Services

Resources

Company

Our Work

Blog

Check out our best work.

A funnel icon represents the limitations of VMs. They are not as scalable or agile as containerized applications. Scaling a VM requires provisioning a whole new server, while scaling a containerized application simply involves spinning up more containers.

How we reduced Kafka message latency by 90% and increased throughput 4x for a $300M SaaS platform.

We work with a large-scale enterprise in the digital communications governance domain as a SaaS provider (~$300M ARR). They use Apache Kafka to power their event-driven architecture. One of their products was being rolled out to many new tenants (increasing the traffic by 2x). During this roll-out phase, their Kafka-based async data pipeline experienced significant performance degradation. This caused cascading ripple effects throughout the other system, making the product unusable. We investigated and fixed this problem and eventually enabled the product to support 4 times the anticipated scale.

90%

Latency Improvement

4x

Throughput improvements

40%

Cost reduction

A funnel icon represents the limitations of VMs. They are not as scalable or agile as containerized applications. Scaling a VM requires provisioning a whole new server, while scaling a containerized application simply involves spinning up more containers.

How we reduced Kafka message latency by 90% and increased throughput 4x for a $300M SaaS platform.

We work with a large-scale enterprise in the digital communications governance domain as a SaaS provider (~$300M ARR). They use Apache Kafka to power their event-driven architecture. One of their products was being rolled out to many new tenants (increasing the traffic by 2x). During this roll-out phase, their Kafka-based async data pipeline experienced significant performance degradation. This caused cascading ripple effects throughout the other system, making the product unusable. We investigated and fixed this problem and eventually enabled the product to support 4 times the anticipated scale.

90%

Latency Improvement

4x

Throughput improvements

40%

Cost reduction

A funnel icon represents the limitations of VMs. They are not as scalable or agile as containerized applications. Scaling a VM requires provisioning a whole new server, while scaling a containerized application simply involves spinning up more containers.

How we reduced Kafka message latency by 90% and increased throughput 4x for a $300M SaaS platform.

We work with a large-scale enterprise in the digital communications governance domain as a SaaS provider (~$300M ARR). They use Apache Kafka to power their event-driven architecture. One of their products was being rolled out to many new tenants (increasing the traffic by 2x). During this roll-out phase, their Kafka-based async data pipeline experienced significant performance degradation. This caused cascading ripple effects throughout the other system, making the product unusable. We investigated and fixed this problem and eventually enabled the product to support 4 times the anticipated scale.

90%

Latency Improvement

4x

Throughput improvements

40%

Cost reduction

A funnel icon represents the limitations of VMs. They are not as scalable or agile as containerized applications. Scaling a VM requires provisioning a whole new server, while scaling a containerized application simply involves spinning up more containers.

How we reduced Kafka message latency by 90% and increased throughput 4x for a $300M SaaS platform.

We work with a large-scale enterprise in the digital communications governance domain as a SaaS provider (~$300M ARR). They use Apache Kafka to power their event-driven architecture. One of their products was being rolled out to many new tenants (increasing the traffic by 2x). During this roll-out phase, their Kafka-based async data pipeline experienced significant performance degradation. This caused cascading ripple effects throughout the other system, making the product unusable. We investigated and fixed this problem and eventually enabled the product to support 4 times the anticipated scale.

90%

Latency Improvement

4x

Throughput improvements

40%

Cost reduction

A funnel icon represents the limitations of VMs. They are not as scalable or agile as containerized applications. Scaling a VM requires provisioning a whole new server, while scaling a containerized application simply involves spinning up more containers.

How we reduced Kafka message latency by 90% and increased throughput 4x for a $300M SaaS platform.

We work with a large-scale enterprise in the digital communications governance domain as a SaaS provider (~$300M ARR). They use Apache Kafka to power their event-driven architecture. One of their products was being rolled out to many new tenants (increasing the traffic by 2x). During this roll-out phase, their Kafka-based async data pipeline experienced significant performance degradation. This caused cascading ripple effects throughout the other system, making the product unusable. We investigated and fixed this problem and eventually enabled the product to support 4 times the anticipated scale.

90%

Latency Improvement

4x

Throughput improvements

40%

Cost reduction

A funnel icon represents the limitations of VMs. They are not as scalable or agile as containerized applications. Scaling a VM requires provisioning a whole new server, while scaling a containerized application simply involves spinning up more containers.

Journey of moving VM-based workloads to Kubernetes

Flip is a fintech company in Indonesia. We migrated their stateless services running on 25+ VMs in the Alibaba cloud to their managed Kubernetes offering (ACK). These application services served Flip's B2B and B2C traffic which often peaked at 25,000 requests/second. Problem Statement. Implement autoscaling of the infrastructure to handle peak traffic. Move to a Cloud-Native architecture to use modern tooling. Solve the problems related to configuration management and operational toil.

30%

Cost Saved

120

Pods at peak load

0sec

Downtime

A funnel icon represents the limitations of VMs. They are not as scalable or agile as containerized applications. Scaling a VM requires provisioning a whole new server, while scaling a containerized application simply involves spinning up more containers.

Journey of moving VM-based workloads to Kubernetes

Flip is a fintech company in Indonesia. We migrated their stateless services running on 25+ VMs in the Alibaba cloud to their managed Kubernetes offering (ACK). These application services served Flip's B2B and B2C traffic which often peaked at 25,000 requests/second. Problem Statement. Implement autoscaling of the infrastructure to handle peak traffic. Move to a Cloud-Native architecture to use modern tooling. Solve the problems related to configuration management and operational toil.

30%

Cost Saved

120

Pods at peak load

0sec

Downtime

A funnel icon represents the limitations of VMs. They are not as scalable or agile as containerized applications. Scaling a VM requires provisioning a whole new server, while scaling a containerized application simply involves spinning up more containers.

Journey of moving VM-based workloads to Kubernetes

Flip is a fintech company in Indonesia. We migrated their stateless services running on 25+ VMs in the Alibaba cloud to their managed Kubernetes offering (ACK). These application services served Flip's B2B and B2C traffic which often peaked at 25,000 requests/second. Problem Statement. Implement autoscaling of the infrastructure to handle peak traffic. Move to a Cloud-Native architecture to use modern tooling. Solve the problems related to configuration management and operational toil.

30%

Cost Saved

120

Pods at peak load

0sec

Downtime

A funnel icon represents the limitations of VMs. They are not as scalable or agile as containerized applications. Scaling a VM requires provisioning a whole new server, while scaling a containerized application simply involves spinning up more containers.

Journey of moving VM-based workloads to Kubernetes

Flip is a fintech company in Indonesia. We migrated their stateless services running on 25+ VMs in the Alibaba cloud to their managed Kubernetes offering (ACK). These application services served Flip's B2B and B2C traffic which often peaked at 25,000 requests/second. Problem Statement. Implement autoscaling of the infrastructure to handle peak traffic. Move to a Cloud-Native architecture to use modern tooling. Solve the problems related to configuration management and operational toil.

30%

Cost Saved

120

Pods at peak load

0sec

Downtime

A funnel icon represents the limitations of VMs. They are not as scalable or agile as containerized applications. Scaling a VM requires provisioning a whole new server, while scaling a containerized application simply involves spinning up more containers.

Journey of moving VM-based workloads to Kubernetes

Flip is a fintech company in Indonesia. We migrated their stateless services running on 25+ VMs in the Alibaba cloud to their managed Kubernetes offering (ACK). These application services served Flip's B2B and B2C traffic which often peaked at 25,000 requests/second. Problem Statement. Implement autoscaling of the infrastructure to handle peak traffic. Move to a Cloud-Native architecture to use modern tooling. Solve the problems related to configuration management and operational toil.

30%

Cost Saved

120

Pods at peak load

0sec

Downtime

How we saved more than 42% on AWS Infrastructure Costs

The client a no-code platform to build mobile apps for a Shopify store. The platform is hosted on AWS and serves customers across the globe. Operational for 7+ years and have a 30+ member dev team with not much DevOps expertise in the team. Problem Statement. Reduce existing cloud infrastructure cost of $25k/month without affecting the business metrics and uptime. Implement cloud governance and infrastructure access policies for security. Continuous billing monitoring, alerts and deeper insights into cost allocations..

42%

Cost reduction for AWS Compute service

56%

Cost reduction for AWS RDS

90%

Cost reduction for AWS Data transfer

How we saved more than 42% on AWS Infrastructure Costs

The client a no-code platform to build mobile apps for a Shopify store. The platform is hosted on AWS and serves customers across the globe. Operational for 7+ years and have a 30+ member dev team with not much DevOps expertise in the team. Problem Statement. Reduce existing cloud infrastructure cost of $25k/month without affecting the business metrics and uptime. Implement cloud governance and infrastructure access policies for security. Continuous billing monitoring, alerts and deeper insights into cost allocations..

42%

Cost reduction for AWS Compute service

56%

Cost reduction for AWS RDS

90%

Cost reduction for AWS Data transfer

How we saved more than 42% on AWS Infrastructure Costs

The client a no-code platform to build mobile apps for a Shopify store. The platform is hosted on AWS and serves customers across the globe. Operational for 7+ years and have a 30+ member dev team with not much DevOps expertise in the team. Problem Statement. Reduce existing cloud infrastructure cost of $25k/month without affecting the business metrics and uptime. Implement cloud governance and infrastructure access policies for security. Continuous billing monitoring, alerts and deeper insights into cost allocations..

42%

Cost reduction for AWS Compute service

56%

Cost reduction for AWS RDS

90%

Cost reduction for AWS Data transfer

How we saved more than 42% on AWS Infrastructure Costs

The client a no-code platform to build mobile apps for a Shopify store. The platform is hosted on AWS and serves customers across the globe. Operational for 7+ years and have a 30+ member dev team with not much DevOps expertise in the team. Problem Statement. Reduce existing cloud infrastructure cost of $25k/month without affecting the business metrics and uptime. Implement cloud governance and infrastructure access policies for security. Continuous billing monitoring, alerts and deeper insights into cost allocations..

42%

Cost reduction for AWS Compute service

56%

Cost reduction for AWS RDS

90%

Cost reduction for AWS Data transfer

How we saved more than 42% on AWS Infrastructure Costs

The client a no-code platform to build mobile apps for a Shopify store. The platform is hosted on AWS and serves customers across the globe. Operational for 7+ years and have a 30+ member dev team with not much DevOps expertise in the team. Problem Statement. Reduce existing cloud infrastructure cost of $25k/month without affecting the business metrics and uptime. Implement cloud governance and infrastructure access policies for security. Continuous billing monitoring, alerts and deeper insights into cost allocations..

42%

Cost reduction for AWS Compute service

56%

Cost reduction for AWS RDS

90%

Cost reduction for AWS Data transfer

Migrating from Heroku PaaS to AWS

When ADPList approached us, they had an immediate problem at hand. The latency for the business critical APIs was high and caused poor user experience. Before anything else, we started focusing on solving that problem. The entire stack for ADPList was running on Heroku. To improve performance, the ADPList team already implemented vertical infrastructure scaling. However, this resulted in a massive increase in costs. Apart from the costs, we realized that Heroku isn’t particularly suited well for microservices deployments at ADPList due to:

300%

Application performance improvement

50%

Cost reduction

3x

Throughput improvement

Migrating from Heroku PaaS to AWS

When ADPList approached us, they had an immediate problem at hand. The latency for the business critical APIs was high and caused poor user experience. Before anything else, we started focusing on solving that problem. The entire stack for ADPList was running on Heroku. To improve performance, the ADPList team already implemented vertical infrastructure scaling. However, this resulted in a massive increase in costs. Apart from the costs, we realized that Heroku isn’t particularly suited well for microservices deployments at ADPList due to:

300%

Application performance improvement

50%

Cost reduction

3x

Throughput improvement

Migrating from Heroku PaaS to AWS

When ADPList approached us, they had an immediate problem at hand. The latency for the business critical APIs was high and caused poor user experience. Before anything else, we started focusing on solving that problem. The entire stack for ADPList was running on Heroku. To improve performance, the ADPList team already implemented vertical infrastructure scaling. However, this resulted in a massive increase in costs. Apart from the costs, we realized that Heroku isn’t particularly suited well for microservices deployments at ADPList due to:

300%

Application performance improvement

50%

Cost reduction

3x

Throughput improvement

Migrating from Heroku PaaS to AWS

When ADPList approached us, they had an immediate problem at hand. The latency for the business critical APIs was high and caused poor user experience. Before anything else, we started focusing on solving that problem. The entire stack for ADPList was running on Heroku. To improve performance, the ADPList team already implemented vertical infrastructure scaling. However, this resulted in a massive increase in costs. Apart from the costs, we realized that Heroku isn’t particularly suited well for microservices deployments at ADPList due to:

300%

Application performance improvement

50%

Cost reduction

3x

Throughput improvement

Migrating from Heroku PaaS to AWS

When ADPList approached us, they had an immediate problem at hand. The latency for the business critical APIs was high and caused poor user experience. Before anything else, we started focusing on solving that problem. The entire stack for ADPList was running on Heroku. To improve performance, the ADPList team already implemented vertical infrastructure scaling. However, this resulted in a massive increase in costs. Apart from the costs, we realized that Heroku isn’t particularly suited well for microservices deployments at ADPList due to:

300%

Application performance improvement

50%

Cost reduction

3x

Throughput improvement

AutoScaling eKYC Machine Learning workloads to 2M+ req/day

Cloud monthly costs were reduced to a maximum of $10k per month (90% savings over predicted costs without the Autoscaler). The solution has been live and running successfully on production for 2+ years. SLA for uptime and response time are met during this period.

AutoScaling eKYC Machine Learning workloads to 2M+ req/day

Cloud monthly costs were reduced to a maximum of $10k per month (90% savings over predicted costs without the Autoscaler). The solution has been live and running successfully on production for 2+ years. SLA for uptime and response time are met during this period.

AutoScaling eKYC Machine Learning workloads to 2M+ req/day

Cloud monthly costs were reduced to a maximum of $10k per month (90% savings over predicted costs without the Autoscaler). The solution has been live and running successfully on production for 2+ years. SLA for uptime and response time are met during this period.

AutoScaling eKYC Machine Learning workloads to 2M+ req/day

Cloud monthly costs were reduced to a maximum of $10k per month (90% savings over predicted costs without the Autoscaler). The solution has been live and running successfully on production for 2+ years. SLA for uptime and response time are met during this period.

Pull Request Based Ephemeral Preview Environments on Kubernetes

Problem Statement. Set up Heroku PaaS style, ephemeral Preview environments for all frontend and backend services on Kubernetes Allow a Preview service to use another service in Preview mode as a dependency Configure the latest migrations and seed data in Preview environments, reducing the time to test a Pull Request Allow automated deletion of the Preview environment if it’s not actively used

Pull Request Based Ephemeral Preview Environments on Kubernetes

Problem Statement. Set up Heroku PaaS style, ephemeral Preview environments for all frontend and backend services on Kubernetes Allow a Preview service to use another service in Preview mode as a dependency Configure the latest migrations and seed data in Preview environments, reducing the time to test a Pull Request Allow automated deletion of the Preview environment if it’s not actively used

Pull Request Based Ephemeral Preview Environments on Kubernetes

Problem Statement. Set up Heroku PaaS style, ephemeral Preview environments for all frontend and backend services on Kubernetes Allow a Preview service to use another service in Preview mode as a dependency Configure the latest migrations and seed data in Preview environments, reducing the time to test a Pull Request Allow automated deletion of the Preview environment if it’s not actively used

Pull Request Based Ephemeral Preview Environments on Kubernetes

Problem Statement. Set up Heroku PaaS style, ephemeral Preview environments for all frontend and backend services on Kubernetes Allow a Preview service to use another service in Preview mode as a dependency Configure the latest migrations and seed data in Preview environments, reducing the time to test a Pull Request Allow automated deletion of the Preview environment if it’s not actively used

Backup and recovery solution for SIEM data at Terabyte scale

Problem Statement. Build an automated backup and recovery process for the SIEM data Provide administrative controls for customizing the backup and restore options Implement tenant-wise customizations for data retention, data locality, and other parameters

1.5TB

Dataset handeled daily

100+

tenents using backup restore solution

$200

Running cost per month

Backup and recovery solution for SIEM data at Terabyte scale

Problem Statement. Build an automated backup and recovery process for the SIEM data Provide administrative controls for customizing the backup and restore options Implement tenant-wise customizations for data retention, data locality, and other parameters

1.5TB

Dataset handeled daily

100+

tenents using backup restore solution

$200

Running cost per month

Backup and recovery solution for SIEM data at Terabyte scale

Problem Statement. Build an automated backup and recovery process for the SIEM data Provide administrative controls for customizing the backup and restore options Implement tenant-wise customizations for data retention, data locality, and other parameters

1.5TB

Dataset handeled daily

100+

tenents using backup restore solution

$200

Running cost per month

Backup and recovery solution for SIEM data at Terabyte scale

Problem Statement. Build an automated backup and recovery process for the SIEM data Provide administrative controls for customizing the backup and restore options Implement tenant-wise customizations for data retention, data locality, and other parameters

1.5TB

Dataset handeled daily

100+

tenents using backup restore solution

$200

Running cost per month

Backup and recovery solution for SIEM data at Terabyte scale

Problem Statement. Build an automated backup and recovery process for the SIEM data Provide administrative controls for customizing the backup and restore options Implement tenant-wise customizations for data retention, data locality, and other parameters

1.5TB

Dataset handeled daily

100+

tenents using backup restore solution

$200

Running cost per month

Homogeneous Monitoring across Cloud and On-premise

Monitor CPU, Memory, and Disk Utilization of the host systems and application containers. We needed a uniform solution to fetch metrics across AWS, GCP and on-premise data centers. All metrics should be stored and served from a single storage backend for uniformity, visualization and alerting.

Homogeneous Monitoring across Cloud and On-premise

Monitor CPU, Memory, and Disk Utilization of the host systems and application containers. We needed a uniform solution to fetch metrics across AWS, GCP and on-premise data centers. All metrics should be stored and served from a single storage backend for uniformity, visualization and alerting.

Homogeneous Monitoring across Cloud and On-premise

Monitor CPU, Memory, and Disk Utilization of the host systems and application containers. We needed a uniform solution to fetch metrics across AWS, GCP and on-premise data centers. All metrics should be stored and served from a single storage backend for uniformity, visualization and alerting.

Homogeneous Monitoring across Cloud and On-premise

Monitor CPU, Memory, and Disk Utilization of the host systems and application containers. We needed a uniform solution to fetch metrics across AWS, GCP and on-premise data centers. All metrics should be stored and served from a single storage backend for uniformity, visualization and alerting.

Migrating 1.3TB of Primary Data from Self-Hosted MySQL to GCP CloudSQL

Problem Statement. Build an automated backup and recovery process for the SIEM data Provide administrative controls for customizing the backup and restore options Implement tenant-wise customizations for data retention, data locality, and other parameters

Migrating 1.3TB of Primary Data from Self-Hosted MySQL to GCP CloudSQL

Problem Statement. Build an automated backup and recovery process for the SIEM data Provide administrative controls for customizing the backup and restore options Implement tenant-wise customizations for data retention, data locality, and other parameters

Migrating 1.3TB of Primary Data from Self-Hosted MySQL to GCP CloudSQL

Problem Statement. Build an automated backup and recovery process for the SIEM data Provide administrative controls for customizing the backup and restore options Implement tenant-wise customizations for data retention, data locality, and other parameters

Migrating 1.3TB of Primary Data from Self-Hosted MySQL to GCP CloudSQL

Problem Statement. Build an automated backup and recovery process for the SIEM data Provide administrative controls for customizing the backup and restore options Implement tenant-wise customizations for data retention, data locality, and other parameters

Zero downtime MySQL schema migrations for 400M row table

Problem Statement. DB schema migrations on large tables (400+ million rows or 150+ GB in size for a single table) caused replication lag and impacted latencies. Adding indexes on large tables also resulted in replication lag and degraded query performance. Developers had to wait months to roll out their features which needed schema changes.

400 M

Biggest table migrated

7

migrations per month

0

Downtime due to schema migrations

Zero downtime MySQL schema migrations for 400M row table

Problem Statement. DB schema migrations on large tables (400+ million rows or 150+ GB in size for a single table) caused replication lag and impacted latencies. Adding indexes on large tables also resulted in replication lag and degraded query performance. Developers had to wait months to roll out their features which needed schema changes.

400 M

Biggest table migrated

7

migrations per month

0

Downtime due to schema migrations

Zero downtime MySQL schema migrations for 400M row table

Problem Statement. DB schema migrations on large tables (400+ million rows or 150+ GB in size for a single table) caused replication lag and impacted latencies. Adding indexes on large tables also resulted in replication lag and degraded query performance. Developers had to wait months to roll out their features which needed schema changes.

400 M

Biggest table migrated

7

migrations per month

0

Downtime due to schema migrations

Zero downtime MySQL schema migrations for 400M row table

Problem Statement. DB schema migrations on large tables (400+ million rows or 150+ GB in size for a single table) caused replication lag and impacted latencies. Adding indexes on large tables also resulted in replication lag and degraded query performance. Developers had to wait months to roll out their features which needed schema changes.

400 M

Biggest table migrated

7

migrations per month

0

Downtime due to schema migrations

Zero downtime MySQL schema migrations for 400M row table

Problem Statement. DB schema migrations on large tables (400+ million rows or 150+ GB in size for a single table) caused replication lag and impacted latencies. Adding indexes on large tables also resulted in replication lag and degraded query performance. Developers had to wait months to roll out their features which needed schema changes.

400 M

Biggest table migrated

7

migrations per month

0

Downtime due to schema migrations

Migrating Monolith Application to Cloud Native Stack

The client provides cloud and on-premise based Hospital Management Services (HMS) SaaS platform. This HMS serves 800+ B2B customers (tenants), including large hospital chains across the Middle East, Africa, and APAC region. The tech stack for the application is a Java-based backend, JSP-based server rendered UI, Redis, and MySQL as datastores. The HMS platform is deployed as a SaaS and in customers' cloud environments and on-premise data centers.

Migrating Monolith Application to Cloud Native Stack

The client provides cloud and on-premise based Hospital Management Services (HMS) SaaS platform. This HMS serves 800+ B2B customers (tenants), including large hospital chains across the Middle East, Africa, and APAC region. The tech stack for the application is a Java-based backend, JSP-based server rendered UI, Redis, and MySQL as datastores. The HMS platform is deployed as a SaaS and in customers' cloud environments and on-premise data centers.

Our blogs.

Debugging KubeVPN's DNS resolution failures

Nilesh D

Software Engineer @One2N

The other day, I was trying to access the Go documentation at go.dev, but the page wouldn't load. Not on Chrome, not in incognito mode, not even on Safari. The error was simple but frustrating: "Could not resolve hostname".

September 30, 2025 | 3 min read

What happens when you run Terraform commands?

Sahil Joseph

SRE @One2N

Saurabh Hirani

Principal SRE @One2N

Have you ever wondered what happens when you run Terraform commands? In this post, we will explore the following topics to gain a better understanding of Terraform internals: Architecture and provider plugin architecture, Credential management, State locking and Debugging

September 24, 2025 | 7 min read

Daily driving Omarchy and Hyprland as a CTO

Jaideep Khandelwal

Founder, CTO @One2N

A hands-on review of Omarchy Linux, an easy-to-install Arch-based Linux distro with Hyprland window manager pre-configured out of the box. Learn what works, what breaks, and who should use it.

September 16, 2025 | 5 min read

DORA Metrics: Useful or Fluff

Jaideep Khandelwal

Founder, CTO @One2N

In this post, we shed light on why following DORA metrics without context might cause more harm than good. We also show how leaders can use a practical, strategic approach to connect DORA metrics to meaningful business outcomes.

September 3, 2025 | 14 min read

A pragmatic guide to get started with OpenTelemetry

Spandan Ghosh

Content @One2N

A guide explaining OTEL, monitoring vs. observability, telemetry’s pillars, OTEL instrumentation, and seamless backend migration. Includes practical migration strategies and trade-offs for SREs adopting OTEL.

August 20, 2025 | 5 min read

How we solved a critical site-to-site VPN IP address conflict in AWS

Rishiraj Rathore

SRE @One2N

Spandan Ghosh

Content @One2N

How One2N solved a critical site-to-site VPN IP address conflict in AWS, including details on the problem, failed solutions, and the final parallel system approach that worked with automation.

July 15, 2025 | 7 min read

Blog

Recent talks

Extreme Programming in the Age of AI | One2N Cursor Meetup Pune

Nilesh D

Software Engineer @One2N

Tune in to this session from the first official Cursor Meetup in Pune, hosted at One2N, where Nilesh D, Software Engineer at One2N explores how Extreme Programming principles can level up your dev workflows and integrate with AI tools like Cursor.

September 27, 2025 | 42 Minutes

Extreme Programming in the Age of AI | One2N Cursor Meetup Pune

Nilesh D

Software Engineer @One2N

Tune in to this session from the first official Cursor Meetup in Pune, hosted at One2N, where Nilesh D, Software Engineer at One2N explores how Extreme Programming principles can level up your dev workflows and integrate with AI tools like Cursor.

September 27, 2025 | 42 Minutes

Extreme Programming in the Age of AI | One2N Cursor Meetup Pune

Nilesh D

Software Engineer @One2N

Tune in to this session from the first official Cursor Meetup in Pune, hosted at One2N, where Nilesh D, Software Engineer at One2N explores how Extreme Programming principles can level up your dev workflows and integrate with AI tools like Cursor.

September 27, 2025 | 42 Minutes

Extreme Programming in the Age of AI | One2N Cursor Meetup Pune

Nilesh D

Software Engineer @One2N

Tune in to this session from the first official Cursor Meetup in Pune, hosted at One2N, where Nilesh D, Software Engineer at One2N explores how Extreme Programming principles can level up your dev workflows and integrate with AI tools like Cursor.

September 27, 2025 | 42 Minutes

Extreme Programming in the Age of AI | One2N Cursor Meetup Pune

Nilesh D

Software Engineer @One2N

Tune in to this session from the first official Cursor Meetup in Pune, hosted at One2N, where Nilesh D, Software Engineer at One2N explores how Extreme Programming principles can level up your dev workflows and integrate with AI tools like Cursor.

September 27, 2025 | 42 Minutes

AI Enabled SRE Practices | One2N Cursor Meetup Pune

Saurabh Hirani

Principal SRE @One2N

Tune in to this session from the first official Cursor Meetup in Pune, hosted at One2N, this lightning talk is by Saurabh Hirani on what are AI enabled SRE (Site Reliability Engineering) practices.

September 27, 2025 | 18 Minutes

AI Enabled SRE Practices | One2N Cursor Meetup Pune

Saurabh Hirani

Principal SRE @One2N

Tune in to this session from the first official Cursor Meetup in Pune, hosted at One2N, this lightning talk is by Saurabh Hirani on what are AI enabled SRE (Site Reliability Engineering) practices.

September 27, 2025 | 18 Minutes

AI Enabled SRE Practices | One2N Cursor Meetup Pune

Saurabh Hirani

Principal SRE @One2N

Tune in to this session from the first official Cursor Meetup in Pune, hosted at One2N, this lightning talk is by Saurabh Hirani on what are AI enabled SRE (Site Reliability Engineering) practices.

September 27, 2025 | 18 Minutes

AI Enabled SRE Practices | One2N Cursor Meetup Pune

Saurabh Hirani

Principal SRE @One2N

Tune in to this session from the first official Cursor Meetup in Pune, hosted at One2N, this lightning talk is by Saurabh Hirani on what are AI enabled SRE (Site Reliability Engineering) practices.

September 27, 2025 | 18 Minutes

AI Enabled SRE Practices | One2N Cursor Meetup Pune

Saurabh Hirani

Principal SRE @One2N

Tune in to this session from the first official Cursor Meetup in Pune, hosted at One2N, this lightning talk is by Saurabh Hirani on what are AI enabled SRE (Site Reliability Engineering) practices.

September 27, 2025 | 18 Minutes

UNIX Philosophy for Modern Engineers | Command Line Heroes

Chinmay Naik

Founder, CEO @One2N

Command Line Heroes is a new meetup series for developers, SREs, and terminal enthusiasts who love working with the command line. ​Our episode 1 kicks off with real-world stories, and hands-on challenges along with a geek out session that let's your show off your terminal setup, or learn from ones who do!

August 23, 2025 | 21 Minutes

UNIX Philosophy for Modern Engineers | Command Line Heroes

Chinmay Naik

Founder, CEO @One2N

Command Line Heroes is a new meetup series for developers, SREs, and terminal enthusiasts who love working with the command line. ​Our episode 1 kicks off with real-world stories, and hands-on challenges along with a geek out session that let's your show off your terminal setup, or learn from ones who do!

August 23, 2025 | 21 Minutes

UNIX Philosophy for Modern Engineers | Command Line Heroes

Chinmay Naik

Founder, CEO @One2N

Command Line Heroes is a new meetup series for developers, SREs, and terminal enthusiasts who love working with the command line. ​Our episode 1 kicks off with real-world stories, and hands-on challenges along with a geek out session that let's your show off your terminal setup, or learn from ones who do!

August 23, 2025 | 21 Minutes

UNIX Philosophy for Modern Engineers | Command Line Heroes

Chinmay Naik

Founder, CEO @One2N

Command Line Heroes is a new meetup series for developers, SREs, and terminal enthusiasts who love working with the command line. ​Our episode 1 kicks off with real-world stories, and hands-on challenges along with a geek out session that let's your show off your terminal setup, or learn from ones who do!

August 23, 2025 | 21 Minutes

UNIX Philosophy for Modern Engineers | Command Line Heroes

Chinmay Naik

Founder, CEO @One2N

Command Line Heroes is a new meetup series for developers, SREs, and terminal enthusiasts who love working with the command line. ​Our episode 1 kicks off with real-world stories, and hands-on challenges along with a geek out session that let's your show off your terminal setup, or learn from ones who do!

August 23, 2025 | 21 Minutes

DORA Metrics or Theatrics? What to Measure, What to Ignore, and How to Improve | One2N Bits Ep. 2

Jaideep Khandelwal

Founder, CTO @One2N

Chinmay Naik

Founder, CEO @One2N

Chinmay and Jaideep talk about DORA metrics: what they are, when they matter, and how to use them without turning it into measurement theater. From deployment frequency to time to recover, they cover how these metrics reflect engineering culture, platform maturity, and real business impact.

August 21, 2025 | 15 Minutes

DORA Metrics or Theatrics? What to Measure, What to Ignore, and How to Improve | One2N Bits Ep. 2

Jaideep Khandelwal

Founder, CTO @One2N

Chinmay Naik

Founder, CEO @One2N

Chinmay and Jaideep talk about DORA metrics: what they are, when they matter, and how to use them without turning it into measurement theater. From deployment frequency to time to recover, they cover how these metrics reflect engineering culture, platform maturity, and real business impact.

August 21, 2025 | 15 Minutes

DORA Metrics or Theatrics? What to Measure, What to Ignore, and How to Improve | One2N Bits Ep. 2

Jaideep Khandelwal

Founder, CTO @One2N

Chinmay Naik

Founder, CEO @One2N

Chinmay and Jaideep talk about DORA metrics: what they are, when they matter, and how to use them without turning it into measurement theater. From deployment frequency to time to recover, they cover how these metrics reflect engineering culture, platform maturity, and real business impact.

August 21, 2025 | 15 Minutes

DORA Metrics or Theatrics? What to Measure, What to Ignore, and How to Improve | One2N Bits Ep. 2

Jaideep Khandelwal

Founder, CTO @One2N

Chinmay Naik

Founder, CEO @One2N

Chinmay and Jaideep talk about DORA metrics: what they are, when they matter, and how to use them without turning it into measurement theater. From deployment frequency to time to recover, they cover how these metrics reflect engineering culture, platform maturity, and real business impact.

August 21, 2025 | 15 Minutes

DORA Metrics or Theatrics? What to Measure, What to Ignore, and How to Improve | One2N Bits Ep. 2

Jaideep Khandelwal

Founder, CTO @One2N

Chinmay Naik

Founder, CEO @One2N

Chinmay and Jaideep talk about DORA metrics: what they are, when they matter, and how to use them without turning it into measurement theater. From deployment frequency to time to recover, they cover how these metrics reflect engineering culture, platform maturity, and real business impact.

August 21, 2025 | 15 Minutes

AWS Billing Nightmares: Hidden Costs That Can Burn Your Budget | One2N Bits Ep. 1

Pranay Narang

SRE @One2N

Chinmay Naik

Founder, CEO @One2N

Dive into the world of cloud cost horrors with Pranay and Chinmay in the premiere episode of One2N Bits! In this quick 5-minute chat, we uncover real-world AWS billing scares that catch even seasoned teams off guard from massive EBS volume costs on unused disks to sneaky networking fees and the sudden spike from public IP billing.

July 28, 2025 | 5 Minutes

AWS Billing Nightmares: Hidden Costs That Can Burn Your Budget | One2N Bits Ep. 1

Pranay Narang

SRE @One2N

Chinmay Naik

Founder, CEO @One2N

Dive into the world of cloud cost horrors with Pranay and Chinmay in the premiere episode of One2N Bits! In this quick 5-minute chat, we uncover real-world AWS billing scares that catch even seasoned teams off guard from massive EBS volume costs on unused disks to sneaky networking fees and the sudden spike from public IP billing.

July 28, 2025 | 5 Minutes

AWS Billing Nightmares: Hidden Costs That Can Burn Your Budget | One2N Bits Ep. 1

Pranay Narang

SRE @One2N

Chinmay Naik

Founder, CEO @One2N

Dive into the world of cloud cost horrors with Pranay and Chinmay in the premiere episode of One2N Bits! In this quick 5-minute chat, we uncover real-world AWS billing scares that catch even seasoned teams off guard from massive EBS volume costs on unused disks to sneaky networking fees and the sudden spike from public IP billing.

July 28, 2025 | 5 Minutes

AWS Billing Nightmares: Hidden Costs That Can Burn Your Budget | One2N Bits Ep. 1

Pranay Narang

SRE @One2N

Chinmay Naik

Founder, CEO @One2N

Dive into the world of cloud cost horrors with Pranay and Chinmay in the premiere episode of One2N Bits! In this quick 5-minute chat, we uncover real-world AWS billing scares that catch even seasoned teams off guard from massive EBS volume costs on unused disks to sneaky networking fees and the sudden spike from public IP billing.

July 28, 2025 | 5 Minutes

AWS Billing Nightmares: Hidden Costs That Can Burn Your Budget | One2N Bits Ep. 1

Pranay Narang

SRE @One2N

Chinmay Naik

Founder, CEO @One2N

Dive into the world of cloud cost horrors with Pranay and Chinmay in the premiere episode of One2N Bits! In this quick 5-minute chat, we uncover real-world AWS billing scares that catch even seasoned teams off guard from massive EBS volume costs on unused disks to sneaky networking fees and the sudden spike from public IP billing.

July 28, 2025 | 5 Minutes

Optimizing Kafka Performance with Automated Benchmarks

Kalpesh Deo

Lead Software Engineer @One2N

Curious about getting the best performance out of your Kafka setup? In this talk, Kalpesh from One2N walks us through his experience of benchmarking Kafka producers using the kafka-producer-perf-test.sh tool. By experimenting with settings like compression.type, linger.ms, and batch.size, Kalpesh’s team managed to boost throughput by 245%, cut down latency by 90%, and save 89% on storage all by fine-tuning some key configurations. What you’ll learn: How tuning Kafka producer settings impacts real throughput, latency, and storage. The power of scripting to automate and simplify performance benchmarking. Techniques to interpret and apply benchmarking results for your own Kafka deployment. Kalpesh’s 16+ years in backend, cloud, and distributed engineering shine through as he delivers actionable insights for engineers seeking to fine-tune their data pipelines

June 7, 2025 | 27 Minutes

Optimizing Kafka Performance with Automated Benchmarks

Kalpesh Deo

Lead Software Engineer @One2N

Curious about getting the best performance out of your Kafka setup? In this talk, Kalpesh from One2N walks us through his experience of benchmarking Kafka producers using the kafka-producer-perf-test.sh tool. By experimenting with settings like compression.type, linger.ms, and batch.size, Kalpesh’s team managed to boost throughput by 245%, cut down latency by 90%, and save 89% on storage all by fine-tuning some key configurations. What you’ll learn: How tuning Kafka producer settings impacts real throughput, latency, and storage. The power of scripting to automate and simplify performance benchmarking. Techniques to interpret and apply benchmarking results for your own Kafka deployment. Kalpesh’s 16+ years in backend, cloud, and distributed engineering shine through as he delivers actionable insights for engineers seeking to fine-tune their data pipelines

June 7, 2025 | 27 Minutes

Optimizing Kafka Performance with Automated Benchmarks

Kalpesh Deo

Lead Software Engineer @One2N

Curious about getting the best performance out of your Kafka setup? In this talk, Kalpesh from One2N walks us through his experience of benchmarking Kafka producers using the kafka-producer-perf-test.sh tool. By experimenting with settings like compression.type, linger.ms, and batch.size, Kalpesh’s team managed to boost throughput by 245%, cut down latency by 90%, and save 89% on storage all by fine-tuning some key configurations. What you’ll learn: How tuning Kafka producer settings impacts real throughput, latency, and storage. The power of scripting to automate and simplify performance benchmarking. Techniques to interpret and apply benchmarking results for your own Kafka deployment. Kalpesh’s 16+ years in backend, cloud, and distributed engineering shine through as he delivers actionable insights for engineers seeking to fine-tune their data pipelines

June 7, 2025 | 27 Minutes

Optimizing Kafka Performance with Automated Benchmarks

Kalpesh Deo

Lead Software Engineer @One2N

Curious about getting the best performance out of your Kafka setup? In this talk, Kalpesh from One2N walks us through his experience of benchmarking Kafka producers using the kafka-producer-perf-test.sh tool. By experimenting with settings like compression.type, linger.ms, and batch.size, Kalpesh’s team managed to boost throughput by 245%, cut down latency by 90%, and save 89% on storage all by fine-tuning some key configurations. What you’ll learn: How tuning Kafka producer settings impacts real throughput, latency, and storage. The power of scripting to automate and simplify performance benchmarking. Techniques to interpret and apply benchmarking results for your own Kafka deployment. Kalpesh’s 16+ years in backend, cloud, and distributed engineering shine through as he delivers actionable insights for engineers seeking to fine-tune their data pipelines

June 7, 2025 | 27 Minutes

Optimizing Kafka Performance with Automated Benchmarks

Kalpesh Deo

Lead Software Engineer @One2N

Curious about getting the best performance out of your Kafka setup? In this talk, Kalpesh from One2N walks us through his experience of benchmarking Kafka producers using the kafka-producer-perf-test.sh tool. By experimenting with settings like compression.type, linger.ms, and batch.size, Kalpesh’s team managed to boost throughput by 245%, cut down latency by 90%, and save 89% on storage all by fine-tuning some key configurations. What you’ll learn: How tuning Kafka producer settings impacts real throughput, latency, and storage. The power of scripting to automate and simplify performance benchmarking. Techniques to interpret and apply benchmarking results for your own Kafka deployment. Kalpesh’s 16+ years in backend, cloud, and distributed engineering shine through as he delivers actionable insights for engineers seeking to fine-tune their data pipelines

June 7, 2025 | 27 Minutes

Talks

Subscribe for more such content

Get the latest in software engineering best practices straight to your inbox. Subscribe now!

Subscribe for more such content

Get the latest in software engineering best practices straight to your inbox. Subscribe now!

Subscribe for more such content

Get the latest in software engineering best practices straight to your inbox. Subscribe now!

Subscribe for more such content

Get the latest in software engineering best practices straight to your inbox. Subscribe now!

Subscribe for more such content

Get the latest in software engineering best practices straight to your inbox. Subscribe now!