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I'm performing an AWS to GCP cloud migration for a client, and a big unknown was the performance of GCP CloudSQL compared to the AWS RDS. The on-paper specs were looking great but we wanted to be sure, so I did some benchmarking and GCP really cooked!
I was a long-standing advocate for AWS, but these findings have shaken that. I’d love to learn about your experience and thoughts on AWS, GCP or migrations; do drop a DM or comment.
The full story about the benchmarking is here – Link
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Most enterprises can't use AI analytics tools, and the model isn't the reason.
The tools want the data sent to them. For the consulting and professional-services firms this product was built for, that ended the conversation early. Sensitive client data could not leave their cloud.
So we flipped it. The whole platform deploys inside the customer's own AWS account, with zero data egress.
Once it's in, a business user types a question in plain English and gets a live, governed dashboard in under 60 seconds.
What happens between the question and the chart:
→ Enterprise identities scope sources, rows, columns and tenant before the query runs
→ Bedrock Agents plan the query and Bedrock Guardrails check it
→ Trino queries SQL, NoSQL and custom sources where the data already lives
→ Apache Superset turns the result into a dashboard people can save and share
→ Subscribed datasets are watched for anomalies, trends and thresholds
→ Every answer keeps its question, sources, validated query, permissions and confidence
It ships as a white-label React widget, so product teams can embed it in their own apps. It's also listed on AWS Marketplace.
Stack: React, Trino, Apache Superset, AWS Bedrock (Agents, Guardrails, AgentCore), ECS Fargate, Aurora PostgreSQL, ElastiCache, CloudFormation, isolated VPC.
The product moved into the enterprise's AWS account.
The data never moved out.
At Zenveus, we start with what the software has to prove: the data, the permissions and the trail it leaves behind.