What are people using for low-latency autocomplete in production? [P]

Our take

In the quest for low-latency autocomplete solutions, understanding the landscape of available systems is crucial. Users often turn to full search backends like Elasticsearch and Meilisearch for robust capabilities. However, LLM-based suggestions offer flexibility, albeit with slower response times per keystroke. Simpler prefix or n-gram systems provide speed but may sacrifice suggestion quality. As organizations evaluate their needs for low latency, acceptable suggestion quality, and minimal infrastructure overhead, insights into current production setups and the balance between classical methods and hybrid approaches become invaluable.

I’ve been looking into autocomplete/typeahead systems recently, especially in contexts where latency really matters (e.g. search-as-you-type or RAG pipelines).

From what I can tell, the main approaches are:

Full search backends (Elasticsearch, Meilisearch, etc.)
LLM-based suggestions (flexible but slow per keystroke)
Simpler prefix / n-gram systems (fast but sometimes limited)

I’m trying to understand what people actually use in production when you need:

very low latency
reasonable suggestion quality
minimal infra overhead

Are most systems still based on classical methods, or are people moving toward hybrid approaches (retrieval + reranking)?

For context, I’ve been experimenting with a small local implementation here:
https://github.com/MarcellM01/query-autocomplete

Not trying to replace full search systems, more to understand where the practical tradeoff line is between latency and quality.

Would be really interested to hear what setups people are running and what worked/didn’t.

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#rows.com#cloud-based spreadsheet applications#natural language processing for spreadsheets#generative AI for data analysis#Excel alternatives for data analysis#financial modeling with spreadsheets#low latency#autocomplete#full search backends#Elasticsearch#search-as-you-type#typeahead#Meilisearch#suggestion quality#LLM-based suggestions#hybrid approaches#RAG pipelines#prefix systems#infrastructure overhead#classical methods