DAKERADakera vs Weaviate
Weaviate is a versatile open-source vector database with a module system for vectorization, generative AI, and multi-modal search. Dakera is a purpose-built memory engine for AI agents. Both can store and search embeddings, but they are designed for very different primary use cases.
Feature Comparison
| Feature | Dakera | Weaviate |
|---|---|---|
| Category | AI Agent Memory Engine | Vector Database with Module System |
| Language | Rust | Go |
| Query API | REST + gRPC | GraphQL + REST |
| Vector Search | HNSW with hybrid BM25 + RRF fusion | HNSW + BM25 (hybrid search available) |
| Reranking | On-device cross-encoder (bge-reranker-base) | Reranker modules (Cohere, etc.) |
| Vectorization | On-device ONNX (MiniLM, BGE, E5) | Module-based (OpenAI, Cohere, HuggingFace, local transformers) |
| Memory Decay | 6 strategies | Not available |
| Sessions | Full session management + namespaces | Not available (tenant isolation via multi-tenancy) |
| Knowledge Graph | Entity extraction (GLiNER), BFS | Cross-references between objects |
| Generative | External LLM integration (optional) | Generative modules (RAG built-in) |
| Multi-modal | Text-focused | Text, image, video, audio (via modules) |
| Multi-tenancy | Namespaces + scoped API keys | Native multi-tenancy (class-level isolation) |
| SDKs | Python, TypeScript, Go, Rust | Python, TypeScript, Go, Java |
| License | MIT SDKs, proprietary server | BSD-3-Clause |
Architecture Differences
Dakera
A focused memory engine with a clear mission: store, retrieve, and manage AI agent memories intelligently. All ML inference (embedding, reranking, entity extraction) happens on-device via ONNX — no external API calls needed. The architecture is intentionally narrow: do memory well rather than being a general-purpose data platform.
Weaviate
A highly extensible vector database built around a module system. Weaviate's core strength is flexibility: plug in different vectorization modules (OpenAI, Cohere, local transformers), add generative AI modules for RAG, support multi-modal data (images, audio), and query via GraphQL. The architecture is broader — it is a platform for AI-native applications, not specifically agent memory. It supports hybrid search (BM25 + vector) natively, making it one of the more capable vector databases for text retrieval.
Deployment Model
| Aspect | Dakera | Weaviate |
|---|---|---|
| Self-hosted | Single binary (Docker, K8s, systemd) | Docker, Kubernetes (Helm), requires modules |
| Cloud | Coming soon | Weaviate Cloud (WCD) — managed, serverless option |
| Dependencies | None (self-contained) | Module containers (vectorizer, generative, etc.) |
| Complexity | Single process | Core + module sidecars (can be complex) |
| Scaling | Vertical + manual horizontal | Horizontal (replication + sharding) |
Pricing Comparison
| Tier | Dakera | Weaviate |
|---|---|---|
| Open Source | Self-hosted free (MIT SDKs) | Fully open-source (BSD-3-Clause) |
| Self-hosted | $0 + your infra | $0 + your infra (+ module API costs) |
| Cloud | Coming soon | Serverless: from $25/mo; Dedicated: from ~$150/mo |
Note: Weaviate's self-hosted cost can be higher due to module dependencies (e.g., if using OpenAI vectorizer, you pay OpenAI per-token). Dakera's on-device inference eliminates this entirely.
When to Choose
Choose Weaviate if:
- You need multi-modal search (images, video, audio alongside text)
- GraphQL is your preferred query language
- You want a module ecosystem for different vectorization and generative backends
- Built-in RAG (generative search) is important for your application
- You want a fully open-source BSD-licensed vector database
- Multi-tenancy at scale with horizontal sharding is a requirement
- Your use case extends beyond agent memory (product search, recommendation, etc.)
Choose Dakera if:
- Your primary use case is AI agent memory (not general vector search)
- Memory decay, importance scoring, and session management are requirements
- You want zero external dependencies — no module sidecars, no API keys for vectorization
- Operational simplicity matters (single binary vs multi-container deployment)
- You need MCP integration for IDE-based AI workflows (83 tools)
- Knowledge graphs with entity extraction (GLiNER, BFS) are part of your design
- You prefer gRPC over GraphQL for programmatic access
Verdict
Weaviate is a powerful, versatile vector database — its module system, GraphQL API, and multi-modal support make it excellent for broad AI application development. If you need to search images, generate RAG responses, and plug in different vectorizers, Weaviate's extensibility is compelling. Dakera is the better choice when your specific need is intelligent agent memory with decay, sessions, and knowledge graphs, deployed with minimal operational overhead. The trade-off is breadth (Weaviate) vs depth in memory-specific features (Dakera).
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Purpose-built for agent memory. Single binary, no modules, no external API dependencies.
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