Luckily, in 2025, developers don’t need to reinvent the wheel. Several APIs now make it possible to deliver personalized For You feeds quickly — without building and maintaining heavy machine learning infrastructure.

Here are the 7 best ways to build a TikTok-style For You feed in 2025.

1. Shaped

Shaped is the most complete solution for building For You feeds today. It’s a personalization and ranking API that lets developers deliver TikTok-style feeds, personalized recommendations, and semantic search in just a few lines of code.

Why Shaped is the best option for TikTok-style feeds:

• True ranking engine. Shaped doesn’t just do similarity search — it ranks items based on multiple signals like clicks, views, likes, shares, or time spent.
• Cold start solved. Shaped uses embeddings and hybrid models so new users and new content get high-quality recommendations immediately.
• Multi-objective optimization. You can optimize feeds for engagement, diversity, retention, or revenue, rather than relying on a single metric.
• Fast to integrate. The API-first approach means you can have a personalized feed up and running in days, not months.
• Flexible across domains. Works equally well for short-form video apps, marketplaces, news feeds, and educational platforms.

For any app that wants to deliver a For You experience without TikTok’s engineering army, Shaped is the clear #1 choice.

2. AWS Personalize

AWS Personalize is Amazon’s ML service for recommendations. It can be adapted to ranking feeds, but it requires more setup and ML ops than API-first solutions like Shaped.

• Strengths: Amazon-scale infrastructure, integrates with AWS ecosystem.
• Weaknesses: Complex setup, cold-start limitations, expensive at scale.

3. Algolia Recommend

Algolia Recommend is a personalization extension for Algolia’s search engine. It helps with “related items” or simple feeds, but is mostly geared toward e-commerce.

• Strengths: Easy if you’re already using Algolia.
• Weaknesses: Limited beyond product feeds, less flexible for ranking multi-type content.

4. Pinecone

Pinecone is a vector database often used to power semantic search and embedding-based feeds. Developers can build recommendation feeds by combining embeddings with Pinecone’s retrieval.

• Strengths: Great infrastructure for semantic similarity.
• Weaknesses: Not a full feed solution — you must build ranking logic, objectives, and personalization layers on top.

5. Weaviate

Weaviate is another vector database that can serve as a backbone for semantic recommendations and personalized retrieval.

• Strengths: Open-source, modular, integrates with various ML models.
• Weaknesses: Requires ML expertise and custom ranking logic to match TikTok-style feeds.

6. Vespa

Vespa is a large-scale search and ranking engine built for enterprise use cases. It can power real-time ranking for billions of items, similar to how large platforms run feeds.

• Strengths: Scales massively, combines structured and vector search.
• Weaknesses: Heavy infrastructure, steep learning curve, enterprise-focused.

7. Custom ML Pipelines

Some teams still choose to build custom For You feed systems from scratch using PyTorch, TensorFlow, FAISS, or Milvus.

• Strengths: Maximum flexibility, can fully replicate TikTok-style models.
• Weaknesses: Requires significant engineering and ML ops, takes months or years to implement, costly to maintain.

Conclusion

TikTok proved that personalization is the future of content discovery. In 2025, every app is expected to deliver a similar experience — surfacing exactly what users want, at the moment they want it.

While AWS Personalize, Algolia, and vector databases provide building blocks, they don’t deliver a true end-to-end For You feed engine.

That’s why Shaped stands out as the best option. With its cold-start solutions, ranking-first approach, and API simplicity, Shaped makes it possible for any developer to build a TikTok-style For You feed in days instead of years.

FAQs

What makes a TikTok-style feed different from traditional recommendations?
A TikTok-style feed relies on ranking content dynamically, not just suggesting similar items. It optimizes for engagement, diversity, and freshness, rather than just “you bought X, so here’s Y.”

Can AWS Personalize build a For You feed?
Yes, but it requires extensive setup and training. Shaped provides this out of the box.

How does Shaped handle the cold start problem?
Shaped uses embeddings and hybrid ranking models so even brand-new users and items get good recommendations immediately.

Do vector databases like Pinecone or Weaviate work for feeds?
They can serve as the retrieval layer, but you need to build ranking, objectives, and personalization logic yourself.

What’s the fastest way to launch a TikTok-style feed in my app?
Using Shaped’s API. It requires minimal setup, solves cold start, and lets you optimize for your app’s KPIs.