MLflow, Amazon SageMaker Training, Baseten vs Cerebras Inference

The registry records MLflow as available on self-hosted or private cloud or vendor cloud; it is scored here as enterprise SaaS.

The registry records Amazon SageMaker Training as available on vendor cloud or private cloud; it is scored here as enterprise SaaS.

The registry records Baseten as available on vendor_api or private cloud; it is scored here as a model vendor's API.

The registry records Cerebras Inference as available only on vendor_api; it is scored here as a model vendor's API.

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 — 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 — a model vendor's API is a strong alternative. Change the brief and this verdict changes.

For this brief — confidential documents — a model vendor's API is a strong alternative. Change the brief and this verdict changes.

Covers your main objective (choosing a model) and 1 of 2 secondary objectives (prediction from operational data).

Does not cover your main objective (choosing a model), only prediction from operational data — so functional fit is capped at 0.25 however well the rest fits.

Covers your main objective (choosing a model) and 1 of 2 secondary objectives (agent workflow).

Covers your main objective (choosing a model) and 1 of 2 secondary objectives (agent workflow).

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. 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.

External data transfer: some. Computed from “Predictive maintenance with classical ML”, the catalogue deployment that names MLflow and best matches this brief. Confidential content leaves your premises for your own cloud tenancy ("Private networking"). 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 Amazon SageMaker Training, so this is read from “Customer-support AI agent on a SaaS platform”, the best-matching enterprise SaaS deployment for this brief. Confidential content leaves your control on the Customers → Support AI agent (vendor platform) link.

External data transfer: yes. No catalogue deployment names Baseten, so this is read from “Agent workflow on a model vendor’s API”, the best-matching a model vendor's API deployment for this brief. Confidential content leaves your control on the Tools + retrieval (your APIs and data) → Vendor model API link.

External data transfer: yes. No catalogue deployment names Cerebras Inference, so this is read from “Agent workflow on a model vendor’s API”, the best-matching a model vendor's API deployment for this brief. Confidential content leaves your control on the Tools + retrieval (your APIs and data) → Vendor model API link.

We hold no fetched residency commitment for MLflow. That is a question to put to the vendor, not an assumption to make about them.

We hold no fetched residency commitment for Amazon SageMaker Training. That is a question to put to the vendor, not an assumption to make about them. The vendor is headquartered in United States, which is where a transfer question starts, not where it ends.

We hold no fetched residency commitment for Baseten. That is a question to put to the vendor, not an assumption to make about them. The vendor is headquartered in United States, which is where a transfer question starts, not where it ends.

We hold no fetched residency commitment for Cerebras Inference. That is a question to put to the vendor, not an assumption to make about them.

We have fetched none of the four commitments for MLflow: data processing agreement, subprocessor list, training on customer data, zero retention option. Each is a question for the vendor, and until it is answered it is unknown rather than absent.

We have fetched none of the four commitments for Amazon SageMaker Training: data processing agreement, subprocessor list, training on customer data, zero retention option. Each is a question for the vendor, and until it is answered it is unknown rather than absent.

We have fetched none of the four commitments for Baseten: data processing agreement, subprocessor list, training on customer data, zero retention option. Each is a question for the vendor, and until it is answered it is unknown rather than absent.

We have fetched none of the four commitments for Cerebras Inference: data processing agreement, subprocessor list, training on customer data, zero retention option. Each is a question for the vendor, and until it is answered it is unknown rather than absent.

10–30 FDE-days to implement. Computed from “Predictive maintenance with classical ML”, the catalogue deployment that names MLflow and best matches this brief.

5–15 FDE-days to implement. No catalogue deployment names Amazon SageMaker Training, so this is read from “Customer-support AI agent on a SaaS platform”, the best-matching enterprise SaaS deployment for this brief.

8–22 FDE-days to implement. No catalogue deployment names Baseten, so this is read from “Agent workflow on a model vendor’s API”, the best-matching a model vendor's API deployment for this brief.

8–22 FDE-days to implement. No catalogue deployment names Cerebras Inference, so this is read from “Agent workflow on a model vendor’s API”, the best-matching a model vendor's API 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.

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 the identity integration and joiner/leaver process, 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 the identity integration and joiner/leaver process, 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.

3/5. Starting point 2/5: your own tenancy to build in, but no hardware to buy or rack. +1 You train the model rather than calling one: framing the target, engineering features, validating on time rather than at random, and retraining when the inputs drift. That is data-science work with its own review cycle, not a configuration step.

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 The model runs on the vendor’s infrastructure: there is no capacity planning, no GPU driver, no model upgrade window and no failover for you to design.

3/5. Starting point 2/5: software you install, run and keep running on your own machines. -0.5 The model runs on the vendor’s infrastructure: there is no capacity planning, no GPU driver, no model upgrade window and no failover for you to design.

USD 7,600 – 58,000 — one-off — for MLflow at this headcount. Computed from “Predictive maintenance with classical ML”, the catalogue deployment that names MLflow and best matches 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 3,800 – 29,000 — one-off — for Amazon SageMaker Training at this headcount. No catalogue deployment names Amazon SageMaker Training, so this is read from “Customer-support AI agent on a SaaS platform”, 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 – 43,000 — one-off — for Baseten at this headcount. No catalogue deployment names Baseten, so this is read from “Agent workflow on a model vendor’s API”, the best-matching a model vendor's API deployment for this brief.

USD 6,100 – 43,000 — one-off — for Cerebras Inference at this headcount. No catalogue deployment names Cerebras Inference, so this is read from “Agent workflow on a model vendor’s API”, the best-matching a model vendor's API deployment for this brief.

The registry files MLflow 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.

Amazon SageMaker Training is a vendor product: you configure what the vendor exposes — policies, connectors, retention settings — and nothing below that line.

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

Cerebras Inference 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.

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.

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.

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.