Media and Entertainment

Engineering built for the live moment..

33 million concurrent cricket viewers. 900 billion metrics monitored monthly. $350K saved annually on a single OTT platform — without dropping a frame.

Talk to our media & entertainment engineering team

40%

Cloud cost reduction, sustained

Zero

Downtime across UPI & FastTag

99.99%

Infrastructure uptime, resilience-proven

6%→1.5%

NPAs slashed via real-time pipelines

The State of BFSI Engineering

The state of media and entertainment engineering.

Live streaming is the hardest workload on the internet. A cricket match, an IPL final, a finale episode — millions of viewers arrive in the same minute, expect 4K quality, and abandon the platform if a single frame stutters. At the same time, content libraries grow continuously, CDN bills mount, and AI-driven recommendation is now table stakes for retention.

Most OTT and media engineering teams are caught between two cost curves and one reliability curve. Storage and CDN spend rises with the library. Compute spend rises with peak viewers. And reliability has to stay flawless even when both curves are bending up.

OpsTree engineers the live-event observability, sale-day reliability and cost-optimised streaming stacks behind India’s leading OTT platforms and new-age social media networks. REMS — our AI observability suite — was built in production on a cricket-peak workload of 33M concurrent viewers.

Where Transformation Stalls

Four industry-specific pain points.

Massive concurrent-viewer spikes

33M viewers in one minute. Architectures need elasticity, regional resilience and traffic management at a scale most stacks never see

Observability that does not collapse under load

When a cricket-league peak pushes monitoring infra to its limits, visibility drops exactly when you need it most

Storage, CDN and log cost growth

Petabyte-scale storage, log retention and CDN egress turn into one of the largest line items on the P&L.

Personalisation data at scale

Recommendation engines need real-time viewer behaviour data across millions of users — without batch lag or data sprawl.

How OpsTree Delivers for BFSI

Six outcome-led capability pillars.

Live-event SRE

Capacity planning, traffic shaping, regional failover and 24/7 NOC for live sports and event streaming. Cricket-peak battle-tested

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AI observability with REMS

Unified open-source telemetry, AI-driven log filtering, real-time SLO monitoring and automated incident workflows at 33M concurrent scale

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Cost optimisation for streaming

Log retention strategy, storage tiering, CDN cost engineering and compute rightsizing — $350K annual savings on a single OTT platform.

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Data engineering for recommendation

Real-time pipelines on Kafka, Airflow and modern lakehouses for viewer behaviour, content metadata and personalisation features.

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Modernised streaming delivery with BuildPiper

Standardised CI/CD across hundreds of microservices. Faster feature rollouts, automated rollback, controlled blast radius.

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Self-managed open-source data stacks

On-prem Kafka, Airflow and Druid deployments that replace expensive managed SaaS — proven $10K+/month savings, full data sovereignty

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CASE STUDY · MEDIA & ENTERTAINMENT

70% Faster Deployments and $350K Storage Savings for Leading OTT Platform

70% faster deployment cycles through optimised, automated CI/CD pipeline

$350K in annual storage cost savings via intelligent architecture redesign

Automated release management across high-traffic content delivery infrastructure

Consistent availability maintained for millions of concurrent streaming users

Stack: AWS Kubernetes CI/CD Prometheus Grafana

Read Case Study→

CASE STUDY · MEDIA & ENTERTAINMENT

Transformed Monitoring for a Global Customer Communications Giant

Monitoring system processing 900 billion metrics monthly with zero downtime

80% reduction in storage costs through intelligent observability data management

60-second automatic failover capability for uninterrupted service continuity

Up to 90% faster incident resolution with unified, centralised observability

Stack: Prometheus Grafana VictoriaMetrics Elasticsearch Kibana

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CASE STUDY · MEDIA & ENTERTAINMENT

Enterprise Scale Multi-Cloud Infrastructure Modernization with Zero Downtime for Sprinklr

Unified Infrastructure as Code standardised across AWS, Azure, and GCP

One-click automated Kubernetes deployment replacing manual weekend maintenance

Consistent security hardening and patching enforced across all cloud environments

Zero production downtime maintained throughout the entire modernisation engagement

Stack: AWS Azure GCP Kubernetes Terraform Helm

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Platforms and Accelerators for BFSI

Built in-house.
Deployed in production.

Vertical-specific application of our three platforms — not a generic product blurb.

⟨/⟩ REMS

Built in production on a 33M-concurrent-viewer cricket workload. Open-source telemetry, AI-driven log filtering and real-time SLO monitoring at scale

↗

◎ BuildPiper

Standardised microservices delivery for OTT platforms — faster feature velocity without sacrificing rollback safety during live events.

↗

◈ SRE in a Box

Kubelift for streaming-cluster upgrades during off-peak. SpendSmart for storage and compute FinOps. Sentryfuse for content-platform security.

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Why BFSI Leaders Choose OpsTree

Four differentiators.

Live-event-grade observability

REMS was forged in production on a 33M-concurrent cricket workload. No competitor can claim the same scale of proof.

OTT-scale battle-tested

Engineering behind India's largest OTT and new-age media platforms during their most demanding moments

Storage and CDN cost expertise

Petabyte-scale log and storage optimisation — savings that show up directly on the P&L.

Open-source first — content sovereignty

Self-managed Kafka, Airflow, Druid stacks that keep your content, viewer data and economics under your control.

Ready to move from pilot to production?

Tell us about the platform. We will tell you what good looks like — with proof, not pitches.

Start your transformation

Tell us where you're starting from — an engineer will follow up within one business day.

Read the AI observability for OTT case study →

Media and Entertainment