Marqo Alternatives

marqo reviews and mentions

• Why You Shouldn’t Invest In Vector Databases?
  12 projects | dev.to | 24 Apr 2025

  In cases where a company possesses a strong technological foundation and faces a substantial workload demanding advanced vector search capabilities, its ideal solution lies in adopting a specialized vector database. Prominent options in this domain include Chroma (having raised $20 million), Zilliz (having raised $113 million), Pinecone (having raised $138 million), Qdrant (having raised $9.8 million), Weaviate (having raised $67.7 million), LanceDB (YC W22), Vespa, Marqo, and others. Many of these players have secured significant funding in recent years and are well-positioned to capture notable market share. These vector databases offer efficient storage, indexing, and similarity search functionalities for vectors. They often incorporate specific optimizations tailored for vector data, such as similarity search based on inverted indexes and efficient vector computations. As a result, they cater to the requirements of companies operating in areas like recommendation systems, image search, and natural language processing.

• Ask HN: What's your serverless stack for AI/LLM apps in production?
  1 project | news.ycombinator.com | 10 Jan 2025

  I have a hosted code-first agent builder platform in production, so I respond these question a lot from our customers.

  1. Probably the best is fly.io IMHO. It has a nice balance between running ephemeral containers that can support long running tasks, and quickly booting up to respond to a tool call. [1]

  2. If your task is truly long running, (I'm thinking several minutes), probably wise to put trigger [2] or temporal [3] under it.

  3. A mix of prompt caching, context shedding, progressive context enrichment [4].

  4. I'm building a platform that can be self-hosted to do a few of the above, so I can't speak to this. But most of my customers do not.

  5. To start with, a simple postgres table and pgvector is all you need. But I've recently been delighted with the DX of Upstash vector [5]. They handle the embeddings for you and give you a text-in, text-out experience. If you want more control, and savings on a higher scale, have heard good things about marqo.ai [6].

  Happy to talk more about this at length. (E-mail in the profile)

  [1] https://fly.io/docs/reference/architecture/

  [2] trigger.dev

  [3] temporal.io

  [4] https://www.inferable.ai/blog/posts/llm-progressive-context-...

  [5] https://upstash.com/docs/vector/overall/getstarted

  [6] https://www.marqo.ai/

• Pinecone integrates AI inferencing with vector database
  2 projects | news.ycombinator.com | 4 Dec 2024
• AI Search That Understands the Way Your Customer's Think
  1 project | news.ycombinator.com | 28 May 2024
• Are we at peak vector database?
  8 projects | news.ycombinator.com | 25 Jan 2024

  We (Marqo) are doing a lot on 1 and 2. There is a huge amount to be done on the ML side of vector search and we are investing heavily in it. I think it has not quite sunk in that vector search systems are ML systems and everything that comes with that. I would love to chat about 1 and 2 so feel free to email me (email is in my profile). What we have done so far is here -> https://github.com/marqo-ai/marqo

• Qdrant, the Vector Search Database, raised $28M in a Series A round
  8 projects | news.ycombinator.com | 23 Jan 2024

  Marqo.ai (https://github.com/marqo-ai/marqo) is doing some interesting stuff and is oss. We handle embedding generation as well as retrieval (full disclosure, I work for Marqo.ai)

• Ask HN: Is there any good semantic search GUI for images or documents?
  2 projects | news.ycombinator.com | 17 Jan 2024

  Take a look here https://github.com/marqo-ai/local-image-search-demo. It is based on https://github.com/marqo-ai/marqo. We do a lot of image search applications. Feel free to reach out if you have other questions (email in profile).

• 90x Faster Than Pgvector – Lantern's HNSW Index Creation Time
  7 projects | news.ycombinator.com | 2 Jan 2024

  That sounds much longer than it should. I am not sure on your exact use-case but I would encourage you to check out Marqo (https://github.com/marqo-ai/marqo - disclaimer, I am a co-founder). All inference and orchestration is included (no api calls) and many open-source or fine-tuned models can be used.

• Embeddings: What they are and why they matter
  9 projects | news.ycombinator.com | 24 Oct 2023

  Try this https://github.com/marqo-ai/marqo which handles all the chunking for you (and is configurable). Also handles chunking of images in an analogous way. This enables highlighting in longer docs and also for images in a single retrieval step.

• Choosing vector database: a side-by-side comparison
  3 projects | news.ycombinator.com | 4 Oct 2023

  As others have correctly pointed out, to make a vector search or recommendation application requires a lot more than similarity alone. We have seen the HNSW become commoditised and the real value lies elsewhere. Just because a database has vector functionality doesn’t mean it will actually service anything beyond “hello world” type semantic search applications. IMHO these have questionable value, much like the simple Q and A RAG applications that have proliferated. The elephant in the room with these systems is that if you are relying on machine learning models to produce the vectors you are going to need to invest heavily in the ML components of the system. Domain specific models are a must if you want to be a serious contender to an existing search system and all the usual considerations still apply regarding frequent retraining and monitoring of the models. Currently this is left as an exercise to the reader - and a very large one at that. We (https://github.com/marqo-ai/marqo, I am a co-founder) are investing heavily into making the ML production worthy and continuous learning from feedback of the models as part of the system. Lots of other things to think about in how you represent documents with multiple vectors, multimodality, late interactions, the interplay between embedding quality and HNSW graph quality (i.e. recall) and much more.

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