How to scale transcoding efficiently for modern media workflows

AI, Articles, Broadcasting, IBC, News
11 Aug, 2025
Posted by: Ana Pace

In today’s media landscape, transcoding is more than just a technical necessity — it’s the backbone of delivering content to multiple platforms, devices, and formats. As audience demand grows, the ability to scale transcoding efficiently becomes a competitive advantage.

But scaling isn’t just about adding more processing power. It’s about optimizing performance, controlling costs, and ensuring reliability while adapting to rapidly changing delivery requirements.

Why scaling transcoding is challenging

Transcoding workloads are inherently resource-intensive. They require substantial CPU/GPU power, high-bandwidth connectivity, and workflows capable of handling multiple codecs, resolutions, and bitrates.
The main challenges include:

High compute demand: Live events and 4K+ content push infrastructure to its limits.
Variable workloads: Peaks during events or launches can overload fixed capacity.
Cost control: Overprovisioning resources leads to wasted spend.
Latency management: Especially critical for live and low-latency streaming.

Strategies for efficient transcoding at scale

1. Use cloud or hybrid architectures: Cloud transcoding offers virtually unlimited scalability, but costs can rise quickly. A hybrid approach — using on-premise hardware for baseline capacity and cloud for peaks — balances performance and budget. Pro tip: DVEO’s Brutus Cloud enables seamless workload shifting between local and cloud resources, so you only pay for what you use during spikes.

2. Optimize codec and bitrate ladders: Not every viewer needs 4K HDR at maximum bitrate. By analyzing audience data, you can adjust bitrate ladders and codec profiles to reduce unnecessary processing and storage without impacting perceived quality.

3. Leverage GPU acceleration: GPUs significantly accelerate transcoding for modern codecs like H.265/HEVC and AV1, enabling more streams per server with lower energy consumption. Example: DVEO AI Servers are optimized for parallel processing, delivering high-density transcoding for live and VOD workflows

4. Automate with intelligent workflows: Manual intervention slows down scaling. Intelligent job scheduling, AI-driven quality checks, and automated resource allocation ensure efficiency and reduce errors.

5. Monitor and adjust in real-time: Scaling isn’t a “set-and-forget” process. Continuous monitoring of CPU/GPU usage, stream quality, and delivery performance ensures you can adapt instantly to changing conditions.

The DVEO approach

At DVEO, we design transcoding solutions to scale intelligently. Our portfolio includes:

Brutus Cloud: Elastic cloud transcoding with predictable pricing.
On-Prem Servers: High-performance hardware for sustained workloads.
AI-Powered QC: Automated quality checks for consistent delivery.
Multi-Protocol Support: SRT, HLS, MPEG-TS, DASH, and more.

Whether you’re delivering live sports to millions or preparing on-demand libraries for global distribution, we make sure your transcoding pipeline is fast, reliable, and cost-effective.

Conclusion

Efficient transcoding at scale isn’t about throwing more hardware at the problem — it’s about strategic scaling, where performance, cost, and quality align. With the right combination of infrastructure, automation, and monitoring, you can meet growing demand without sacrificing efficiency.