Calsoft hosts Ops for AI meetup addressing production challenges

Rajshekar Chavakula, Senior Technical Architect at Calsoft

Shrish Ashtaputre

Three technical sessions covering CI/CD, observability, and FinOps for AI production systems with approximately 100 in-person participants

Unlike traditional software, AI involves massive sunk costs in experimentation, requiring a fundamentally different approach to financial operations.”

— Rajshekhar Chavakula, Senior Technical Architect at Calsoft

PUNE, MAHARASHTRA, INDIA, January 20, 2026 /EINPresswire.com/ -- Calsoft's January 17 meetup on Operations for AI applications drew approximately 100 in-person attendees at its headquarters. The three-hour technical program, organized in collaboration with AI Circle community, featured practitioner-led sessions on CI/CD for AI, observability frameworks, and FinOps strategies, with attendees engaging in discussions on deployment optimization, cost management, and operational guardrails for AI workloads.

𝗤𝘂𝗶𝗰𝗸 𝗩𝗶𝗲𝘄:

- Three technical sessions covering CI/CD, observability, and FinOps for AI production systems with approximately 100 in-person participants
- Platform teams, ML engineers, DevOps practitioners, and finance teams exchanged insights on MLOps workflows, cost optimization, and deployment strategies
- Calsoft demonstrated proprietary Cloud Simulator and Cost Analysis tools addressing AI infrastructure cost challenges

Hemant Kumar, Principal Software Engineer at Kaizen, opened with CI/CD for AI, addressing data versioning and model registry challenges. Mukta Aphale, Developer Evangelist at Last9, presented on observability for AI systems. Rajshekhar Chavakula, Senior Technical Architect at Calsoft, concluded with an in-depth session on FinOps in AI, exploring how AI workloads are transforming cloud cost management and demonstrating Calsoft's proprietary cost optimization tools.

Chavakula's session identified three critical hidden cost drivers impacting organizations: GPU utilization challenges where companies pay for expensive compute resources but struggle to maintain full utilization, data gravity costs from massive egress fees when moving training data between clouds, and zombie resources such as development environments running unattended over weekends.

"AI FinOps differentiates between 'Good Waste' — failed experiments that provided valuable learning — and 'Bad Waste' such as idle GPUs," Chavakula explained. "Unlike traditional software, AI involves massive sunk costs in experimentation, requiring a fundamentally different approach to financial operations."

The session outlined how organizations are shifting from traditional infrastructure management to workload-based financial operations, measuring cost per training run, cost per thousand tokens for inference, and cost per prediction rather than simple server costs. Chavakula introduced practical guardrails including workload ownership accountability, cost-friendly default settings with auto-shutdown features, real-time alerts on unusual spending spikes, and approval workflows for high-cost operations.

Calsoft demonstrated two proprietary tools developed to address AI cost challenges. The Cloud Simulator, currently in lab phase, enables organizations to simulate infrastructure costs before deployment. During the demonstration, the tool identified a misconfigured scale-in policy that would have left 50 expensive GPU nodes running idle for an hour, preventing costly deployment errors. The Cost Analysis Tool, in audit phase, identifies zombie resources including SageMaker endpoints with zero invocations still incurring charges and detached EBS volumes with high IOPS provisioning.

"In AI, a forgotten instance isn't ten dollars per month; it's ten dollars per hour," Chavakula noted, highlighting the critical importance of automated cost monitoring for AI workloads.

The FinOps session emphasized that AI financial operations represent a cultural shift where engineering, finance, and business teams collaborate on data-driven spending decisions. The goal is maximizing business value through improved unit economics rather than simply reducing expenditure, recognizing that increased spending may be justified when return on investment is demonstrated.

Shrish Ashtaputre, Senior Technical Director at Calsoft, welcomed attendees and positioned the event within Calsoft's AI and MLOps practice areas. "We have a very strong practice in AI, ML, AIOps, MLOps areas," Ashtaputre said, noting Calsoft's 27-year presence in technology services.

The networking session allowed participants to exchange implementation experiences on managing AI costs, deployment strategies, and operational best practices. The event reflected growing enterprise focus on operationalizing AI beyond experimentation, with practitioners sharing insights on bridging the gap between research and production deployment.

𝗔𝗯𝗼𝘂𝘁 𝗖𝗮𝗹𝘀𝗼𝗳𝘁

Calsoft is a technology services and product engineering company with 27 years of industry experience and headquarters in Pune, India. The company operates offices in Bangalore, Indore, Kolkata, and the United States, serving over 2,000 employees. Calsoft specializes in data center technologies, including storage, networking, virtualization, cloud infrastructure, and maintains dedicated practices in AI, ML, AIOps, and MLOps. For more information, visit: https://www.calsoftinc.com/

Richa Thomas
Calsoft
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