Selecting the right embedding model involves navigating complex trade-offs between performance, cost, and operational requirements. A systematic workflow helps you make informed decisions based on your specific needs.

The complexity of model selection

Every embedding model involves trade-offs. The most obvious is the relationship between model performance, size, and cost:

This chart shows a clear positive correlation between model size and retrieval performance. Generally:

• Larger models → Better performance → Higher costs and slower inference
• Smaller models → Adequate performance → Lower costs and faster inference

But size and performance aren't the only considerations. Other critical factors include:

• Deployment preferences: A proprietary model like Gemini may perform well but won't meet requirements for local inference
• Domain specificity: A model excelling at general benchmarks may underperform on specialized domains (legal, medical, coding)
• Operational complexity: Local models may be cheaper to run but require infrastructure expertise and maintenance

A systematic approach

To navigate this complexity effectively, we recommend a four-stage workflow:

Stage 1: Identify your needs

Clearly articulate requirements and preferences across four key dimensions:

• Data characteristics (modality, language, domain, length)
• Performance needs (accuracy, latency, throughput, volume)
• Operational factors (hardware, deployment, maintenance)
• Business requirements (hosting, licensing, budget, compliance)

Stage 2: Compile candidate models

Create a manageable list of potentially suitable models using efficient screening heuristics:

• Filter by required modality support
• Prioritize models already available in your organization
• Include well-known, industry-standard models
• Consider benchmark leaders from MTEB and similar leaderboards

Stage 3: Detailed evaluation

Run comprehensive evaluations using your own data and use cases:

• Design evaluation criteria aligned with your requirements
• Create representative test datasets
• Measure performance on your specific tasks
• Compare results across candidate models

Stage 4: Periodic re-evaluation

Establish ongoing processes to reassess your model choice:

• Monitor changes in your data, application, or requirements
• Stay informed about new models and improvements
• Set regular review schedules
• Plan for model migration when beneficial

Why this workflow works

This approach balances thoroughness with efficiency:

1. Systematic coverage: Ensures you consider all relevant factors
2. Efficient screening: Avoids evaluating hundreds of models in detail
3. Data-driven decisions: Uses your actual data and requirements, not just benchmarks
4. Adaptive approach: Maintains relevance as your needs and the model landscape evolve

The workflow recognizes that there's no universally "best" embedding model – only the best model for your specific requirements and constraints.

What's next?

Now that you understand the overall workflow, let's dive deeper into Stage 1: identifying your specific needs and requirements across all relevant dimensions.