GigaOm has just released its latest Radar for Vector Databases, now in its third edition. The report evaluates 17 leading open source and commercial solutions using GigaOm’s structured framework, covering table-stakes capabilities, key features, emerging strengths and broader business criteria.

Earlier editions were published as Sonar reports, a format GigaOm uses for technologies still in early exploration. The move to the Radar format marks a significant shift: Vector databases have moved beyond experimentation and are now being adopted in mainstream production environments.

Driven by generative AI, vector search has become a core part of enterprise AI stacks. Major data management vendors, including Oracle, IBM, Microsoft and others, have added vector capabilities to their platforms. At the same time, vector pureplays continue to push the boundaries of retrieval performance, multimodality and relevance. GigaOm’s Radar captures this rapid evolution across both categories.

From the report, it’s clear that distinct buyer segments are emerging. On one side are large enterprises extending their existing data platforms with vector features to support early retrieval-augmented generation (RAG) projects, semantic search or grounding large language models (LLMs) with internal knowledge.

These solutions fit neatly into established ecosystems and bring strong governance and compliance. For CIOs who want to stay aligned with their current suppliers, they are a practical choice for employee-facing GenAI use cases where performance and accuracy don’t need to reach production-grade levels.

At the other end of the spectrum is a category I’d describe as AI search platforms — systems built for customer-facing applications where search, ranking and retrieval are core to the product experience. Think Perplexity-style conversational search, Spotify-scale recommendations or large-scale personalization.

These platforms go beyond vector search by combining retrieval with integrated ranking pipelines, multimodal search, model inference and distributed execution. In scenarios where accuracy, latency and scale are mission-critical, this class of system is essential. Vespa is one example of this type of platform.

Sitting between these two ends of the market are the vector pure-plays, including Pinecone, Weaviate and Milvus. These platforms shine when teams want to get moving quickly. Most offer serverless or SaaS experiences with minimal friction — spin up an endpoint, embed content, test a RAG prototype and see results immediately.

They’re excellent for pilots, experimentation and departmental use cases. But as projects mature and workloads become more complex or customer-facing, many teams run into issues: integrating external ranking pipelines, tuning hybrid retrieval, managing multimodality or scaling reliably under higher query loads. These challenges don’t diminish their value in pilots, but they do help explain why some organizations outgrow pure-play vector databases as they move into full production.

Across all segments, one theme stands out: Vector storage alone isn’t enough. Effective AI applications increasingly rely on hybrid retrieval, advanced ranking, multimodal embeddings and techniques that integrate vector search with broader context. These trends, together with the technical considerations behind them, are explored in detail in the full GigaOm Radar.

What’s Next?

Generative AI is reshaping both customer experiences and employee workflows. Workers now expect the intuitive, AI-powered tools they use at home to be available at work. But delivering accurate, trustworthy answers at scale across fragmented enterprise data remains a real challenge.

Mainstream data platforms like Snowflake, Redshift, Oracle and PostgreSQL have added basic vector capabilities, making them “good enough” for internal GenAI search where latency and accuracy are less stringent.

Meanwhile, advanced customer-facing scenarios, supporting deep research, interactive assistants, personalization and large unstructured search spaces, require far more: integrated ranking, low-latency retrieval, multimodal support and large-scale performance. This is where AI search platforms come into play.

In this landscape, vector pure plays risk being caught in the middle — challenged by data platforms on the low end and by integrated AI search platforms on the high end. The market is maturing quickly, and buyers are becoming clearer about which architectural category fits which use case.

Taken together, these trends highlight just how quickly the vector landscape is evolving and why the latest GigaOm Radar is such a helpful resource. The report provides a structured, vendor-neutral view of where each solution fits today, what capabilities matter most and how the space is likely to develop over the next 12 to 18 months.

Whether you’re experimenting with early RAG prototypes, extending existing enterprise data platforms or building search-centric AI applications, the Radar offers a grounded framework to help teams make more informed decisions. I encourage anyone exploring this space to dive into the full report for a deeper, more comprehensive assessment.

You can download a copy of report here.

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