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TopicGoalCurrent PerceptionStrategic NarrativeKey Messages
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EnablementReduce the friction for developers to adopt, integrate, and succeed with Weaviate by providing the tools, flexibility, and ease-of-use.Weaviate is seen as complex and difficult to optimize.Weaviate is an open source and AI database that enables developers to bring their prototypes to production easily.AI-native: Integrates with embedding/reranker models. Automatic vectorization saves boilerplate code.
Ease of learning: Comprehensive docs & tutorials.
Academy, Quickstart, Recipes
Open source: Customization and self-hosting (Docker/K8s).
K8s Guide, Docker Guide
Migration: Guide
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Strong CommunityDemonstrating that developers are supported in their journey.N/AWeaviate is not just a product; it’s a living, open-source project powered by a responsive core team and driven by a thriving developer community.Direct developer assistance via Forum.
Open source community on GitHub.
Integrated in the Generative AI stack ecosystem.
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Performance at scalePosition Weaviate as a high-performance vector database constantly innovating (vector search, filtered search, hybrid search) for data at any scale.Perceived as slow at scale due to old GraphQL/REST benchmarks and outdated competitor comparisons.Weaviate delivers high performance at scale, not only for vector search but also for filtered vector search.gRPC-driven improvements: Strong performance boost since Feb 2024. Blog.
Fast filtered search: Uses ACORN for speed. Blog.
Philosophy: Benchmarks should show expected latency, not just competitor comparison.
Rankings: Consistently top 3 (e.g., #2 on Qdrant benchmark). Benchmarks.
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CostCounter the perception of being expensive by demonstrating superior total value.More expensive per 100M vectors than competitors.(WIP by Alvin)Updated pricing strategy: See Cost calculator.
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Hybrid SearchJustify BM25 + vector search combination as a superior form of hybrid search.Immature feature (lack of sparse embeddings).Weaviate’s hybrid search combines BM25 and vector search instead, which leads to better results.BM25 can be more effective than sparse models on keyword search.
For effective hybrid search, you need BM25 anyway.
Improve the dense model instead of adding sparse complexity. Explained.
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Weaviate AgentsPosition Weaviate Agents as the solution to query, improve, and augment data with AI agents.(Unawareness)Query, improve, and augment your data with the power of agents.Better outcomes: Minimize errors/bias, adapt to changing data.
Accelerate insights: No SQL queries needed.
Simplify dev: Save time on querying/enriching.
Query agent tutorial.
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Weaviate EmbeddingsPosition Weaviate Embeddings as the solution for friction-free AI development.(Unawareness)Managed embedding inference service in Weaviate Cloud that eliminates friction in AI development.Freedom: No rate limits, easy model swapping.
Performance: High throughput, no artificial constraints.
Efficient: Co-located models and data. Blog.
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AI Memory LayerPosition Weaviate as the simplest, most effective memory layer for persistent & adaptive AI context.N/AWeaviate is the indispensable memory layer that gives your AI applications and agents true long-term context.Builders shouldn't manage low-level storage.
Mem0: Docs.
CrewAI: Docs.
GraphRAG: With Neo4j.
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