What are the performance trade-offs of the DSPy MIPROv2 optimizer?

All answers to this question.

MIPROv2 is definitely more resource-intensive because it explores a much larger space of potential instructions and examples using a Bayesian approach. For a simple FAQ bot, it might be overkill. However, if you are building an agent that needs to handle high-variance inputs with very high precision, the "training" cost pays for itself in the long run. We found that MIPROv2 was able to squeeze an extra 12% accuracy out of a smaller model like Mistral-7B, making it competitive with GPT-4 for our specific task. Think of it as a one-time R&D cost that significantly lowers your inference costs and improves user satisfaction over time.

   Answered 2025-12-15 by Julia Bennett

Does the DSPy compiler cache the results of these optimization runs? I wouldn't want to accidentally re-run a $50 MIPROv2 compilation every time I restart my dev server.

   Answered 2025-12-20 by Samuel Wright

• Yes, you can save the compiled program to a JSON file. Once it’s optimized, you just load the weights and instructions into your production instance. You only ever need to re-compile if your data distribution changes or if you update the core logic of your DSPy modules. It’s exactly like saving a trained model in traditional machine learning.

     Commented 2025-12-22 by Larry Scott

I’ve found that even the basic BootstrapFewShotWithRandomSearch gets you 90% of the way there for a fraction of the token cost compared to MIPROv2.

   Answered 2025-12-30 by Cynthia Taylor

• Agreed. Start simple and only move to the heavy-duty DSPy optimizers if you hit a performance plateau that you can't solve with more training data.

     Commented 2025-12-31 by Nathan Drake