# Why traconiq Migrated Their Multi-TB Telemetry Dataset to Neon

DevFeed: [Why traconiq Migrated Their Multi-TB Telemetry Dataset to Neon](<https://devfeed.tech/articles/why-traconiq-migrated-their-multi-tb-telemetry-dataset-to-neon-5862.md>)

Original publisher: [Read original article](<https://neon.com/blog/why-traconiq-migrated-from-aws-rds-to-neon>)

Author: Carlota Soto

Published: 2025-07-01T16:21:46Z

Content type: article

Language: en

Sources: [Blog -- Neon Docs](<https://devfeed.tech/sources/blog-neon-docs.md>)

Topics: [dataset](<https://devfeed.tech/topics/dataset.md>), [telemetry](<https://devfeed.tech/topics/telemetry.md>), [Amazon RDS](<https://devfeed.tech/topics/amazon-rds.md>), [autoscaling](<https://devfeed.tech/topics/autoscaling.md>), [Scalability](<https://devfeed.tech/topics/scalability.md>), [Databases](<https://devfeed.tech/topics/databases.md>), [Amazon S3](<https://devfeed.tech/topics/amazon-s3.md>)

Tags: [amazon-rds](<https://devfeed.tech/tags/amazon-rds.md>), [autoscaling](<https://devfeed.tech/tags/autoscaling.md>), [backup](<https://devfeed.tech/tags/backup.md>), [case-studies](<https://devfeed.tech/tags/case-studies.md>), [cost](<https://devfeed.tech/tags/cost.md>), [dataset](<https://devfeed.tech/tags/dataset.md>), [postgres](<https://devfeed.tech/tags/postgres.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [s3](<https://devfeed.tech/tags/s3.md>), [scalability](<https://devfeed.tech/tags/scalability.md>), [snapshots](<https://devfeed.tech/tags/snapshots.md>), [storage](<https://devfeed.tech/tags/storage.md>), [telemetry](<https://devfeed.tech/tags/telemetry.md>)

## AI overview

This article explains why traconiq migrated its multi-terabyte telemetry dataset from Amazon RDS to Neon. It describes rising costs from multiple regions and environments, fixed peak-capacity provisioning despite cyclical load, storage volumes that could not shrink after data was moved to Amazon S3, and the limitations and expense of snapshots for backups. Neon is presented as enabling more efficient scaling through branching and autoscaling.

## Source excerpt

"Our workload ingests hundreds of data points per second and our RDS costs were increasing, especially since we had multiple regions and environments. With Neon, we found a way to scale our setup more efficiently, using branching instead of duplicating instances and autoscaling t...