Kling AI builds some of the most advanced AI video generation models in the world. To do that, they need massive volumes of high-quality public video data - consistently, reliably, and at a speed that keeps pace with their model development cycles.
Titan handled that for them, providing end-to-end video data collection so Kling AI’s team could stay focused on what they’re actually there to do.
At a Glance
| Company | Kling AI |
|---|---|
| What they needed | Continuous public video data at petabyte scale for training, without building or maintaining internal collection infrastructure |
| What Titan delivered | 31 Gbps monthly of video datasets delivered directly to their cloud storage |
| Delivery success | 99.8% sustained over engagement |
| Infrastructure | 3.8M+ residential IPs, supports 4K/8K/10h+ videos |
| Key outcome | Kling AI’s ML team trains models while Titan manages the entire data collection layer |
Who Should Read This:
This case study is most relevant for:
- ML engineers training video generation or multimodal models who need large-scale public video data
- Data engineers building or managing training data pipelines for AI video teams
- AI research teams evaluating whether to build internal collection infrastructure or use managed providers
- Engineering leaders responsible for sourcing, delivering, or budgeting AI training data at enterprise scale
- Teams building models similar to Kling AI, Runway, Pika, or Sora exploring how leading AI video companies approach data operations
The Problem
Kling AI’s model development runs on continuous iteration cycles. That means they need video data flowing consistently - not in one-time bursts, but as an ongoing pipeline that doesn’t break when YouTube updates their systems or when collection needs to scale up.
Building that infrastructure themselves would have meant:
- 2-3 engineers dedicated to collection infrastructure
- Constant maintenance as YouTube’s anti-bot systems evolve
- Managing millions of residential IPs to avoid throttling
- Debugging download failures and format inconsistencies
- Engineering time pulled away from actual model development
For a company at Kling AI’s level, that’s not just operational overhead - it’s opportunity cost. Every hour spent maintaining scrapers is an hour not spent improving models.
What Titan Did for Kling AI
Kling AI worked with Titan to handle the entire video data collection layer. Here’s what that looked like in practice:
The Specific Challenge
Kling AI needed a steady flow of public video data at scale to train their video generation models. The data had to arrive consistently, be properly formatted for training workflows, and keep flowing as their requirements evolved - without forcing their team to build and maintain collection infrastructure that wasn’t their core competency.
What Titan Delivered
Sustained collection at scale
31 Gbps monthly delivered continuously using 3.8M+ residential IPs. This infrastructure handles YouTube’s anti-bot systems, maintains throughput without triggering blocks, and keeps data flowing even as platform protections change.
Production-grade reliability
99.8% delivery success meant Kling AI’s team wasn’t chasing failed jobs, filtering broken files, or debugging why collection stalled. The data showed up consistently.
Ready-to-use datasets
Video files, synchronized audio, metadata, and transcripts landed directly in Kling AI’s cloud storage - properly formatted, QA-checked, and structured for training workflows. No intermediate steps. No additional pipeline needed just to receive the data.
This direct cloud delivery eliminated setup friction entirely. Datasets became immediately accessible in Kling AI’s existing infrastructure the moment delivery completed - ready to feed into training jobs without reformatting, restructuring, or moving between systems.
Configured for their requirements
4K, 8K, and 10h+ duration support. Delivery cadence matching their training schedule. Format specifications fitting their actual consumption needs - not generic outputs requiring reformatting.
What That Meant for Kling AI
Kling AI’s ML engineers stayed focused on model training while Titan handled the entire data collection layer. When YouTube rolled out new anti-bot measures or collections needed to scale up, Kling AI’s team didn’t have to stop model work to handle it.
The data infrastructure became invisible - working consistently in the background, letting Kling AI’s team focus on advancing video generation models instead of maintaining data operations.
Who This Works For
If you’re in one of these situations, here’s specifically how Titan can help:
| Your Situation | How Titan Helps |
|---|---|
| Training video generation models | Delivers 31 Gbps monthly (4K/8K/10h+ videos) directly to your cloud - you get the data without building or maintaining collection pipelines |
| Need 100K+ videos monthly | 99.8% delivery success using 3.8M+ residential IPs - eliminates the IP blocks and throttling that stall DIY collection at scale |
| Evaluating build vs buy | 30-50% cheaper than building when engineering costs are included - no proxy management, scraper maintenance, or pipeline debugging |
| Collecting YouTube data at scale | Purpose-built for video platforms - handles YouTube’s aggressive anti-bot systems (they ban datacenter IPs after 50-100 requests and throttle individual IPs after 100 requests/hour) |
| Training multimodal models | Complete datasets delivered together: video + audio + metadata + transcripts in training-ready formats - not separate sources requiring weeks to sync and reformat |
Explore more:** Top 5 YouTube data collection solutions | How to collect video training data for AI | Residential proxy networks for video data
FAQ
How do AI video companies source video training data at scale?
Most enterprise teams face a straightforward choice: build internal collection pipelines or work with a managed provider. Building internally works, until it doesn’t. At scale, collection infrastructure requires dedicated engineering, ongoing maintenance, and residential proxy networks that most model teams aren’t set up to run. Managed providers like Titan handle the full acquisition layer so ML teams stay focused on model work.
What does managed video data collection actually mean?
It means Titan handles everything between “we need public video data” and “the data is in our cloud storage ready to use.” Collection, infrastructure, IP rotation, quality assurance, format packaging, and delivery. The customer defines requirements. Titan executes. The data arrives.
How does Titan handle data provenance and copyright concerns?
Titan’s collection infrastructure is built around publicly available sources with transparent acquisition methods. For AI video teams facing increasing scrutiny around training data origins - particularly as regulators and platforms pay closer attention to how models are trained — working with a provider that collects cleanly and transparently matters.
Is this relevant for teams building models similar to Kling AI, Runway, or Sora?
Yes. If your team is training or fine-tuning video generation models and needs consistent access to large volumes of public video data, the operational challenge Kling AI faced is the same one you’ll face. Scale, format requirements, and delivery preferences may differ - but the underlying need is the same.
What does getting started look like?
Titan scopes engagements starting with a defined pilot - typically 10TB evaluation delivery - before moving into full production. The pilot validates quality and fit before larger commitments.
Work With Titan
If your team needs public video data at scale - reliably delivered, properly formatted, and ready to use - talk to us about what that looks like for your workflow.
→ Request a sample delivery plan
→ Talk to Titan about your data pipeline
