Freshservice VS Weaviate

Freshservice Reviews

Weaviate Reviews

Freshservice mentions (3)

• Outlook Helpdesk Form

  If you're fine with writing emails instead of filling in an Outlook form (as a user), then https://freshservice.com/ might work. Source: about 2 years ago

• Low Hanging Fruit - Recommendations for Ticketing System????

  FreshService is pretty good and ticks all the boxes you're looking for (https://freshservice.com/). Source: over 2 years ago

• SchoolDude as a help desk / ticketing system.

  If you're not capable of hosting the solution yourself, there are solutions that have per-agent models that will cost you much less than SchoolDude, all while being substantially more feature rich. osTicket and FreshService are both great examples. A cloud hosted instance of osTicket is only $9/agent/month. FreshService is a more polished solution, but costs more at $19/agent/month. Source: about 3 years ago

Weaviate mentions (28)

• How to choose the right type of database

  Weaviate: An open-source, cloud-native vector database built for scalable and fast vector searches. It's particularly effective for semantic search applications, combining full-text search with vector search for AI-powered insights. - Source: dev.to / 2 months ago

• 7 Vector Databases Every Developer Should Know!

  Weaviate is an open-source vector search engine with out-of-the-box support for vectorization, classification, and semantic search. It is designed to make vector search accessible and scalable, supporting use cases such as semantic text search, automatic classification, and more. - Source: dev.to / 3 months ago

• Qdrant, the Vector Search Database, raised $28M in a Series A round

  Congrats to them! What have your experiences with vector databases been? I've been using https://weaviate.io/ which works great, but just for little tech demos, so I'm not really sure how to compare one versus another or even what to look for really. - Source: Hacker News / 3 months ago

• How Modern SQL Databases Are Changing Web Development - #4 Into the AI Era

  A RAG implementation's quality and performance highly depend on the similarity-based search of embeddings. The challenge arises from the fact that embeddings are usually high-dimensional vectors, and the knowledge base may have many documents. It's not surprising that the popularity of LLM catalyzed the development of specialized vector databases like Pinecone and Weaviate. However, SQL databases are also evolving... - Source: dev.to / 4 months ago

• Make Notion search great again: Vector Database

  To find semantically similar texts we need to calculate the distance between vectors. While we have just a few short texts we can brute-force it: calculate the distance between our query and each text embedding one by one and see which one is the closest. When we deal with thousands or even millions of entries in our database, however, we need a more efficient way of comparing vectors. Just like for any other way... - Source: dev.to / 6 months ago