Media And Entertainment  - OpsTree Global
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Media and Entertainment 

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.

01
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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02
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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03
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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04
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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05
Modernised streaming delivery with BuildPiper
Standardised CI/CD across hundreds of microservices. Faster feature rollouts, automated rollback, controlled blast radius.
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06
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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A leading Indian OTT platform

70% faster deployments and $350K saved during cricket peaks

33M concurrent viewers supported with AI-led observability
900B+ metrics managed monthly via REMS
$350K annual savings from optimised log retention
1 PB data migrated with zero downtime; 70% faster deployments
Stack: Stack: REMS open-source telemetry AI-driven log filtering
Read Case Study→
A new-age social media platform

$10K/month saved by replacing managed Kafka and Airflow

Migrated to self-managed Kafka and Airflow on Linode Kubernetes
200+ Airflow DAGs migrated to open-source setup
$10,000/month in cost savings achieved
Datadog-powered observability for proactive monitoring
Stack: Stack: Self-managed Kafka Airflow Linode Kubernetes Datadog
Read Case Study→
An IoT-powered automation leader

Real-time processing of 1 Gbps streaming data

1 Gbps real-time data processed for instant insights
10 TB migrated from legacy DBs to Druid
Query time reduced from hours to minutes/seconds
100+ cron jobs centralised on Apache Airflow
Stack: Stack: Kafka Druid Airflow real-time analytics
Read Case Study→
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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 33M-concurrent

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

↗ real-time SLO monitoring at scale
◎ BuildPiper
Standardised delivery for OTT

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

↗ Safety during live events.
◈ SRE in a Box
Kubelift for streaming-cluster upgrades

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

↗ Sentryfuse for content-platform security.
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 →
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Possibilities ReImagined