borrowedJudgment": "High for generative execution, and double-stacked: the agents' reasoning core is Anthropic-via-AWS, chosen by Qlik, invisible to the customer. Classical ML (Qlik Predict) is Qlik IP, Ceded to Qlik. The enterprise accumulates real captive artifacts here — automation libraries especially — while holding no runtime authority at any link.",
      "notes": "Watch-list (dated): Analytics Agent — planned Q3 2026, not scored. Predict model artifacts are not a portability question worth chasing: even if export existed, the feature engineering, training pipeline, and serving context around a model are all Ceded — a portable artifact with captive surroundings is the data-portability decoy one layer up.",
      "components": [
        {
          "component": "Qlik Predict (No-Code ML Train/Deploy/Predict)",
          "detail": "Long-GA (formerly Qlik AutoML). Trains, deploys, and serves classical ML predictions inside Qlik Cloud. The model artifact is not where the dependence lives; the AutoML pipeline around it is — features, training, and serving are all platform-bound. Proprietary Qlik platform — opinions captive, no open exit.",
          "dapm": "Ceded"
        },
        {
          "component": "Turnkey Agents (Discovery, Predict Agent, Automate Agent)",
          "detail": "GA Feb–June 2026. Qlik-built, Qlik-hosted product agents — configurable, not constructible. No framework for customer-built agents exists. Proprietary Qlik platform — opinions captive, no open exit.",
          "dapm": "Ceded"
        },
        {
          "component": "Qlik Automate (Workflow Execution + Connectors)",
          "detail": "Long-GA (formerly Application Automation). Workflow automation across a large connector library, triggerable by the Automate Agent. The accumulated automation library is the real capture surface in this cell — hundreds of workflows that rebuild from scratch anywhere else. Proprietary Qlik platform — opinions captive, no open exit.",
          "dapm": "Ceded"
        }
      ]
    },
    {
      "id": "layer2c",
      "label": "Layer 2C",
      "shortName": "Reasoning",
      "title": "Agentic Infrastructure — The Reasoning Plane",
      "purpose": "Policy-driven placement and resource coordination — the Autonomy Layer",
      "status": "gap",
      "statusLabel": "Bring Your Own Reasoning Plane (MCP-First)",
      "nvidia": [
        {
          "component": "No NVIDIA Layer 2C Dependency",
          "detail": "No reasoning-plane product exists to carry a dependency. The zero-NVIDIA column completes here."
        }
      ],
      "gap": "Qlik's honest answer to the reasoning plane is bring your own. The MCP Server (GA February 2026) is the clearest statement of posture in the portfolio: it makes Qlik a well-behaved, governed tool inside somebody else's agentic infrastructure — Claude, Copilot, whatever assistant platform the enterprise runs. Inside Qlik's own walls, the agentic experience coordinates the product agents, agents inherit the requesting user's permissions, and the Automate Agent prompts for confirmation before executing actions. For the buyer, agentic capability arrives without any reasoning-plane infrastructure to stand up.\n\nNone of that is a reasoning plane. Applying the test: Intelligence-2C — which agent may act, on what, under which policy — exists only as inherited platform permissions plus hardcoded confirmation prompts. There is no productized surface: no agent registry treating agents as governed assets, no gateway product with rate limits, audit, and fallbacks, no policy engine an admin configures, no supervisor for agents the customer builds (there are no customer-built agents — the 2B finding). Infrastructure-2C — live placement of inference across model, cost, and compliance tiers — is missing twice over: with a single fixed Bedrock/Claude path there is not even a routing decision to govern. The live-placement gap is universal across the instrument, noted rather than penalized; the productized-governance gap is Qlik-specific. The enterprise that deploys Qlik agents alongside Copilot agents and homegrown agents owns the cross-system reasoning plane entirely, with Qlik participating as one MCP tool among many.\n\nCalibration: Databricks earned moderate with three productized, GA governance components — Unity Catalog agent governance with On-Behalf-Of auth, the Mosaic AI Gateway, and the Supervisor Agent — and the moderate cohort (AWS, IBM, OCI, Cisco, HPE) all have multi-component productized Intelligence-2C. Qlik has product plumbing, not products: permission inheritance and confirmation dialogs are properties of the agents scored at 2B, not a distinct policy plane. That is the Nutanix/VMware/Dell/VAST cohort — gap. Scoring implicit permission inheritance as moderate would credit at 2C what every SaaS platform does by default.\n\nThe posture deserves naming without softening the score: MCP-first is arguably more architecturally honest than a thin bolt-on gateway would be. Qlik is betting the reasoning plane belongs to the enterprise — the 'bring any Layer 2C' invitation Dell makes at the bottom of the stack, made here one layer up, with the same consequence: the function stays the enterprise's to build, and the authority stays Retained by default.",
      "borrowedJudgment": "None to borrow — and that is the finding. To the extent a reasoning plane exists in a Qlik deployment, it is external: the enterprise's assistant platform reaching in through MCP, or nothing. The enterprise owns the function by default because Qlik has not claimed it.",
      "notes": "Vendor follow-up: does any GA administrative agent-governance surface exist (per-agent entitlements, agent audit console, tenant-level agent policy) beyond inherited user permissions? Product docs show none; docs are the primary source. If such a surface ships GA, the cell re-scores to thin moderate per the Databricks calibration. Watch-list (dated): Analytics Agent — planned Q3 2026. Sub-threshold signals, prose only: MCP OAuth, user-permission inheritance, human-in-the-loop confirmation prompts.",
      "components": []
    },
    {
      "id": "layer3",
      "label": "Layer 3 (+1)",
      "shortName": "Applications",
      "title": "AI Application Layer — The Value Plane",
      "purpose": "AI-powered business capabilities — business logic, workflow automation",
      "status": "strong",
      "statusLabel": "Qlik's Native Layer — The Analytics Value Plane",
      "nvidia": [
        {
          "component": "No NVIDIA Layer 3 Dependency",
          "detail": "The value plane is Qlik IP with Bedrock-mediated generation. The NVIDIA column ends the row empty end-to-end — the cleanest zero-NVIDIA row in the series; Qlik's AI dependence chain runs through AWS and Anthropic instead."
        }
      ],
      "gap": "This is what anyone buys Qlik for, and it is the deepest first-party value plane among the non-hyperscaler rows after Palantir. Qlik Cloud Analytics / Qlik Sense is a complete analytics application suite — apps, dashboards, the associative exploration experience where the exclusion primitive lives (what is not related, as a native engine state), Insight Advisor's natural-language insights — decades mature with an enormous deployed base. Qlik Answers puts a conversational application over unstructured knowledge. And the agentic loop closes end-to-end in GA product: Discovery Agent detects, Qlik Predict and the Predict Agent forecast, the Automate Agent executes into downstream business systems. Detect, predict, act — shipped; for the analytics domain, one of the most complete insight-to-action stories on the map. Embedded and OEM analytics (qlik-embed) extend the same value plane into customers' own products.\n\nTwo concerns. First, the domain boundary: this value plane is analytics and decision workflows, not operational business applications — Palantir's Foundry apps run supply chains; Qlik answers questions about data and automates what follows. Completeness within a bounded domain is the same caveat Databricks carries, and it holds here. Second, this cell is where the row's oldest and heaviest capture sits, and it is the coupled, visible kind: enterprise Qlik estates hold years of apps, data models, and set-analysis expressions — a proprietary expression language whose accumulated logic lifts to no other platform — on top of the QVD estates scored at 1A. The row's shape inverts Databricks': Qlik's newest surface (the lakehouse) is its most open, and its oldest surface (the app estate) is its most captive.\n\nCalibration: Databricks Layer 3 is strong (first-party Genie, AI/BI, and Apps, with the analytics-centric caveat honestly stated) — Qlik matches that shape with a deeper and older first-party application estate, same caveat applying. Palantir is strong and broader (operational). VMware and Nutanix are moderate (platform-enabled, not platform-provided) — Qlik ships the applications, clearing that line easily. Not partner (Dell, Cisco, HPE, IBM): nothing here is ISV-delivered.",
      "borrowedJudgment": "The application opinions are Qlik's, Ceded to Qlik — and the enterprise's accumulated artifacts (apps, data models, set-analysis expressions, automations) are the largest captive estate in the row, decades deep for legacy shops. The generative layer inside these applications inherits the AWS/Anthropic chain from 1B/2B: Qlik chooses, AWS serves, Anthropic reasons.",
      "notes": "Everything scored here is long-GA and doc-confirmed. Qlik Answers' application surface rides on the 1B components — referenced, not double-counted. Watch-list items (Analytics Agent, Q3 2026) live at 2B/2C.",
      "components": [
        {
          "component": "Analytics Applications & Associative Exploration",
          "detail": "Qlik Sense apps, dashboards, set analysis, and Insight Advisor — decades-mature, first-party, GA. The engine, the expression language, and every app built in them are proprietary; the accumulated set-analysis logic is among the least portable artifacts in the entire instrument. Proprietary Qlik platform — opinions captive, no open exit.",
          "dapm": "Ceded"
        },
        {
          "component": "Agentic Analytics Experience (Detect → Predict → Act)",
          "detail": "GA Feb–June 2026. The application-level loop over the 2B runtime: Discovery Agent detects, Predict forecasts, Automate Agent executes into downstream systems. Qlik-shaped, Qlik-bound. Proprietary Qlik platform — opinions captive, no open exit.",
          "dapm": "Ceded"
        },
        {
          "component": "Embedded & OEM Analytics (qlik-embed)",
          "detail": "GA. Embeds Qlik apps and Answers assistants into customers' own products. The embedding library is Qlik's and the embedded content is Qlik apps; OEM customers inherit the same captivity one product-tier removed. Proprietary Qlik platform — opinions captive, no open exit.",
          "dapm": "Ceded"
        }
      ]
    }
  ]
}