Unlock Caching to Scale OTT Platform for Millions of Users

Introduction

Building and scale OTT Platform to serve millions of users is no small feat. From managing traffic surges during major events to ensuring smooth, global streaming experiences, the challenges are as varied as they are significant. For example, live-streaming sports finals or releasing blockbuster movies can trigger massive, unpredictable traffic spikes. Without effective caching techniques and the right strategies, such events can overwhelm infrastructure, resulting in lag, buffering, or even outages.

OTT platforms must also address regional challenges like latency, compliance with local regulations, and optimized content delivery. A failure to manage these factors effectively can degrade user experience and erode customer loyalty. Moreover, downtime during peak periods not only frustrates users but also tarnishes the platform’s reputation. High availability and resilience are non-negotiable in such a competitive industry.

Equally critical is the need to balance quality with cost-efficiency. Delivering high-quality content at scale can strain operational budgets, especially without efficient caching strategies and scalable infrastructure. Inefficient systems and poor cost management can eat into profitability, making it imperative for OTT platforms to adopt smart, scalable solutions.

In this blog, we’ll explore how strategies like server-side and client-side caching, Kubernetes for scalable infrastructure, and ETL pipelines for content delivery and analytics can help scale OTT platforms meet these challenges head-on, delivering exceptional performance while optimizing costs.

The Importance to Scale OTT Platform, User Experience, and Cost-Efficiency

In the highly competitive world of OTT platforms, performance is everything. Today’s viewers expect content to load instantly, stream seamlessly without buffering, and deliver personalized experiences in real time. Any delay, stutter, or issue with the service can cause frustration, leading to churn and reduced engagement. Therefore, optimizing the performance of an OTT platform is essential for retaining users and staying competitive.

However, delivering top-tier performance comes at a cost. Running a platform that caters to millions of viewers can be expensive, especially when scaling to meet peak demand. Efficient resource management and cost optimization are critical to profitability. As the platform grows, the ability to scale OTT platform efficiently without overspending becomes a key focus.

For OTT platform founders, CEOs, and CTOs, achieving this delicate balance of performance, user experience, and cost efficiency is paramount. Not only do they need to keep users happy and engaged, but they also have to ensure that the platform is operating efficiently from both a technical and financial perspective. Effective optimization strategies can reduce operational costs, minimize downtime, and scale OTT platform to meet growing demand without sacrificing quality or user experience.

Server-Side Caching: Enhancing Speed and Reducing Load

Running an OTT platform means dealing with a surge in user requests, especially during busy times or when new content drops. To scale OTT platform effectively, Server-side caching is key to keeping things fast and scalable. It works by storing popular data—like recently watched lists, personalized suggestions, images, and thumbnails—closer to users. This reduces the need for repeated database queries or heavy backend processing. With less strain on the servers and faster response times, the platform ensures a smooth, uninterrupted experience, even for millions of users at once.

Using Content Delivery Networks (CDNs) to Scale OTT Platform API Responses

When building an OTT platform, delivering a seamless user experience is crucial, especially for data-intensive pages like the home page. To scale OTT platform effectively, leveraging a CDN plays a vital role in optimizing performance. Instead of focusing on generic benefits, let me walk you through how we utilized a CDN to scale OTT platform by caching the home page, master data, and user-specific responses, ensuring faster load times and a better user experience.

Problem Statement

The home page is just one example where we implemented this approach to scale OTT platform effectively. As the central hub for personalized content—including banners, categories, recommendations, and more—the home page required fetching and displaying large volumes of data. Without optimization, this resulted in increased latency, frequent database queries, and higher server loads. These issues significantly affected the user experience, especially during periods of peak traffic.

Our Solution

To address these challenges, we decided to integrate a CDN to cache API responses at various levels. Here is a breakdown of what we did:

1. Caching the Entire Home Page Response
   • The home page API response contains a mix of static and dynamic content. We decided to cache the entire response in JSON format at the CDN level.
   • By leveraging the CDN’s edge servers, we reduced the need for repeated requests to our origin server, significantly decreasing the load on our backend.
2. Caching Master Data
   • Master data, such as categories, genres, and metadata, is relatively static and doesn’t change frequently.
   • We set up a long TTL (Time-To-Live) for this data in the CDN, ensuring that the cache was updated only when necessary. This reduced bandwidth usage and improved response times for users across the globe.
3. User-Specific Responses
   • Personalization is key for an OTT platform, but it poses challenges when caching user-specific data.
   • To address this, we implemented key-based caching. The CDN cached responses based on user IDs, ensuring each user received personalized content without generating redundant database queries.

Results

1. Reduced Page Load Time
   • Caching API responses in the CDN significantly accelerated home page load times, even during high-traffic events. This optimization enabled the platform to seamlessly handle a minimum of 10 million concurrent users and scale OTT platform to a peak load of 50 million concurrent users without performance degradation.
2. Reduced Bandwidth Costs
   • By serving most requests from the CDN’s edge servers, we significantly reduced the bandwidth consumed by our origin server, thereby lowering infrastructure costs. This optimization allowed our platform to achieve 25,000 transactions per second (TPS) with a latency of just 250-300 milliseconds, ensuring a fast and seamless user experience.
3. Increased Content Availability
   • With CDN caching, users could access cached content even if the origin server experienced downtime, ensuring uninterrupted service.
4. Improved Website Security
   • The CDN added a layer of security by shielding our origin server from direct traffic, mitigating risks like DDoS attacks.

By implementing this CDN strategy, we not only improved the performance of our OTT platform but also enhanced the overall user experience. This approach was instrumental in helping us scale OTT platform effectively. Caching API responses proved to be a game-changer for our home page and master data, demonstrating that with the right strategy, even complex scenarios like user-specific responses can be optimized efficiently.

Using Redis for Dynamic Object Caching to scale OTT platform

In our OTT platform, dynamic object caching using Redis played a pivotal role in delivering a seamless and efficient user experience. Here’s how we leveraged Redis for caching dynamic content and the benefits it brought to our platform:

1. Personalized Data Caching
   • Features like continue watching, watchlists, and user preferences required dynamic caching as the data varied per user.
   • We utilized Redis for key-based caching, where keys were structured using user IDs, profiles, and content identifiers. For example:
     Key: “user:{userId}:profile:{profileId}:watchlist”
   • Value: We store objects in Redis to enable their reuse across multiple API calls, ensuring efficient data retrieval and reducing redundant computations.
   • TTL for these keys was set to 30 days, ensuring long-term availability while avoiding stale data.
2. Master Data Caching
   • Relatively static content, such as categories, genres, and banners, was cached in Redis with a TTL of 1 day.
   • The CMS system triggered cache invalidation whenever updates were made, ensuring the data remained fresh without overloading the database.
3. Session and Token Management
   • Redis was also used to cache user sessions and authentication tokens for quick access and improved authentication performance.
4. High-Volume Content Delivery
   • For high-demand APIs like the home page and trending content, Redis acted as a secondary cache after the CDN. If the data was not available in the CDN, Redis served as the fallback, ensuring faster data retrieval than querying the database directly.

Key Optimizations

1. Lazy Loading
   • We adopted a lazy-loading approach for caching, where data was only added to the Redis cache after the first request. This strategy prevented unnecessary caching of unused data.
2. Real-Time Updates
   • User actions, such as adding/removing items from the watchlist, triggered real-time updates in Redis. This ensured consistency across devices and avoided stale data issues.
3. Data Storage Optimization
   • we adopted a more structured and efficient approach to caching by utilizing Redis HashTables. This method provided greater control over the cached data and enhanced our ability to manage it effectively.
4. Eviction Policies
   • To manage memory effectively, we configured Redis with eviction policies such as allkeys-lru (Least Recently Used). This ensured that old and unused keys were automatically removed to make room for new data.

Challenges and Solutions

• Concurrency Issues: With multiple devices accessing and updating the same data, concurrency control was critical. We implemented Redis transactions to ensure atomic operations.
• Large Object Handling: Some objects, like personalized home pages, were too large for direct caching. We split these into smaller chunks for efficient storage and retrieval.
• Scalability: As user numbers grew, so did the load on Redis. Horizontal scaling and sharding were implemented to distribute the load across multiple Redis instances.

Outcomes

• Reduced Latency: Redis’s in-memory caching significantly decreased response times for dynamic content, enhancing the user experience.
• Lower Database Load: By offloading frequent queries to Redis, we reduced the load on our primary database, ensuring better performance and scalability.
• Improved Personalization: Dynamic caching allowed us to deliver highly personalized content quickly, setting our platform apart in the competitive OTT space.

Redis proved to be an indispensable component of our architecture, playing a key role in helping scale OTT platform. Its dynamic object caching capabilities ensured our users experienced lightning-fast interactions without compromising on content relevance, striking the right balance between performance and personalization.

Client-Side Caching: Enhancing Performance on Mobile Devices

To maximize the benefits of client-side caching on mobile devices and scale OTT platform effectively, we implemented a mechanism that utilizes app lifecycle events—specifically when the application goes into the background or comes to the foreground. This approach ensures that the client-side cache remains fresh and synchronized with server-side data, providing users with a smooth and uninterrupted experience.
Background Sync Events

When the application transitions to the background (e.g., a user minimizes the app or switches to another application):

1. Push Updated Data:
   • Any unsynced user activity, such as updates to the watchlist, playback progress, or user preferences, is pushed to the server.
   • This ensures that server-side data remains up-to-date and consistent, even if the app is closed.
2. Pull Server-Side Updates:
   • The app asynchronously fetches updated data from the server, such as new content recommendations or changes to user profiles.
   • This ensures that the client-side cache is refreshed without impacting user interactions when the app is reopened.

Implementation of Content Caching on Mobile Devices

Mobile applications utilize various local storage mechanisms to enhance performance and user experience by reducing dependency on network calls. Key caching techniques include:

1. Persistent Storage:
   Leverage lightweight storage options like SharedPreferences (Android) or UserDefaults (iOS) for caching key-value pairs. These are particularly useful for small, non-sensitive data such as user settings, theme preferences, or the last login timestamp.

Practical Example:

An OTT app can enhance user experience by locally caching the homepage layout, banners, and personalized recommendations. This allows the app to display content instantly on launch, providing a seamless experience while awaiting updated API data.

Use of Differentiated Time-to-Live (TTL) Values

To strike the perfect balance between performance and data freshness while scale OTT platform effectively, it’s essential to assign differentiated Time-to-Live (TTL) values based on the type of data being cached. This approach ensures that static and semi-static content loads instantly, while dynamic, user-specific data stays accurate and up-to-date. Here’s how you can apply TTL values effectively:

1. Static Data (Rarely Changes)

Static data includes elements like category headers, app banners, and predefined playlists that change infrequently.

• Recommended TTL: 24–72 hours
• Why: These assets are not time-sensitive and can remain cached for extended periods without causing inconsistencies. Caching them for longer durations reduces server load and ensures faster page loads.

2. Semi-Dynamic Data (Periodic Updates)

Semi-dynamic data consists of content that updates periodically, such as trending shows, personalized recommendations, or featured collections.

• Recommended TTL: 15–60 minutes
• Why: Refreshing this data periodically ensures it reflects recent user activity, trends, or updated recommendations while minimizing unnecessary backend calls.

3. Highly Dynamic Data (Frequent Updates)

Highly dynamic data includes user-specific, rapidly changing information like playback history, real-time notifications, or live updates.

• Recommended TTL: 1–5 minutes or no caching (for highly sensitive data)
• Why: This type of data directly impacts the user experience and requires high accuracy. Using short TTLs or bypassing caching altogether ensures users always see the most up-to-date information.

Benefits of TTL Differentiation

By tailoring TTL values to the nature of the data, you can:

• Improve performance by serving cached responses for static and semi-static content.
• Maintain user satisfaction by ensuring highly dynamic content remains fresh.
• Reduce server load by limiting frequent database queries for less time-sensitive data.

This differentiation allows static and semi-static content to be loaded instantly while ensuring user-relevant dynamic content remains up-to-date.

Cache Invalidation Strategies on the Frontend

To ensure data freshness and maintain a smooth user experience, we implemented robust cache invalidation strategies for our OTT platform’s front end. Here’s how we tackled it:

1. Admin-Controlled Invalidation via CMS
   • Content changes, such as updates to categories or genres, trigger cache invalidation. The CMS invalidates cached data stored in Redis and the CDN, ensuring users always receive the latest content. This approach minimizes manual intervention and automates the content update process across all caching layers.
2. Time-Based Expiration
   • Cached resources were assigned a Time-To-Live (TTL) to enable automatic invalidation. For example:
     • Static or semi-static resources like master data had a 1-day TTL in the CDN.
     • Categories and genres in Redis followed a similar TTL strategy to synchronize across devices.
   • A Cache-Control: max-age=3600 header was used to enforce a one-hour expiry for specific resources, balancing freshness with performance.
   • For personalized user-specific data (e.g., watchlist, continue-watching), a longer TTL (e.g., 30 days) ensured user convenience without frequent re-fetches.
3. Backend APIs for Cache Management
   • A dedicated backend API was implemented to handle master content retrieval directly from the CDN. This API also stored fetched data in Redis for redundancy, minimizing calls to the CDN or the main server.
   • Cache invalidation was synchronized through the CMS, which triggered updates to both the CDN and Redis layers, ensuring consistent data across all caching endpoints
4. Cache Synchronization Between Redis and CDN
   To ensure a seamless user experience and maintain data consistency, we designed a robust synchronization process between Redis and the CDN. This process tackled potential mismatches that could arise from frequent updates to content or caching layers.
   
   Every content update through the Content Management System (CMS) triggered two key actions:
   1. Redis Invalidation: The relevant keys in Redis were immediately invalidated to remove outdated data.
   2. CDN Purge: Stale objects in the CDN were purged to ensure that updated content was served to end-users without delay.
   
   To avoid service interruptions, a reliable fallback system was implemented:
   1. Redis Miss: If Redis did not contain the requested data, the system seamlessly fell back to the CDN to retrieve it.
   2. Simultaneous Refresh: While serving the content from the CDN, the missing data was refreshed and updated in Redis, ensuring future requests could be served quickly.
   
   To maintain consistency between Redis and the CDN:
   1. Aligned TTLs (Time-To-Live): Both Redis and the CDN were configured with synchronized expiration times, preventing one layer from holding outdated data longer than the other.
   2. Consistent Invalidation Rules: Shared invalidation logic ensured that any content updates or deletions were uniformly applied across both caching layers.
   
   By leveraging these strategies, you can maintain a balance between data freshness, performance, and efficient resource utilization, delivering an optimal user experience.

Scalable Infrastructure with Kubernetes: Ensuring Resilience and Efficiency to Scale OTT Platform

Running an OTT platform that serves millions of users is a complex endeavor, particularly when traffic surges unpredictably during live events or new content releases. Ensuring uninterrupted service while balancing costs and performance is no easy task. Here’s how we tackled these challenges step-by-step, learning from each stage of our journey to scale OTT platform effectively.

1. Role of Network Load Balancers in Efficient Traffic Distribution

In the initial stages of building the OTT platform, we utilized Application Load Balancers (ALBs) to distribute user traffic. While ALBs offered flexibility with routing HTTP/HTTPS traffic, they struggled to handle sudden surges, especially during live events or blockbuster releases. These limitations resulted in increased latency, unbalanced traffic distribution, and suboptimal resource utilization.

To address these issues, we switched to Network Load Balancers (NLBs), which provided the high throughput and low-latency performance needed to handle real-time traffic surges effectively.

Key Benefits for OTT Platforms:

• High Availability: If a server becomes unresponsive, the NLB automatically reroutes traffic to healthy nodes, ensuring uninterrupted service.
• Traffic Prioritization: For OTT use cases, NLBs can handle different types of traffic (e.g., video streaming, API requests) by directing them to specific backends optimized for those workloads.
• Seamless Scaling: NLBs work in harmony with Kubernetes to manage the distribution of traffic as new pods are added or removed based on demand.

For example, during a live sports event, an NLB can manage the sudden influx of users, ensuring a smooth streaming experience.

2. Real-Time Auto-Scaling with the Karpenter

Managing compute resources was another significant challenge. Initially, we relied on Horizontal Pod Autoscalers (HPAs) to scale OTT platform services at the application level. While HPAs worked well for pod scaling, they lacked the speed and flexibility required for node-level scaling during sudden spikes in traffic.

To address these shortcomings, we introduced Karpenter, an open-source Kubernetes cluster auto-scaler.

How Karpenter Benefits OTT Platforms:

• Faster Scaling: Karpenter reacts to traffic spikes in seconds, quickly launching new instances to meet the demand for content delivery.
• Resource Optimization: It intelligently selects the most cost-effective EC2 instance types, sizes, and regions to run workloads, ensuring performance without overspending.
• Workload Prioritization: For OTT platforms, Karpenter can prioritize critical workloads like streaming and recommendations while deferring lower-priority tasks (e.g., background analytics).

For example, during a live sports event with millions of concurrent viewers, Karpenter swiftly scaled the infrastructure, ensuring uninterrupted streaming and minimal latency, then scaled down as traffic normalized.

3. Cost Optimization with Scaling Strategies

As the platform grew, controlling costs without compromising performance became crucial. By leveraging Kubernetes and Karpenter, we implemented effective strategies to optimize resource utilization and reduce expenses.

Strategies for Cost Optimization:

1. Off-Peak Scaling:
   • Schedule workloads to scale down during non-peak hours. For example, if the peak usage is between 6 PM and 11 PM, scale down resources during early mornings when user activity is minimal.
   • Leverage Kubernetes Horizontal Pod Autoscaler (HPA) to scale the number of pods based on traffic metrics, such as CPU or memory usage.
2. Spot Instances for Non-Critical Workloads:
   • Use AWS Spot Instances for transient or non-critical tasks, such as batch processing or preloading caches. Spot Instances can reduce compute costs by up to 90%.
3. Right-Sizing Resources:
   • Continuously monitor workloads to identify over-provisioned pods or nodes. Use Karpenter to dynamically adjust instance types based on current requirements, ensuring no resources are wasted.
4. Node Pool Segmentation:
   • Separate workloads into different node pools based on their priority. For instance, maintain high-priority streaming services on on-demand instances while running analytics on spot instances.

Example: By maintaining a balanced mix of on-demand and spot instances (e.g., a 50-50 split), we reduced costs significantly while delivering a flawless user experience, even during high-demand scenarios.

Building ETL Pipelines for Content Delivery to Recommendation Engines, OpenSearch, Analytics, and Data Lakes

When scaling an OTT platform to serve millions of users, one of the biggest challenges is ensuring data flows smoothly from the source to where it’s needed whether that’s recommendation engines, search systems, or analytics dashboards. Having built ETL pipelines for similar platforms, I can tell you this is where the magic happens. The right setup ensures personalized recommendations, fast searches, and actionable business insights, all in real-time, helping to scale OTT platform effectively.

Let’s walk through how we made it work.

Why ETL Pipelines Matter for OTT Platforms

Modern OTT platforms thrive on data. Every user interaction—what they search for, what they watch, how they engage—creates valuable insights. But raw data isn’t useful until you process it. That’s where ETL (Extract, Transform, Load) pipelines come in.

For our platform, we designed the pipeline to be fast, scalable, and reliable. It had to handle millions of records daily without missing a beat, especially during spikes like live events. Here’s how we broke it down:

Step 1: Content Ingestion

The first step is getting raw data into the system. We dealt with three main sources:

• User Activity Logs: Capturing interactions like play/pause actions, searches, and clicks on recommendations.
• Video Metadata: Details like titles, genres, durations, and release dates from our MySQL database.
• External APIs: Bringing in extras like trending content or third-party reviews.

To make this work, we used Debezium for Change Data Capture (CDC). This tool listens for changes in the MySQL database—like when new content is added—and streams them to Kafka in real time. Kafka then acts as the traffic controller, streaming data across the pipeline with minimal latency.

Step 2: Data Transformation

Once the data’s in, it needs cleaning and organizing. This is the stage where raw logs and metadata start to take shape:

• Data Cleaning: We removed duplicates and fixed inconsistencies. You’d be surprised how much junk comes in from various sources.
• Data Enrichment: Adding context was a game changer. For example, pairing a user’s watch history with trending content in their region helped us refine recommendations.
• Data Structuring: Raw logs don’t work well for analytics or search. So, we transformed them into formats optimized for downstream systems.

One specific win was for our recommendation engine. By structuring logs to include time-of-day patterns, we saw a huge improvement in the relevance of suggestions like surfacing workout videos in the morning or thrillers late at night.

Step 3: Data Loading and Integration

After transformation, the processed data was sent to various endpoints. Here’s how we handled the key destinations:

• Recommendation Engines: This was all about driving engagement. The cleaner and more structured the data, the better the recommendations.
• OpenSearch: This powered our search functionality. We continuously updated it with logs and metadata so users always had the freshest search results.
• Analytics Dashboards: For the business side, we ensured teams had access to real-time insights.
• Reports and Data Lakes: We stored everything both raw and processed data for long-term analysis or machine learning experiments.

Real-Time Data Synchronization with Kafka and Debezium

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To scale OTT platform effectively and deliver accurate and up-to-date search results, we integrated Kafka into our workflow, enhanced by Debezium for Change Data Capture (CDC). This ensures real-time synchronization between the database and the search index, reflecting content changes instantly.

Workflow Architecture

1. Database Listener with Debezium

• Debezium for CDC: Captures real-time changes in the MySQL database, including:
  • New content additions.
  • Updates to existing metadata.
  • Deletions.
• Streaming Updates: These changes are streamed to Kafka, initiating instant processing without missing updates.

2. Message Queue with Kafka

• Transformation and Scalability: Kafka transforms database changes into messages, handling high traffic efficiently.
• Fault Tolerance: Built-in redundancy ensures data integrity and reliable message delivery.

3. Data Ingestion to targets

• Consumers: Kafka consumers process messages, extracting relevant data changes.
• Search Index Updates: Changes are applied instantly in OpenSearch to ensure:
  • New content is searchable in real time.
  • Updated metadata reflects in search results.

Recommendation Engine Optimization

Personalizing user experiences through real-time recommendations was achieved using the Gorse recommendation platform, a key component in helping scale OTT platform. Our strategy revolved around data aggregation, transformation, and optimized caching to balance performance and cost.

1. Leverage Cohort-Based Caching

• Grouped users by shared attributes (demographics, viewing patterns, engagement levels) to:
  • Reduce cache storage needs.
  • Simplify recommendation updates.

2. Optimize ETL Pipelines

• Incremental Data Updates: Process only new or changed data to enhance efficiency.
• Data Pre-Aggregation: Transform data during ETL to reduce real-time computation needs.

3. Hybrid Caching Strategy

• Static Cohort Cache: Pre-generate recommendations nightly for defined cohorts.
• Dynamic Cache: Tailor on-demand recommendations for premium or highly active users.

Scaling Search with OpenSearch

OpenSearch powers fast, scalable search capabilities, critical for OTT platforms handling millions of users. Here’s how we optimized its performance:

1. Leverage Query and Document-Level Caching

• Cache popular queries (e.g., trending genres) and high-demand content.
• Use TTL policies to ensure freshness and minimize query load.

2. Optimize ETL Pipelines for OpenSearch

• Incremental Indexing: Update only changed or new records.
• Bulk Operations: Streamline large-scale updates for efficiency.

3. Partition OpenSearch Clusters

• Partition data by region, language, or genre for optimized performance.
• Balance shards across nodes to avoid hotspots.

Data-Driven Analytics for Actionable Insights

We use open source tool Matomo to capture the user and content analytics.

1. Real-Time and Batch Data Processing

• Real-Time Processing: Used Motamo for dynamic use cases like trending content.
• Batch Processing: Used Motamo for aggregated historical data for detailed insights.

2. Intelligent Caching

• Cached user-specific data (e.g., watchlists) and content metrics (e.g., view counts).
• Distributed caching with TTL mechanisms reduced latency and improved responsiveness.

Data Lake and Reports

A centralized data lake stores raw data from VMS, analytics, and recommendation systems, supporting high scalability and cost-effective reporting. We used open source tool as Metabase.

Key Strategies

1. Partitioned Storage: Organized by time and region to improve access speed.
2. ETL Pipeline Automation: Incremental updates reduced resource usage and optimized performance.
3. Query Result Caching: Cached heavy report outputs to minimize reprocessing.

Conclusion

Scaling an OTT platform to millions of users requires more than a strong infrastructure—it needs smart strategies that balance performance, reliability, and user experience. You can reduce server load and speed up content delivery by using server-side caching. Adding client-side caching ensures a better user experience, especially on mobile devices, where bandwidth and delays are more important.

A key part of scalability is using a containerized infrastructure like Kubernetes. It improves reliability, helps save costs, and handles traffic spikes smoothly. Building efficient data pipelines for recommendation engines, OpenSearch, analytics, and data lakes also helps create personalized experiences and supports data-driven decisions.

By combining these strategies, you can create a high-performing infrastructure to scale OTT platform, impress users, and stay ahead in the competitive streaming market. Focusing on caching, scalability, and data pipelines will help your platform grow and succeed in the long run.

Thank you for Reading !! 🙌🏻😁📃, see you in the next blog.🤘

I hope this article proves beneficial to you. If you have any doubts or suggestions, feel free to mention them in the comment section below or contact us.

The end ✌🏻

References

• Why companies turning to a Fractional CTO for growth?
• Symbolic Links and Their Use.
• CloudForecast in Action: Manage Cloud Costs and Scale Seamlessly
• How does Karpenter cut Kubernetes costs by 20% and scale them faster?
• How CI/CD Cut Costs & Time for a Leading Chatbot Company?