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 (14 core tools (86+ available via profiles))
- Knowledge graphs with entity extraction (GLiNER, BFS) are part of your design
- You prefer gRPC over GraphQL for programmatic access
Verdict
Dakera focuses deeply on agent memory — offering hybrid BM25 + HNSW vector search with cross-encoder reranking, 6 memory decay strategies, knowledge graphs with GLiNER entity extraction, and 14 core MCP tools (86+ available via profiles) in a single 44 MB Rust binary scoring 88.2% on LoCoMo with AES-256-GCM encryption at rest. Weaviate is a powerful, versatile vector database — its module system, GraphQL API, and multi-modal support make it genuinely excellent for broad AI application development where you need to search images, plug in different vectorizers, and generate RAG responses. Choose Dakera when you need deep agent memory features with minimal operational overhead. Choose Weaviate when you need an extensible, multi-modal vector database for diverse AI workloads.
Frequently Asked Questions
Can Dakera handle images and multi-modal data like Weaviate?
No. Dakera is text-focused — it stores and retrieves text memories with on-device embedding generation. Weaviate supports multi-modal vectorization (images, video, audio) via its module system. If your use case requires searching across images or combining text with visual data, Weaviate is the better fit. Dakera excels specifically at AI agent text memory with decay and session semantics.
Does Dakera support GraphQL queries like Weaviate?
Dakera uses REST and gRPC APIs, not GraphQL. Weaviate's GraphQL interface offers flexible querying with nested filters and aggregations. Dakera's API is purpose-built for memory operations — store, recall, search, session management — which are simpler operations that don't require GraphQL's query flexibility. The trade-off is simplicity vs expressiveness.
Why choose a single-binary memory engine over Weaviate's module ecosystem?
Operational simplicity. Weaviate's modules add vectorizers, generators, and readers as separate services — powerful but complex to operate. Dakera ships everything in one 44 MB binary: embedding model, BM25 index, HNSW graph, knowledge graph, and cross-encoder reranker — zero external dependencies, zero module configuration. Deploy in under 5 minutes vs configuring a module stack.
How do Weaviate Cloud and Dakera's self-hosted model compare?
Weaviate Cloud is a fully managed offering with enterprise support, SLAs, and horizontal scaling available today. Dakera is currently self-hosted only (Dakera Cloud is coming soon). If you need a managed service with enterprise guarantees right now, Weaviate Cloud is ready. If you prefer full data control and purpose-built agent memory features, Dakera's self-hosted deployment works on any Linux server.
Can I use Weaviate's vectorizer modules with Dakera?
No — Dakera generates embeddings on-device using ONNX Runtime with zero external API calls. Weaviate lets you plug in OpenAI, Cohere, or local model modules for vectorization. Dakera's approach means no API costs and no network latency for embedding, but you use the built-in model. Weaviate gives you model flexibility at the cost of external dependencies and per-call API charges.
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Purpose-built for agent memory. Single binary, no modules, no external API dependencies.
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