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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

Conway's Law as a lever to fix legacy architectures

Chinmay Naik

CEO @One2N

Conway’s law talks about how software architecture mirrors the org chart. Learn what this means in practice and how to use Reverse Conway Maneuver to refactor existing system architectures.

Read Blog

Hardening Kubernetes Security with Istio Ambient Mode on Flat Networks

Harshwardhan Mehrotra

SRE @One2N

Spandan Ghosh

Content @One2N

Learn how One2N used Istio Ambient mode to enforce zero-trust security on a flat EKS Hybrid network - with mTLS, SPIFFE identity, egress control, and NetworkPolicies.

Read Blog

Git was not built for multiple AI agents. Worktrees fix that.

Srivatsa RV

SRE @One2N

Spandan Ghosh

Content @One2N

Running multiple AI coding agents on the same repo? Git's single-checkout model breaks fast. Learn how git worktrees give each agent its own isolated workspace.

Read Blog

How one parameter stalled Postgres Replication with Debezium for 3 Weeks

Harsh Mishra

SRE @One2N

A story about WAL segments, missing events, and the one line of SQL that should have been there from the start.

Read Blog

Multi-Tenant SFTP: How We Built a Zero-Ops Setup on AWS Transfer Family

Kshitish Deshpande

Software Engineer @One2N

How One2N built a zero-ops multi-tenant SFTP system for a fintech client using AWS Transfer Family: one endpoint, isolated buckets, no manual server ops.

Read Blog

My new laptop broke Slack. That's how I ended up on Bluefin.

Harsh Mishra

SRE @One2N

Spandan Ghosh

Content @One2N

A kernel patch broke Slack on my Dell XPS. Here's how that led me to Bluefin, immutable Linux, bootc, and OCI images and why it solved my problem for good.

Blogs

Recent talks.

AI Enabled SDLC | Entain Tech Brews

Jaideep Khandelwal

CTO @One2N

We’ve all “vibe coded” with AI. In this Tech Brews at Entain talk, Jaideep Khandelwal shows how to turn that into a disciplined, AI‑enabled SDLC that teams can trust in production.

Talking to Coding Agents from Neovim

Saurabh Hirani

Principal SRE @One2N

In this Mitramandal.ai session, Saurabh Hirani goes under the hood of how Neovim talks to CLI‑based coding agents like Claude Code, OpenCode, Pi, and Ko, and why these patterns apply to any editor you use. He traces his own journey from Neovim to VS Code and back, driven by the realization that the best agentic UX often lives on the command line rather than in editor chat sidebars.

Migrating a Fintech Analytics Stack to Open Source - FOSS Meetup Pune

Kshitish Deshpande

Software Engineer @One2N

How One2N moved a fintech processing 50 to 70M USD daily from AWS Glue and Athena to a self-hosted ClickHouse stack, cutting infra costs dramatically.

Kubernetes for Hybrid Cloud Environments - #60 Kubernetes Pune Meetup

Harshwardhan Mehrotra

SRE @One2N

How One2N designed an EKS hybrid setup for a betting platform across 40 data centers with strict data residency and latency requirements.

Platform Engineering Strategy: Scaling from 5 to 500 Engineers | One2N Bits Ep. 6

Jaideep Khandelwal

CTO @One2N

Srivatsa RV

SRE @One2N

When do you actually need a platform team? Srivatsa and Jaideep break down platform engineering from first principles: culture, org size, and adoption.

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.

Talks

Ask whatever you have in your mind

Whether you have questions or are ready to discuss your business, we’re here to help. Reach out today.

Ask whatever you have in your mind

Whether you have questions or are ready to discuss your business, we’re here to help. Reach out today.