Vector Database Services

Get individual support for your organization’s vector database developments.

Vector databases like Qdrant and Milvus are purpose-built to store and search high-dimensional data, making them essential for machine learning applications that rely on similarity search - such as recommendation systems, semantic search, and generative AI. Unlike traditional databases, they’re optimized for speed and scale when working with embeddings from models like OpenAI, Hugging Face, or custom ML pipelines.

Vector database expertise when you need it most. Our team has a proven track record solving critical embedding and similarity search challenges — contact us to optimize your vector infrastructure and accelerate query performance.

Vector database implementation challenges

Setting up effective retrieval systems using vector databases requires navigating numerous technical decisions that can significantly impact performance, scalability, and accuracy. The following points outline key challenges organizations face when implementing vector search solutions: Setting up retrieval systems based on embedding search via vector databases is challenging due to the technical decisions involved.

  • Which vector database is suitable for a client's existing infrastructure

    • Depending on the size of the document collection and other considerations, either a dedicated vector db like qdrant or milvus might be preferable or using existing databases with plugins like pgvector

  • What embeddings to use for a particular problem

    • What modalities should be supported (image/text, multi-language)

    • Should the embedding be finetuned

  • What optimization parameters to use for the best performance/quality compromise

    • Modern vector database use quantization and special indexing methods (e.g. HNSW, IVF or LSH) to enable fast retrieval with high accuracy. Finding the correct parameters for an appropriate accuracy/speed trade-off can be challenging since it requires deep knowledge of the employed methods

  • How to deploy vector databases for redundancy and horizontal/vertical scaling

  • How to set up a robust injection pipeline to keep vector collections up-to-date

dida services

We provide comprehensive solutions for organizations seeking to implement and optimize vector database systems for their specific needs. Our expert team offers end-to-end support throughout the entire implementation lifecycle, ensuring you achieve maximum value from your vector search capabilities. Our specialized services include:


End-to-end vector database implementation tailored to your infrastructure needs, including database selection, configuration, and optimization for the ideal accuracy/speed balance


Strategic embedding model selection and fine-tuning for domain-specific applications, ensuring optimal semantic representation


Comprehensive monitoring and performance measurement systems to track retrieval quality and detect data distribution shifts


Seamless integration of vector search capabilities into existing applications, including advanced solutions utilizing hybrid search, reranking models, and LLM agents

How it works

Our streamlined process ensures you receive expert machine learning support tailored to your specific needs, with a clear path from initial consultation to implementation.

We work with you through these key steps:

  • 1

    Book a 30min introductory meeting with one of our Machine Learning Engineers and tell us about your current situation / problem.

  • 2

    Based on your situation and requirements, dida will propose a lean plan on how to support your team quickly and efficiently (i.e. 1-3 days of vector database support for approx. 1-3K EUR).

  • 3

    Within the next three working days, an experienced Machine Learning Engineer with competences in vector database developments will start working on your problem.

  • 4

    After successful completion: Decide to expand the service, book a certain capacity per month or continue on your own. 


Who is this for

This service is best suited for data science and IT teams that…

  • are planning on working with vector databases and want dida to do the initial setup and integration into their projects, or

  • are already working with vector databases and desire consultation, development support or evaluations of already developed code.

About dida

dida is a software company from Berlin, Germany, specialized in the development of  individual machine learning services. The highly technical team not only trains and optimizes neural networks, it also sets up, maintains or optimizes vector databases like Qdrant or Milvus.

Frequently asked questions

  • Who will be working on our project?

    Depending on the desired support volume dida will provide you with 1-2 experienced Machine Learning engineers that have the most experience with this specific tool / technology / framework.

  • Who will be our main point of contact?

    The dida engineer who will lead the project will be the main point of contact so that respective engineers can directly communicate with each other.

  • How quickly can dida’s team help us address our current challenges with our vector database?

    fter signing the contract, dida guarantees to start within the next three working days

  • How will we communicate during the project?

    We’re open to your preferred choice of communication and organization (Email, Slack, MS Teams, Gitlab / GIthub issues, …)

  • Does dida work remotely or on-site?

    Most of dida’s work is typically performed from our office in Berlin, Germany. Nevertheless we regularly visit our clients for workshops, interim and final presentations or whenever the situation demands it. If on-site work is required, please let us know so that we can arrange it.

  • How often will we receive updates on the project?

    For short term support services you will be updated either daily or after every milestone.

  • What qualifications and experience does your team have?

    dida’s team comprises largely of scientists and engineers with backgrounds in mathematics and physics - many of them holding PhDs from prestigious institutions. The highly specialized team has 8 years of industry experience in implementing machine learning solutions. dida solves algorithmically complex problems and often tackles use cases where less specialized in-house departments previously failed. Amongst its clients are large European organizations such as Deutsche Bahn, Klöckner, Zeiss or APCOA.  

  • Are there any subcontractors involved in the service delivery?

    No, all purchased services will be provided by dida employees.

Contact us

Connect with our vector database experts to explore tailored solutions for your implementation needs. We’re here to help you optimize your retrieval architecture and embedding strategies with precision and expertise.