Choosing the right embedding model for vector search involves balancing multiple factors such as search quality, resource usage, language support, and cost constraints. While public benchmarks like MTEB can guide model selection, they may not reflect the nuances of domain-specific data, making it essential to evaluate models based on specific needs. Evaluating search quality is crucial and should be done before finalizing a model, taking into account the languages it supports and the effectiveness of its tokenizer. Embedding models serve different purposes—some excel in semantic similarity, others in retrieval or question answering—requiring precise task definition and a well-curated ground truth dataset for evaluation. Practical considerations like model size, sequence length, and infrastructure costs also play significant roles. Operational factors such as throughput, latency, and cost further influence model selection, with trade-offs necessary depending on the specific use case. As projects evolve, the need to revisit model choices may arise, and tools like Qdrant offer flexibility in managing multiple models and hosting options, including solutions like Cloud Inference for reducing latency and cost.