Vertex is a platform to build ML. EkamFlow is customer decisioning, ready.
Google Vertex AI is a full ML platform for teams building custom models on Google Cloud. EkamFlow is a customer decisioning layer — churn, LTV, next best action, recommendations, lead scoring and 9 more predictions — trained on your data and served through one API. No ML team required.
Google Vertex AI is the right choice when your team is deeply invested in Google Cloud, wants access to AutoML tools and BigQuery ML, and has ML engineers building custom models. EkamFlow is the right choice when the goal is customer decisioning (churn, next best action, LTV, recommendations, lead scoring) and you want per-customer predictions in production in weeks rather than months — with no ML engineers on staff. Companies often use both: Vertex for bespoke ML and BigQuery-native workflows; EkamFlow for the standard customer-decisioning stack that ships in weeks.
When each is the right choice
Use Vertex AI when…
- Your data warehouse is BigQuery and you want tight ML+warehouse integration
- You have a Google Cloud-native stack and want ML in the same environment
- You have an ML team building custom models and using AutoML tools
- You need Vertex-specific features (Feature Store, Model Registry, Pipelines)
- Foundation-model tuning via Model Garden / PaLM / Gemini is core to your roadmap
Use EkamFlow when…
- You want ready-to-use customer decisioning, not tools for building ML
- You want predictions in weeks — churn, LTV, NBA, recs, lead scoring
- You have no ML team, or your team is bottlenecked
- You're on a non-GCP warehouse (Snowflake, Databricks, Redshift) or multi-cloud
- Marketing, growth, and CRM teams are the primary consumers
Vertex AI vs EkamFlow — side by side
| Dimension | Vertex AI | EkamFlow |
|---|---|---|
| Category | Horizontal ML platform (build-your-own) | Customer decisioning platform (ready-to-use) |
| Time to first prediction in production | 3–6 months (with AutoML) or 6–12 months (custom) | Days to weeks |
| Team required | ML engineer / data scientist | None — warehouse read access is enough |
| Use case coverage | Whatever you can build (or AutoML templates) | 13 pre-built customer-decisioning use cases |
| Cloud lock-in | GCP-native | Runs on AWS, GCP, or Azure; warehouse-agnostic |
| Model retraining | You build the pipeline (or Vertex Pipelines) | Continuous, automatic |
| Feature engineering | Manual or Feature Store | Automatic from warehouse schema |
| Latency (real-time serving) | Depends on Vertex Endpoints config | <60ms |
| Marketing / CRM integration | Custom-built | REST API + reverse-ETL to CDPs, CRMs, ESPs |
| Cost profile | $200k–$1M/yr team + Vertex + BigQuery costs | Single vendor cost, no team required |
| Where they win | GCP-native ML, AutoML, foundation-model tuning | Standard customer decisioning at scale, any cloud |
When teams typically switch to EkamFlow
BigQuery ML hit its ceiling for customer decisioning
Teams start with BigQuery ML for churn or LTV — it's fast to try and lives in the warehouse. But BQML plateaus for real-time serving, complex feature engineering, and continuous retraining. Rather than migrate to full Vertex AI custom models (a 6-month project), teams switch the customer-decisioning use cases to EkamFlow and keep BigQuery for analytics.
Marketing needs move faster than the ML team
Marketing teams need churn scores or NBA now, not next quarter. The internal ML team using Vertex is busy with other projects and can't ship in the marketing team's timeframe. EkamFlow lets marketing move on the standard use cases independently while the ML team stays focused on bespoke work.
Multi-cloud or non-GCP warehouse constraint
Vertex is deeply GCP-integrated. Companies that run Snowflake, Databricks, Redshift, or a multi-cloud architecture find the round-trip cost and complexity of using Vertex for customer decisioning outweighs the tooling benefit. EkamFlow is warehouse-agnostic and runs in whichever cloud the warehouse is in.
Frequently asked about EkamFlow vs Vertex AI
BigQuery ML is excellent for exploratory model-building and warehouse-native predictions — but has limits for real-time serving, complex feature engineering, and continuous retraining. EkamFlow starts where BQML tops out: real-time predictions, automatic feature engineering across joined tables, and continuous retraining without pipeline maintenance. Many customers use BQML for analytics and EkamFlow for production customer decisioning.
Yes. BigQuery is a first-class supported warehouse. EkamFlow reads directly from BigQuery, trains the private model in your cloud environment, and returns predictions via REST API or reverse-ETL back into your GCP-native stack.
AutoML is a tool for training a model with less code, but you still need to prepare features, deploy the model, integrate serving, monitor drift, and retrain. AutoML solves the model-training step; EkamFlow solves the entire lifecycle for a specific set of customer-decisioning use cases. Different products for different jobs.
Yes, and many customers do. Vertex handles bespoke ML, foundation-model fine-tuning, and custom pipelines. EkamFlow handles the standard customer-decisioning use cases (churn, LTV, NBA, recs, lead scoring). Both platforms read from the same warehouse, so there's no data-duplication overhead.
Both platforms respect region and data-residency requirements. Vertex runs in GCP regions of your choice; EkamFlow runs in your cloud environment (AWS, GCP, or Azure) and never moves data outside your perimeter. For GCP-only shops, EkamFlow can run in a GCP region alongside Vertex.
That's not what EkamFlow does. Foundation-model fine-tuning for generative AI, LLM-powered features, or chatbots is squarely Vertex's territory (or SageMaker's, or a hyperscaler equivalent). EkamFlow is focused on tabular customer decisioning — churn, next best action, recommendations, propensity — which is a fundamentally different ML problem.
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