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Case Study

Curve: A Successful Migration from Datadog to Coralogix


About Curve

Curve is a FinTech company that offers a payment card and application that allows users to link multiple payment cards and use them all through one single card. The application automatically selects the card that offers the best rewards or benefits for a particular purchase when users make a payment with the Curve card.

The Challenge: A Costly Observability Solution & Scaling Data Needs

Curve’s technology group consists of ten teams covering product development, data analysis, infrastructure management, and payment technology. With 600K monthly active users, each team heavily relies on observability data for rapid MTTD and MTTR to address any issues affecting Curve’s service availability or performance. 

However, as Curve experienced massive growth, their data volumes increased as well. This created a significant challenge when trying to scale up with their original observability solution, Datadog. With Datadog’s different pricing structures for different services, it was extremely difficult to predict what Curve’s spend would be like over the long-term. This created a culture of fear around using the platform with engineers hesitant to try new features as they might exceed budget limits. 

Furthermore, with their increased data volumes, Curve was constrained to two weeks’ worth of observability data due to Datadog’s high cost for retention. 

 “The simple way of looking at it, is that with Datadog we had 2 weeks’ worth of information but once we passed those 2 weeks, it was gone.”
-Amit Sule, Senior Engineering Manager

As a result, engineering teams were often unable to access older data, limiting the thoroughness of their analysis. With customers expecting 24/7 availability and an excellent user experience, these limitations created by Datadog’s high pricing resulted in a restricted capacity to handle issues in a proactive and timely manner.

With time this challenge became increasingly pressing for Curve and prompted them to seek a robust observability solution that provided built-in cost optimization for large scale volumes.

The Solution: A Cost-Efficient Observability Shift With Coralogix

When Curve encountered the challenge of scaling their observability solution while staying within their budget constraints, one of their investors suggested turning to Coralogix.

In contrast to other vendors like Datadog, Coralogix is specifically designed for cost-efficiency. Coralogix’s in-stream analysis and processing of data enables Curve to extract insights from data as soon as it is ingested, without the need for costly indexing or hot storage. With a rich variety of real-time alerts and custom dashboards Curve leverages this innovation to affordably gain insights into all their data. 

Coralogix’s unique, cost-conscious architecture, also empowers Curve to tailor their data processing and storage strategies to match their precise needs. Using Coralogix’s TCO (Total Cost Of Ownership) Optimizer, ingested data that demands lightning-fast querying around the clock can be routed to indexing and hot storage. Meanwhile, less time-sensitive data can find its home in archive, with Coralogix Remote Query ensuring swift querying without the overhead of indexing and storage. 

Curve started the migration to Coralogix with one of their teams that had the strongest  observability needs due to a high amount of microservices. Log ingestion was achieved within a day, with the remainder of the migration time spent manually replicating their dashboards from Datadog’s proprietary, closed platform. Curve successfully rolled out Coralogix to the 9 remaining teams within the following 2 months giving their 80+ engineers complete visibility into their entire tech stack. 

“The migration was a success because it was a joint activity, with such a close group communicating every day.”
-James Lawson, VP Engineering at Curve

The Benefits

Curve’s adoption of Coralogix as their observability solution brought a multitude of benefits that positively impacted their operations and efficiency:

  • Real-Time Archive Querying
    With Coralogix, Curve created an archive tailored to their specific needs which allows them to efficiently store and rapidly query historical data. It also ensures that they don’t have to reindex their archived data. This eliminated the latency typically created by adding a massive volume of reindexed data as well as the exorbitant retrieval costs that Datadog normally charged. Antonio Giuliana, Staff Engineer at Curve enthused that “with Coralogix we query from S3 with infinite retention so we can go back in time as much as we want.”
  • 40% Cost Savings
    Perhaps the most tangible and immediate benefit that Curve experienced was the significant cost savings. By optimizing their data processing and storage, Curve saved 40% on their observability when compared to Datadog, their previous observability solution.
  • Smooth Migration and Exceptional Support
    One of the standout features of the transition to Coralogix was the seamless migration process. Curve’s team highlighted the exceptional support and close communication they experienced during the transition. The Coralogix support team proved to be highly knowledgeable and available, with a 24/7 in-app chat service and under 1 minute response time.
  • Efficient Data Handling
    The introduction of Coralogix’s TCO Optimizer was a game-changer for Curve. This feature allowed them to manage their data in a highly efficient manner. They leveraged in-stream analysis, a critical component of the TCO Optimizer, to route compliance and other less critical data directly into an S3 bucket with only the most frequently searched data being indexed and sent to hot storage.


The adoption of Coralogix’s cost-efficient and highly responsive observability solution enabled Curve to not only address their scaling data needs but also achieve significant cost savings, reducing their spending by 40%. With a seamless migration process, exceptional support, in-stream analysis, and the power of the TCO Optimizer, Curve now possesses a robust observability platform that caters to their precise requirements.