Azure Machine Learning, LlamaIndex, Google Vertex AI vs Azure AI Document Intelligence

The registry records Azure Machine Learning as available on vendor cloud or private cloud; it is scored here as enterprise SaaS.

The registry records LlamaIndex as available on self-hosted or private cloud; it is scored here as self-hosted.

The registry records Google Vertex AI as available on vendor cloud or private cloud; it is scored here as private cloud.

The registry records Azure AI Document Intelligence as available on vendor cloud or private cloud or on-premise enterprise; it is scored here as enterprise SaaS.

For this brief — confidential documents — enterprise SaaS is conditional: worth it only if the conditions in the options section hold. Change the brief and this verdict changes.

For this brief — confidential documents — self-hosted is a strong alternative. Change the brief and this verdict changes.

For this brief — confidential documents — private cloud is the recommended shape of answer. Change the brief and this verdict changes.

For this brief — confidential documents — enterprise SaaS is conditional: worth it only if the conditions in the options section hold. Change the brief and this verdict changes.

Azure Machine Learning is a platform for running a model you choose, not a product built for one job, so there is no use-case overlap to measure — what it does depends on what you build on it. The fit that can be assessed is the deployment's, in the rows below.

Covers your main objective (data extraction) and 2 of 3 secondary objectives (document Q&A, internal company search).

Google Vertex AI is a platform for running a model you choose, not a product built for one job, so there is no use-case overlap to measure — what it does depends on what you build on it. The fit that can be assessed is the deployment's, in the rows below.

Covers your main objective (data extraction) and 1 of 3 secondary objectives (document classification).

You stated no hosting preference, so no option is favoured on that basis. No hard restriction on where processing happens was stated. This is a vendor-operated service. Your cloud account does not determine where it runs; confirm the vendor’s data processing and integration with your existing systems.

You stated no hosting preference, so no option is favoured on that basis. No hard restriction on where processing happens was stated. You did not name a cloud you already run on, so no design is favoured on that basis.

You stated no hosting preference, so no option is favoured on that basis. No hard restriction on where processing happens was stated. You did not name a cloud you already run on, so no design is favoured on that basis.

You stated no hosting preference, so no option is favoured on that basis. No hard restriction on where processing happens was stated. This is a vendor-operated service. Your cloud account does not determine where it runs; confirm the vendor’s data processing and integration with your existing systems.

External data transfer: yes. No catalogue deployment names Azure Machine Learning, so this is read from “Document automation on a specialised SaaS product”, the best-matching enterprise SaaS deployment for this brief. Confidential content leaves your control on the Finance / operations team (validation) → Document-automation product link.

External data transfer: none. No catalogue deployment names LlamaIndex, so this is read from “Document classification and OCR pipeline”, the best-matching self-hosted deployment for this brief. No edge in this design crosses out of the company network.

External data transfer: some. Computed from “Document extraction pipeline in your cloud account”, the catalogue deployment that names Google Vertex AI and best matches this brief. Confidential content leaves your premises for your own cloud tenancy ("Private endpoints"). You keep control of the account; the provider is a processor, so a DPA and a documented region apply.

External data transfer: yes. No catalogue deployment names Azure AI Document Intelligence, so this is read from “Document automation on a specialised SaaS product”, the best-matching enterprise SaaS deployment for this brief. Confidential content leaves your control on the Finance / operations team (validation) → Document-automation product link.

We have fetched a supported-countries page for Azure Machine Learning, which says where the product may be bought — not where content is processed or stored. No residency commitment has been read, so the residency question is unknown. The vendor is headquartered in United States, which is where a transfer question starts, not where it ends.[1]

Self-hosted: the content stays wherever you run the server, so residency is a property of your own infrastructure rather than a vendor commitment.

We have fetched a supported-countries page for Google Vertex AI, which says where the product may be bought — not where content is processed or stored. No residency commitment has been read, so the residency question is unknown. The vendor is headquartered in United States, which is where a transfer question starts, not where it ends.[2]

We have fetched a supported-countries page for Azure AI Document Intelligence, which says where the product may be bought — not where content is processed or stored. No residency commitment has been read, so the residency question is unknown. The vendor is headquartered in United States, which is where a transfer question starts, not where it ends.[1]

Published and fetched: data processing agreement, subprocessor list. Still open: training on customer data, zero retention option — ask the vendor.[3][4]

Run on your own infrastructure, so the vendor questions apply only to whatever you still buy. We hold none of the four commitments for this subject, and for a self-hosted deployment none of them is required.

Published and fetched: data processing agreement, subprocessor list. Still open: training on customer data, zero retention option — ask the vendor.[5][6]

Published and fetched: data processing agreement, subprocessor list. Still open: training on customer data, zero retention option — ask the vendor.[3][4]

5–14 FDE-days to implement. No catalogue deployment names Azure Machine Learning, so this is read from “Document automation on a specialised SaaS product”, the best-matching enterprise SaaS deployment for this brief.

6–15 FDE-days to implement. No catalogue deployment names LlamaIndex, so this is read from “Document classification and OCR pipeline”, the best-matching self-hosted deployment for this brief.

8–22 FDE-days to implement. Computed from “Document extraction pipeline in your cloud account”, the catalogue deployment that names Google Vertex AI and best matches this brief.

5–14 FDE-days to implement. No catalogue deployment names Azure AI Document Intelligence, so this is read from “Document automation on a specialised SaaS product”, the best-matching enterprise SaaS deployment for this brief.

The vendor runs the service. Your standing work is access control, the policy staff work under, and re-reading the contract and subprocessor list when they change — not patching or capacity.

You own GPU drivers and the model server, the connectors and their credentials, operating-system patching and backups. Assessed difficulty 3/5 is the shape of that work; someone has to hold it after go-live.

You own GPU drivers and the model server, the connectors and their credentials, operating-system patching and backups. Assessed difficulty 4/5 is the shape of that work; someone has to hold it after go-live.

The vendor runs the service. Your standing work is access control, the policy staff work under, and re-reading the contract and subprocessor list when they change — not patching or capacity.

3/5. Starting point 1/5: a vendor product, configured rather than installed. +0.5 Single sign-on adds an identity provider, group-to-role mapping and a joiner/leaver process to the deployment.

3/5. Starting point 2/5: software you install, run and keep running on your own machines. +0.5 You run the model server yourself: GPU drivers, quantisation choice, memory headroom and restarts are all yours to own.

4/5. Starting point 2/5: your own tenancy to build in, but no hardware to buy or rack. +0.5 You run the model server yourself: GPU drivers, quantisation choice, memory headroom and restarts are all yours to own.

3/5. Starting point 1/5: a vendor product, configured rather than installed. +0.5 Single sign-on adds an identity provider, group-to-role mapping and a joiner/leaver process to the deployment.

USD 3,800 – 27,000 — one-off — for Azure Machine Learning at this headcount. No catalogue deployment names Azure Machine Learning, so this is read from “Document automation on a specialised SaaS product”, the best-matching enterprise SaaS deployment for this brief. Licences are not in that figure: no per-seat price has been read from the vendor’s own pricing page, so the software line is excluded from the total rather than guessed. Read it as implementation only.

USD 6,100 – 33,000 — one-off — for LlamaIndex at this headcount. No catalogue deployment names LlamaIndex, so this is read from “Document classification and OCR pipeline”, the best-matching self-hosted deployment for this brief.

USD 6,100 – 43,000 — one-off — for Google Vertex AI at this headcount. Computed from “Document extraction pipeline in your cloud account”, the catalogue deployment that names Google Vertex AI and best matches this brief.

USD 3,800 – 27,000 — one-off — for Azure AI Document Intelligence at this headcount. No catalogue deployment names Azure AI Document Intelligence, so this is read from “Document automation on a specialised SaaS product”, the best-matching enterprise SaaS deployment for this brief. Licences are not in that figure: no per-seat price has been read from the vendor’s own pricing page, so the software line is excluded from the total rather than guessed. Read it as implementation only.

Azure Machine Learning is a vendor product: you configure what the vendor exposes — policies, connectors, retention settings — and nothing below that line.

The registry files LlamaIndex as open source, so the interface, retrieval behaviour and the model behind it can be changed — subject to the licence, which is a fetched fact on its own page.

Google Vertex AI is a vendor product: you configure what the vendor exposes — policies, connectors, retention settings — and nothing below that line.

Azure AI Document Intelligence is a vendor product: you configure what the vendor exposes — policies, connectors, retention settings — and nothing below that line.

8 issues spotted for this brief, 0 of them legal requirements; 0 carry a quote from the instrument they rest on. Leading with: Confidentiality duties bind independently of data protection law.

7 issues spotted for this brief, 0 of them legal requirements; 0 carry a quote from the instrument they rest on. Leading with: Self-hosting moves the security obligation to you.

9 issues spotted for this brief, 0 of them legal requirements; 0 carry a quote from the instrument they rest on. Leading with: Confidentiality duties bind independently of data protection law.

8 issues spotted for this brief, 0 of them legal requirements; 0 carry a quote from the instrument they rest on. Leading with: Confidentiality duties bind independently of data protection law.