How nOps Rebuilt Their Cloud Optimization Platform on Databricks Lakebase, and Why Other ISVs Should Too
TL;DR · AI Summary
The article promotes nOps' migration to Databricks Lakebase but lacks technical depth, architectural comparisons, or measurable engineering outcomes, serving primarily as product marketing.
Key Takeaways
- nOps migrated its cloud optimization platform to Databricks Lakebase for unified
- The article urges other ISVs to follow suit but provides no quantitative evidenc
- This is a vendor-driven case study designed to promote Lakebase as a platform fo
Mindmap
See how the topics connect at a glance.
查看大纲文本（无障碍 / 无 JS 友好）
- nOps迁移到Databricks Lakebase
How nOps Rebuilt Their Cloud Optimization Platform on Databricks Lakebase, and Why Other ISVs Should Too | Databricks Blog
[](http://www.databricks.com/)
[](http://www.databricks.com/)
- Why Databricks
- * Discover
- Customers
- Partners
- Product
- * Databricks Platform
- Integrations and Data
- Pricing
- Open Source
- Solutions
- * Databricks for Industries
- Cross Industry Solutions
- Migration & Deployment
- Solution Accelerators
- Resources
- * Learning
- Events
- Blog and Podcasts
- Get Help
- Dive Deep
- About
- * Company
- Careers
- Press
- Security and Trust
- DATA + AI SUMMIT 
Table of contents
Table of contents
Table of contents
PlatformMay 5, 2026
How nOps Rebuilt Their Cloud Optimization Platform on Databricks Lakebase, and Why Other ISVs Should Too
by Bryan Smith
nOps, a Databricks Built On partner managing over $4 billion in annual cloud spend, migrated their production application to Databricks Lakebase. The result was a faster, simpler architecture that eliminated the glue between their app and their analytics, and a playbook for ISVs looking to do the same.
Every ISV building on Databricks eventually hits the same architectural crossroads: your analytics live in the Lakehouse, but your application needs a relational database for low-latency reads and writes. So you bolt on a separate Postgres instance (maybe RDS, maybe something self-managed) and suddenly you're maintaining ETL pipelines, cron jobs, and change-detection logic just to keep two systems in sync.
nOps lived that reality for years. And then they found a better way.
nOps: Automating Cloud Savings at Scale
For those unfamiliar, nOps is an automated cloud cost optimization platform that manages commitment-based discounts across AWS, GCP, and Azure. Their approach is distinctly "always-on." They monitor, purchase, and exchange cloud commitments on an hourly basis, using machine learning to balance effective savings rates against commitment lock-in risk. The model is performance-based: nOps only charges a percentage of the incremental savings they generate.
It's a data-intensive operation. Every hour, nOps analyzes usage patterns across thousands of customer accounts, evaluates commitment portfolios across three major cloud providers and dozens of services, and makes automated purchasing decisions. On top of that, they surface cost visibility, forecasting, and anomaly detection through a centralized FinOps platform.
The analytical backbone for all of this has long been Databricks Lakehouse. But the front-end application, the platform customers log into to see their savings, manage budgets, and explore cost data, needed something more.
The Problem: Two Worlds, Loosely Connected
nOps's previous architecture was a familiar pattern for ISVs on Databricks. Advanced analytics and metric computation ran in the Lakehouse. Customer-facing data (account configurations, user preferences, rapidly changing client-specific state) lived in a separate relational database powered by third-party vendors and homegrown solutions.
The seams between these two systems created real friction. Scheduled jobs and cron-based change detection were required to keep the front-end database and the Lakehouse in sync. Data that was "live" in one system might take minutes or longer to appear in the other. And the operational overhead of managing a separate database stack, with its own scaling, backup, and security concerns, pulled engineering time away from what nOps actually does best: building commitment automation.
When nOps expanded from AWS-only to multi-cloud coverage across GCP and Azure in early 2026, the growing workloads strained this architecture. The team decided to rebuild the platform, this time focusing on their specialty and choosing infrastructure that simply works.
The Decision: Why Lakebase
nOps selected Databricks Lakebase, a fully managed PostgreSQL database integrated directly with the Lakehouse, as the OLTP backbone for their new platform.
Jordan Stein, Director of Product at nOps, pointed to three factors that made Lakebase the right fit:
- Tight coupling to the Lakehouse. This was the biggest factor. With Lakebase, nOps's data engineering teams can immediately access frequently changing customer data from their Lakehouse pipelines without scheduled jobs, crons, or lag. As Jordan put it: "We're talking scheduled jobs that had to run, crons that are coming and picking up those changes, whereas now we know that the moment it's live, we can consume it. This has been a game changer for us."
- Auto-scaling and auto-stop. Even with aggressive auto-stop settings during development, the nOps team was "shocked by the performance." Lakebase's serverless compute adjusts to workload demands and scales to zero when idle, which matters for a cost-optimization company that practices what it preaches.
- Ease of adoption. Point-in-time restore has already proven valuable. Flexible OAuth roles simplify access control. And because Lakebase lives within the Databricks workspace, their teams are working in a platform they already know. No new tool to learn, no separate console to manage.
The Architecture: One Platform, Tightly Integrated
Here's what nOps's new architecture looks like:
Lakebase serves as the central Postgres database and single source of truth for both the front-end application and their AI infrastructure.
Databricks Lakehouse continuously consumes data from Lakebase for analysis and metric computation.
The nOps platform automatically discovers and surfaces Databricks Metric Views, so standardized metrics computed in the Lakehouse show up consistently in the front-end.
Data flows in one direction, from Lakebase into the Lakehouse for analytics, with no direct write-back needed. This keeps the architecture clean and the source of truth unambiguous.
The rest of the stack follows the same approach: Vercel for hosting and observability, WorkOS for authentication, and Databricks for everything data.
Hear It from nOps
Jordan Stein recently walked through the full nOps Lakebase migration story in a partner spotlight presentation. Watch the video to hear how the transition went, what surprised them about performance, and how the Lakehouse integration changed their data engineering workflows:
The ISV Playbook: Why Lakebase Changes the Game
nOps's story isn't unique. Nearly every ISV building on Databricks faces the same OLTP-meets-analytics tension. What's worth paying attention to is how cleanly Lakebase resolves it.
Eliminate the sync tax. The most expensive code in any ISV's stack is often the code that moves data between systems. Lakebase's native integration with Unity Catalog and one-click Delta Lake sync replaces custom ETL pipelines with managed infrastructure. That's engineering time you get back.
One governance model. When your OLTP database is registered as a Unity Catalog asset, you get unified governance, lineage, and access control across operational and analytical data. No more managing security policies in two places.
Postgres compatibility means zero rewrite. Lakebase is fully managed PostgreSQL. Your existing libraries, ORMs, and SQL tools work out of the box. Extensions like pgvector and PostGIS are supported. You migrate by pointing your app at a new connection string, not by rewriting queries.
Scale economics that make sense. Usage-based pricing with scale-to-zero means you're not paying for idle capacity. For ISVs with variable workloads (and which ISV doesn't have variable workloads?) this directly impacts unit economics.
Ship faster. When your application database and your data warehouse are the same platform, an entire category of integration work disappears. Your team ships features instead of maintaining plumbing.
Early Adopters, Real Impact
nOps is a good example of what an innovative Built On partner looks like. Rather than waiting for Lakebase to mature through multiple release cycles, they recognized the architectural fit early, committed to a production migration, and are already seeing results: faster data pipelines, lower operational overhead, and a better experience for their customers.
That willingness to move early is strategically smart too. By building on Lakebase now, nOps has a tighter integration with the Databricks platform than competitors who are still duct-taping separate database stacks together. Their platform is simpler to operate and faster to extend.
Get Started
**Explore Lakebase**. If you're an ISV building on Databricks, or considering it, learn more about Lakebase and how it can simplify your architecture.
**Explore nOps**. If your organization is looking to reduce cloud costs across AWS, GCP, or Azure without the commitment risk, visit nOps to see how their automated optimization platform, now powered by Databricks Lakebase, can help.
Get the latest posts in your inbox
Subscribe to our blog and get the latest posts delivered to your inbox.
Sign up
*
Work Email
*
Country Country*
By clicking “Subscribe” I understand that I will receive Databricks communications, and I agree to Databricks processing my personal data in accordance with its Privacy Policy.
Subscribe
Why Databricks
Discover
Customers
Partners
Why Databricks
Discover
Customers
Partners
Product
Databricks Platform
- Platform Overview
- Sharing
- Governance
- Artificial Intelligence
- Business Intelligence
- Database
- Data Management
- Data Warehousing
- Data Engineering
- Business Productivity
- Application Development
- Security
Pricing
Integrations and Data
Product
Databricks Platform
- Platform Overview
- Sharing
- Governance
- Artificial Intelligence
- Business Intelligence
- Database
- Data Management
- Data Warehousing
- Data Engineering
- Business Productivity
- Application Development
- Security
Pricing
Open Source
Integrations and Data
Solutions
Databricks For Industries
- Communications
- Financial Services
- Healthcare and Life Sciences
- Manufacturing
- Media and Entertainment
- Public Sector
- Retail
- View All
Cross Industry Solutions
Solutions
Databricks For Industries
- Communications
- Financial Services
- Healthcare and Life Sciences
- Manufacturing
- Media and Entertainment
- Public Sector
- Retail
- View All
Cross Industry Solutions
Data Migration
Professional Services
Solution Accelerators
Resources
Learning
Events
Blog and Podcasts
Resources
Documentation
Customer Support
Community
Learning
Events
Blog and Podcasts
About
Company
Careers
Press
About
Company
Careers
Press
Security and Trust
Databricks Inc.
160 Spear Street, 15th Floor
San Francisco, CA 94105
1-866-330-0121
- [](https://www.linkedin.com/company/databricks)
- [](https://www.facebook.com/pages/Databricks/560203607379694)
- [](https://twitter.com/databricks)
- [](https://www.databricks.com/feed)
- [](https://www.glassdoor.com/Overview/Working-at-Databricks-EI_IE954734.11,21.htm)
- [](https://www.youtube.com/@Databricks)
- [](https://www.linkedin.com/company/databricks)
- [](https://www.facebook.com/pages/Databricks/560203607379694)
- [](https://twitter.com/databricks)
- [](https://www.databricks.com/feed)
- [](https://www.glassdoor.com/Overview/Working-at-Databricks-EI_IE954734.11,21.htm)
- [](https://www.youtube.com/@Databricks)
© Databricks 2026. All rights reserved. Apache, Apache Spark, Spark, the Spark Logo, Apache Iceberg, Iceberg, and the Apache Iceberg logo are trademarks of the Apache Software Foundation.
We Care About Your Privacy
Databricks uses cookies and similar technologies to enhance site navigation, analyze site usage, personalize content and ads, and as further described in our Cookie Notice. To disable non-essential cookies, click “Reject All”. You can also manage your cookie settings by clicking “Manage Preferences.”
Manage Preferences
Reject All Accept All
Privacy Preference Center
Opt-Out Preference Signal Honored
Privacy Preference Center
- ### Your Privacy
- ### Strictly Necessary Cookies
- ### Performance Cookies
- ### Functional Cookies
- ### Targeting Cookies
- ### TOTHR
#### Your Privacy
When you visit any website, it may store or retrieve information on your browser, mostly in the form of cookies. This information might be about you, your preferences or your device and is mostly used to make the site work as you expect it to. The information does not usually directly identify you, but it can give you a more personalized web experience. Because we respect your right to privacy, you can choose not to allow some types of cookies. Click on the different category headings to find out more and change our default settings. However, blocking some types of cookies may impact your experience of the site and the services we are able to offer.
#### Opting out of sales, sharing, and targeted advertising
Depending on your location, you may have the right to opt out of the “sale” or “sharing” of your personal information or the processing of your personal information for purposes of online “targeted advertising.” You can opt out based on cookies and similar identifiers by disabling optional cookies here. To opt out based on other identifiers (such as your email address), submit a request in our Privacy Request Center.
#### Strictly Necessary Cookies
Always Active
These cookies are necessary for the website to function and cannot be switched off in our systems. They assist with essential site functionality such as setting your privacy preferences, logging in or filling in forms. You can set your browser to block or alert you about these cookies, but some parts of the site will no longer work.
#### Performance Cookies
- [x] Performance Cookies
These cookies allow us to count visits and traffic sources so we can measure and improve the performance of our site. They help us to know which pages are the most and least popular and see how visitors move around the site.
#### Functional Cookies
- [x] Functional Cookies
These cookies enable the website to provide enhanced functionality and personalization. They may be set by us or by third party providers whose services we have added to our pages. If you do not allow these cookies then some or all of these services may not function properly.
#### Targeting Cookies
- [x] Targeting Cookies
These cookies may be set through our site by our advertising partners. They may be used by those companies to build a profile of your interests and show you relevant advertisements on other sites. If you do not allow these cookies, you will experience less targeted advertising.
#### TOTHR
- [x] TOTHR
Cookie List
Consent Leg.Interest
- [x] checkbox label label
- [x] checkbox label label
- [x] checkbox label label
Clear
- - [x] checkbox label label
Apply Cancel
Confirm My Choices
Allow All
