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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.

Read Case Study

Migrating from AWS Glue to Self-Hosted ClickHouse: 70% Cost Reduction for a Fintech Client.

We recently partnered with a B2B fintech processing between $50M and $70M in transactions every single day. Their analytics platform was built on AWS managed services, including AWS DMS for CDC replication, AWS Glue for ETL, AWS Glue tables using S3 for storage, and Amazon Athena for querying. Initially, the platform worked fine, but as data volumes grew, monthly costs exceeded $13,000 and projected to reach $35,000 with a 3x growth. The analytics solution was not cost-effective. One2N helped migrate them to a self-hosted open-source stack, reducing costs by 70% while maintaining production-grade reliability.

70%

Cost reduction

$108K

Annual Savings

81%

Compression in DB size

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.

Read Case Study

Migrating from AWS Glue to Self-Hosted ClickHouse: 70% Cost Reduction for a Fintech Client.

We recently partnered with a B2B fintech processing between $50M and $70M in transactions every single day. Their analytics platform was built on AWS managed services, including AWS DMS for CDC replication, AWS Glue for ETL, AWS Glue tables using S3 for storage, and Amazon Athena for querying. Initially, the platform worked fine, but as data volumes grew, monthly costs exceeded $13,000 and projected to reach $35,000 with a 3x growth. The analytics solution was not cost-effective. One2N helped migrate them to a self-hosted open-source stack, reducing costs by 70% while maintaining production-grade reliability.

70%

Cost reduction

$108K

Annual Savings

81%

Compression in DB size

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.

Read Case Study

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.

Read Case Study

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.

Read Case Study

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.

Read Case Study

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

Read Case Study

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

Read Case Study

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

Read Case Study

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

Read Case Study

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

Read Case Study

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

Read Case Study

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

Read Case Study

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

Read Case Study

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

Read Case Study

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.

Read Case Study

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.

Read Case Study

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

Read Case Study

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

Read Case Study

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.

Read Case Study

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.

Read Case Study

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

Read Case Study

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

Read Case Study

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.

Read Case Study

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.

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.

Read Case Study

Migrating from AWS Glue to Self-Hosted ClickHouse: 70% Cost Reduction for a Fintech Client.

We recently partnered with a B2B fintech processing between $50M and $70M in transactions every single day. Their analytics platform was built on AWS managed services, including AWS DMS for CDC replication, AWS Glue for ETL, AWS Glue tables using S3 for storage, and Amazon Athena for querying. Initially, the platform worked fine, but as data volumes grew, monthly costs exceeded $13,000 and projected to reach $35,000 with a 3x growth. The analytics solution was not cost-effective. One2N helped migrate them to a self-hosted open-source stack, reducing costs by 70% while maintaining production-grade reliability.

70%

Cost reduction

$108K

Annual Savings

81%

Compression in DB size

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.

Read Case Study

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.

Read Case Study

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

Read Case Study

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

Read Case Study

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

Read Case Study

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

Read Case Study

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

Read Case Study

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.

Read Case Study

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

Read Case Study

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.

Read Case Study

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

Read Case Study

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.

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.

Read Case Study

Migrating from AWS Glue to Self-Hosted ClickHouse: 70% Cost Reduction for a Fintech Client.

We recently partnered with a B2B fintech processing between $50M and $70M in transactions every single day. Their analytics platform was built on AWS managed services, including AWS DMS for CDC replication, AWS Glue for ETL, AWS Glue tables using S3 for storage, and Amazon Athena for querying. Initially, the platform worked fine, but as data volumes grew, monthly costs exceeded $13,000 and projected to reach $35,000 with a 3x growth. The analytics solution was not cost-effective. One2N helped migrate them to a self-hosted open-source stack, reducing costs by 70% while maintaining production-grade reliability.

70%

Cost reduction

$108K

Annual Savings

81%

Compression in DB size

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.

Read Case Study

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

Read Case Study

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

Read Case Study

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

Read Case Study

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

Read Case Study

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

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.

Read Case Study

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

Read Case Study

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

Read Case Study

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

Read Case Study

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.

Read Case Study

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.

Read Case Study

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.

Blogs.

Read Blog

Gitops for Kafka in the real world: How we governed 78 clusters without breaking production

Harshwardhan Mehrotra

SRE @One2N

This post is about how we, as a lean platform engg team used gitops and jikkou to bring 78 kafka clusters under control, cut ticket based changes from days to minutes, and kept legacy kafka 0.8.2 clusters safe in production.

February 16, 2026 | 4 min read

Read Blog

Prayogshala - The Engineering Laboratory at One2N

Chinmay Naik

CEO @One2N

We created Prayogshala, One2N’s internal engineering lab, to capture our learnings, experiments and knowledge base. See how it helps engineers get better by asking "why" before "how".

February 2, 2026 | 4 min read

Read Blog

The Gotchas of OTEL collector processors for effective observability in K8s

Sanket Rajgiri

SRE @One2N

Spandan Ghosh

Content @One2N

Struggling to make sense of OpenTelemetry Collector processors for real-world projects? This blog breaks down what each OTEL processor actually does, where it matters, and shares real lessons from messy SRE problems like taming noisy data, surviving crashes, and staying under cost limits in Kubernetes.

January 26, 2026 | 6 min read

Read Blog

How Queueing Theory Makes Systems Reliable

One2N

Team

Learn how SREs use queueing theory to explain why 70 percent utilisation feels calm, 90 percent feels cursed, and how the same math helps you choose headroom, tame retries, and protect your error budget before incidents hit.

December 17, 2025 | 2 min read

Read Blog

How to read SRE graphs without lying to yourself

One2N

Team

Are your SRE charts messing with your head? We’ll show you step by step how to actually make sense of those dashboards: percentiles, averages, heatmaps, and all, so you spot real issues fast. No jargon, just practical advice from daily SRE work.

December 10, 2025 | 3 min read

Read Blog

Error Budget Calculation: Downtime Minutes for every SLO

One2N

Team

Turn your SLO into something you can argue about in a meeting: this guide shows how to convert 99.9% into 43 real minutes of downtime, read burn rate, push back on “five nines,” and decide when to ship or hit pause.

December 3, 2025 | 3 min read

Blogs

Recent talks.

Solving Synthetic Data generation using LLMs | mitramadal.ai EP2 - 2026

Chinmay Naik

CEO @One2N

In this talk at mitramandal.ai EP2, Chinmay Naik (Founder & CEO, One2N) walks through how One2N solved the problem of synthetic data generation using LLMs - covering the real-world use cases that drove it, why anonymization falls short, and how they built a tool that uses LLMs for schema generation and deterministic faker-based data generation.

February 21, 2026 | 36 Minutes

.zshrc tmuxinator tease | Command Line Heroes EP3 - Bengaluru

Saurabh Hirani

Principal SRE @One2N

Command Line Heroes is a meetup series for developers, SREs, and terminal nerds who love living inside the shell. ​In episode 3 from Bengaluru, Saurabh Hirani (Principal SRE at One2N) walks through a real-world debugging story where Kiro CLI quietly breaks his beloved Tmuxinator setup. ​ This talk is a deep dive into how a “simple” autocomplete feature ended up sitting between Ghostty/VS Code, tmux, and zsh, and why things started to go weird: tmux panes showed commands but refused to execute them, shell startup got slower, and mysterious processes began to appear. ​ Along the way, Saurabh uses LLMs, exec, and process inspection to peel back the layers and explain what’s really happening inside your terminal sessions. CLI Heroes Ep3 was an in person meetup held on 31st Jan 2026, thanks to Smarsh Inc for providing the venue, this is just a small snippet of the things that happened at the meetup. Make sure to follow One2N events on luma to be notified of upcoming events: https://one2n.io/meetups

January 31, 2026 | 35 Minutes

The implications of AI in the SRE world | One2N Bits Ep.5

Jaideep Khandelwal

CTO @One2N

Saurabh Hirani

Principal SRE @One2N

AI in SRE is not just about flashy incident bots or code‑completion in your terminal. In this One2N Bits episode, Saurabh Hirani (Principal SRE, One2N) and Jaideep (CTO, One2N) break down what it really takes for AI to be useful in SRE teams, from documentation and RCAs to on‑call culture and leadership incentives. ​ ​They discuss whether AI can actually replace SREs, how to think in terms of “sub‑agents” mapped to SRE workflows, and why organizational context, knowledge sharing, and responsibility ownership matter more than yet another AI tool. ​ What you’ll learn Why AI in SRE is more than a code completion agent, and how to scope AI sub‑agents across incident management, capacity planning, and automation. How to handle knowledge hoarding, fear of replacement, and responsibility diffusion when AI is mandated from the top. Practical ways to use AI to clean up RCAs, keep architecture and docs fresh, and shorten onboarding feedback loops for SREs. How AI can free SRE teams from boilerplate and cognitive load so they can focus on business‑critical reliability work.

January 5, 2026 | 15 Minutes

Using LLMs to Improve Incident Response Times | mitramadal.ai - 2025

Jaideep Khandelwal

CTO @One2N

mitramandal is an AI meetup by One2N where engineers share how they've actually integrated AI into their daily development workflows. every developer is solving applied AI differently: some are orchestrating agentic pipelines across MCPs, others are wiring real-time RAG loops into Cursor or Claude, and more are operationalizing multimodal copilots inside CI/CD. we've seen fine-tuning flywheels, retrieval sandboxes, and autonomous evaluation harnesses that ship to prod every week. Talk 1: Using LLMs to Improve Incident Response Times - Jaideep Khandelwal : https://youtu.be/f_nV4GyWO0k?si=Zt_PnLzongzt4E9q Talk 2: The long way to production - Aditya Kasibhatla : https://youtu.be/SWW9rEzgxvA?si=Tr_Z_hz0gAb0JAqd Talk 3: vMCP LEGO blocks for AI workflows and agents - Shiladitya Banerjee : https://youtu.be/Fi8j3NG7WYA?si=xHRJwandjrJLUo0N

December 6, 2025 | 37 Minutes

AWS Outage: Real Costs & Disaster Recovery | One2N Bits Ep.4

Jaideep Khandelwal

CTO @One2N

Srivatsa RV

SRE @One2N

Srivatsa and Jaideep dive into disaster recovery essentials, right after the recent AWS outage. Learn why real business continuity demands more than cloud promises, how RTO/RPO set the stakes, and the economics behind DR setups. Packed with practical examples and leadership insights for SREs, CTOs, and engineering teams.

November 18, 2025 | 19 Minutes

Common pitfalls in Data Analytics: Patterns over Tools | One2N Bits Ep.3

Kshitish Deshpande

Software Engineer @One2N

Jaideep Khandelwal

CTO @One2N

How does analytics break in real-world startups and scaleups? In this episode, Kshitish from One2N breaks down the most common traps teams fall into not just technical, but mental models dragged from transactional to analytical systems.

November 4, 2025 | 13 Minutes

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