Google Cloud
Google Vertex AI
Google Cloud's platform for building, training, and running AI models, now being renamed Gemini Enterprise Agent Platform.
A data science platform where analysts and coders build AI models and agents side by side, using visual steps or written code.
Dataiku is a data science and AI platform where analysts and data scientists prepare data, build and deploy machine learning models, and now build AI agents through its Agent Hub, using either visual drag-and-drop steps or written code. A feature called LLM Mesh lets a team route requests to different language model providers from one place and monitor how each is used. Admins set governance policies, track data lineage, and manage approvals across every model and agent in the system. It is made by the company Dataiku, runs on AWS, Azure, or Google Cloud, and connects to data warehouses such as Snowflake and Databricks. Gartner named Dataiku a Leader in its Magic Quadrant for the fourth year running.
This fits a company with both technical data scientists and business analysts who need to work in the same tool, since it supports visual workflows and raw code side by side. A good starting point is a pilot with one analytics or operations team building a single model, such as a churn predictor, before opening the platform to more users. Dataiku does not publish list pricing, so treat any number from a sales conversation as a starting point for negotiation and ask for a time-boxed trial first. The main risks are the cost of named user licenses as more teams adopt the tool, and the effort needed to set clear governance rules before agents and models multiply across departments.
These estimates cover licensing, setup, integrations, staff time, security, administration, and support.
| Cost measure | Low | Base | High |
|---|---|---|---|
| First-year total | $81.3K | $128.2K | $206.5K |
| Three-year total | $175.2K | $264.6K | $409.1K |
| First-year cost per unit | $81.3K | $128.2K | $206.5K |
| Average annual cost per unit (over three years) | $58.4K | $88.2K | $136.4K |
| Component | Low | Base | High |
|---|---|---|---|
| Licensing and usage | $27K | $36K | $48.6K |
| Implementation | $13.2K | $22K | $37.4K |
| Integration | $15.4K | $28K | $50.4K |
| Staff time and change management | $8.4K | $12K | $16.8K |
| Security | $6.3K | $9K | $13.5K |
| Administration | $4.9K | $6.5K | $8.8K |
| Support | $2.3K | $3K | $4.1K |
Estimate assumptions: Based on 1 production workspace. This is a planning allowance, not verified product pricing. Confirm the vendor’s billing unit and current price or quote before budgeting.
Benefits have not been estimated: The current research does not estimate potential savings, return on investment, or how long it would take to recover the cost. Earlier benefit estimates are excluded.
Confirm current product identity, commercial packaging, data processing terms, sign-in and access rules, retention, integrations, support model, implementation effort, and rollback conditions.
Verify identity, package, availability, ownership, pricing, and security evidence before approving a pilot
Do not approve a pilot yet. Verify the current product identity, package, availability, owner, pricing, and security evidence; then define one workflow, a baseline, and rollback criteria.
Define what success looks like for a test of Dataiku and assign someone to lead it.
Once the requirements above are met, compare a small trial with how your team works today.
Track adoption, output quality, business results, and actual costs against the estimate.
Use the results to decide whether to stop, adjust, or expand the pilot.
Confirm encryption, how the vendor uses your data, customer data separation, how long data stays and how to delete it, activity records, sign-in and user setup, where data is handled, other companies that process data, past incidents, and what your team must manage.
Review before rollout: confirm how to export your data, revoke access, and return to your existing workflow. Assign an owner and test the rollback plan before expanding use.
Do not approve a pilot yet. Verify the current product identity, package, availability, owner, pricing, and security evidence; then define one workflow, a baseline, and rollback criteria.
Google Cloud
Google Cloud's platform for building, training, and running AI models, now being renamed Gemini Enterprise Agent Platform.
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Why it’s an alternative—
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