What's the most cost-effective way to classify tickets and feedback using a topic modeling software?

The most cost-effective approach is usually:

1. Start with open-source topic modeling

   • Use BERTopic, Guided Topic Modeling, or LDA if your text is fairly structured and you want low infrastructure cost.
   • If you need stronger semantic grouping, BERTopic with a small embedding model is often the best balance of quality and cost.

2. Use an embeddings API only if needed

   • If accuracy matters more than minimizing all costs, use a cheap embedding model once to vectorize all tickets/feedback, then cluster or model topics locally.
   • This often costs less than repeatedly sending text to a large LLM.

3. Keep preprocessing simple

   • Clean text, remove signatures/boilerplate, normalize synonyms, and deduplicate.
   • Better preprocessing reduces model size and manual review effort, which is often the biggest cost saver.

4. Choose semi-automated classification

   • Pure unsupervised topic modeling is cheap, but topic labels can be messy.
   • A more cost-effective operational setup is:
     • cluster topics automatically,
     • have humans label only the top clusters,
     • then use those labels for future tickets.
   • This gives you good accuracy without paying for full manual labeling.

5. Fine-tune only if volumes are high

   • If you classify lots of tickets every day and your categories are stable, training a lightweight classifier on labeled data can become cheaper than topic modeling over time.
   • But for exploratory categorization or changing feedback themes, topic modeling is usually cheaper upfront.

Best low-cost stack

• Embedding model: small/cheap model
• Topic model: BERTopic
• Storage/processing: local Python + pandas + scikit-learn
• Human-in-the-loop: label and merge topics after clustering

Practical recommendation

If your goal is lowest total cost with decent quality, I’d do:

• BERTopic + small sentence embeddings + human review of top clusters

If you want, I can recommend:

• the cheapest software options,
• a step-by-step implementation, or
• a cost comparison between BERTopic, LDA, and LLM-based classification.