Problem: MongoDB Atlas charges for data egress when connections route over the public internet. For AI/ML workloads involving embeddings and vector search, this creates substantial operational costs because:

Embedding operations involve frequent, high-volume reads and writes of vector data

Each query or upsert of embeddings incurs egress charges

Vector search workloads are data-intensive by nature — a single embedding operation can transfer kilobytes to megabytes of vector data

At scale (thousands of embeddings per day), egress costs can exceed the database subscription itself

Solution: PrivateLink or VPC peering would enable private network connectivity from Railway to MongoDB Atlas, eliminating egress charges entirely for embedding and vector operations. This would make MongoDB Atlas economically viable for embedding-heavy AI applications on Railway.

Impact: Many users deploying LLM/RAG applications on Railway rely on MongoDB Atlas for vector storage, but the public egress costs make it prohibitively expensive compared to self-hosted or co-located solutions. Private connectivity would unlock MongoDB Atlas as a practical choice for these workloads.