Dimensionality Trade-offs
Higher dimensions (e.g., 1536) capture more nuance but require significantly more RAM/storage in a vector database like Pinecone or Qdrant. A 384-dimensional open-source model (like BGE-small) can often provide 95% of the retrieval performance at 25% of the storage cost.
Context Window Limits
Standard embedding models chunk text at 512 tokens. If your chunks are larger (e.g., full pages), you need models specifically trained for long contexts (e.g., Nomic Embed), or you must implement rigorous document chunking strategies before embedding.
Local vs API Embeddings
Because embedding generation is highly parallelizable and doesn't require auto-regressive generation, it is exceptionally fast to run locally. Self-hosting embedding models via ONNX or sentence-transformers is almost always preferable to paying API costs at scale.
Internal Resources
- Inference Cost Calculator
- Latency Estimator
- Tabular vs Deep Learning
- Local LLMs vs Managed APIs
- Time Series Baselines
- Tabular Model Selector
- Vision Architecture Selector
- VRAM Calculator
- Choosing Embeddings
- CNN vs ViT in 2024
- RLHF vs DPO
- RAG Chunking Strategies
- RAG Chunk Size Calculator
- Token Ratio Estimator
- Synthetic Data Generation
- Quantization Methods Explained
- Quantization Memory Savings
- Fine-Tuning vs LoRA
- LoRA Rank Calculator
- Multimodal Model Landscape