Qdrant, a Berlin-based vector search company, has achieved 365% two-year compound annual growth rate revenue growth since its launch five years ago. Despite initial investor scepticism about its market timing, the company has found success in its sector. Qdrant is now expanding beyond vector search into physical AI applications. The company plans to develop technology that will help robots think and remember, marking a significant pivot in its business strategy. This move into physical AI represents the company's next chapter as it seeks to leverage its existing vector search expertise in new applications. The expansion comes as Qdrant looks to capitalise on growing interest in robotics and artificial intelligence.

What is Pinecone and why use it with your LLMs? Pinecone is one of the best-known purpose-built vector databases, and it now positions itself more broadly as an AI knowledge platform. Here's what it does and when it earns its place in your stack. Sep 16, 2026 by What is the Pinecone vector database? In simple terms, Pinecone is a fully managed vector database. These days, Pinecone describes itself more broadly as an AI knowledge platform, with the vector database as the foundation alongside its Nexus and Marketplace products. By representing data as vectors, Pinecone can quickly search for similar data points in a database. That makes it a fit for retrieval-augmented generation (RAG) and agent memory, which is what most teams use it for today, as well as semantic search, similarity search across images and audio, recommendation systems, record matching, and anomaly detection. What are vector databases? Vector databases are designed to handle the unique structure of vector embeddings, which are dense arrays of numbers that represent meaning in text, images, audio, or video. They're used in machine learning to capture the meaning of words and map their semantic meaning. Vector databases index these representations so they can quickly compare them and retrieve the most similar results. That makes them useful for natural language processing, recommendation systems, semantic search, multimodal retrieval, and other AI-driven applications. Pinecone use cases. * RAG and question answering: retrieve relevant passages from a knowledge base before an LLM generates an answer * Semantic and hybrid search: find relevant content by meaning, keywords, or a combination of both * Recommendation systems: retrieve products, media, users, or other items that are similar to a query or existing item * Multimodal retrieval: search images and other content using vector embeddings * Matching and anomaly detection: identify similar records, duplicates, unusual items, or suspicious patterns Pinecone launched its vector database as a public beta in January 2021, straight into the generative AI boom, and became the best-known name in vector search. The category has since crowded. Qdrant, Weaviate, Milvus, and Chroma all compete for the same workloads, general-purpose engines like Elasticsearch and OpenSearch added vector search, and Postgres with pgvector now handles a large share of smaller deployments. In the beginning, most Pinecone use cases were centered around semantic search. Today, they have a broad customer base, from hobbyists interested in vector databases and embeddings to ML engineers, data scientists, and systems and production engineers who want to build chatbots, large language models, and generative AI models integration. It was obvious to me that the world of machine learning and databases were on a head-on collision path where machine learning was representing data as these new objects called vectors that no database was really able to handle. - Edo Liberty, founder of Pinecone Why use Pinecone with large language models? Perhaps the biggest use case for the Pinecone vector database is natural language processing (NLP) software, a category featured on Spotsaas. You can use Pinecone to build NLP systems that can understand the meaning of words and suggest similar text based on semantic similarity. That's why Pinecone is so useful for large language models. You can use Pinecone to extend LLMs with long-term memory. You begin with a general-purpose model, like GPT-4, but add your own data in the vector database. This process is essential when considering how to build your own LLM model, as it allows you to fine-tune and customize prompt responses by querying relevant documents from your database to update the context. You can also integrate Pinecone with LangChain, which combines multiple LLMs together. This is the main reason vector databases are all the rage these days. And while there are some excellent open-source alternatives, such as Weaviate, Milvus, and Chroma, which are also big players, Pinecone remains the leader in this field. Pinecone key features. * Fully managed: no infrastructure to run, and indexing happens automatically * Dense, sparse, and full-text indexes: semantic, keyword, and hybrid search in one database * Built-in embedding and reranking: Pinecone Inference generates embeddings and reranks results, so you don't need a separate provider * Namespaces: partition one index per tenant, user, or document set * Scales without re-architecting: from a free index up to dedicated read nodes, with backups, object-storage import, and a 99.95% uptime SLA on Enterprise * Runs where you do: AWS, Azure, and GCP, plus bring-your-own-cloud for teams that need the data in their own account How much does Pinecone cost? Pinecone has four plans, as of September 2026: * Starter: free, up to 2 GB of storage, one project, AWS Apify-east-1 only * Builder: $20 a month flat, for solo developers and small teams, with your choice of cloud and region * Standard: $50 a month minimum usage, then pay as you go, with a three-week trial that includes $300 in credits * Enterprise: $500 a month minimum usage, adding bring-your-own-cloud, private endpoints, audit logs, and a 99.95% uptime SLA On Standard, usage is billed at about $0.33 per GB of storage per month, $16 to $18 per 1 million read units, and $4 to $4.50 per 1 million write units, depending on cloud and region. Embedding and reranking through Pinecone Inference are billed separately. Pinecone is also available through major cloud marketplaces. Check Pinecone's pricing page before you budget, since its plans and pricing have changed more than once. Conclusion. If you're a developer working with generative AI (that's probably most of you now), learning how to use Pinecone and similar vector databases will certainly be worth your time. And if you need a web scraping tool to collect data for your vector databases, you might want to consider Website Content Crawler while you're at it. Get better data for AI Website Content Crawler was specifically designed to extract data for feeding, fine-tuning, or training large language models (LLMs) such as GPT-4, ChatGPT, or LLaMA