DAKERADakera vs Qdrant
Both Dakera and Qdrant are written in Rust and offer gRPC + REST APIs. But they solve different problems: Qdrant is a high-performance vector search engine with advanced filtering, while Dakera is a memory engine that uses vector search as one component of a broader memory system.
Feature Comparison
| Feature | Dakera | Qdrant |
|---|---|---|
| Category | AI Agent Memory Engine | Vector Search Engine |
| Language | Rust | Rust |
| Vector Search | HNSW (part of hybrid retrieval) | HNSW with custom modifications (highly optimized) |
| Full-text Search | BM25 built-in, fused with vector via RRF | Full-text index (basic, not BM25-level) |
| Filtering | Metadata filtering on recall | Advanced payload filtering (nested, geo, range, match) |
| Reranking | On-device cross-encoder | Not built-in |
| Memory Decay | 6 strategies | Not available |
| Sessions | Full session management | Not available (collection-level only) |
| Knowledge Graph | Entity extraction, BFS traversal | Not available |
| Quantization | Not currently | Scalar, Product, Binary quantization |
| Multi-vector | Single embedding per memory | Named vectors (multiple per point) |
| Distributed | Single-node focus | Native sharding + replication |
| Embedding | On-device ONNX | Bring your own (FastEmbed optional) |
| SDKs | Python, TypeScript, Go, Rust | Python, TypeScript, Go, Rust, Java, C# |
| APIs | REST + gRPC | REST + gRPC |
| License | MIT SDKs, proprietary server | Apache 2.0 |
Architecture Differences
Dakera
A memory-first engine where HNSW vector search is one retrieval path. Queries go through hybrid retrieval (BM25 + vector), Reciprocal Rank Fusion, and cross-encoder reranking. On top of search, Dakera adds memory semantics: decay (memories fade over time), importance scoring, sessions, namespaces, and knowledge graphs. The architecture prioritizes memory intelligence over raw vector throughput.
Qdrant
A purpose-built vector search engine optimized for performance at scale. Qdrant's HNSW implementation includes custom optimizations for high throughput and low latency. Its standout feature is the advanced filtering system — nested conditions, geo-spatial queries, and payload indexes allow complex queries without sacrificing performance. Native support for sharding, replication, quantization (scalar/product/binary), and named vectors makes it production-ready at massive scale.
Deployment Model
| Aspect | Dakera | Qdrant |
|---|---|---|
| Self-hosted | Single binary (Docker, K8s, systemd) | Single binary (Docker, K8s, Helm) |
| Cloud | Coming soon | Qdrant Cloud (managed, multi-region) |
| Clustering | Single-node primary | Native distributed mode (shards + replicas) |
| Storage | Embedded (self-contained) | Disk-optimized with mmap + WAL |
| Snapshots | Export/import | Native snapshot + recovery |
Pricing Comparison
| Tier | Dakera | Qdrant |
|---|---|---|
| Open Source | Self-hosted free (MIT SDKs) | Fully open-source (Apache 2.0) |
| Self-hosted | $0 + infrastructure | $0 + infrastructure |
| Cloud | Coming soon | From $0.025/hr (1GB RAM node) on Qdrant Cloud |
When to Choose
Choose Qdrant if:
- You need a high-performance vector database at massive scale (billions of vectors)
- Advanced filtering (geo, nested, range) is critical to your queries
- You want fully open-source with Apache 2.0 license
- Native distributed mode with sharding and replication is required
- Quantization (scalar, product, binary) for memory efficiency matters
- Multi-vector support (named vectors per point) fits your use case
- You need a managed cloud offering with multi-region support
Choose Dakera if:
- You are building AI agents that need memory beyond vector search
- Memory decay, session management, and importance scoring are requirements
- Hybrid retrieval (BM25 + vector + cross-encoder reranking) improves your recall
- Knowledge graphs with entity extraction are part of your architecture
- On-device embedding generation (no external API) is important
- MCP integration for IDE-based AI workflows is needed
- You want a complete memory system without assembling multiple components
Verdict
Dakera delivers a complete agent memory engine — hybrid BM25 + HNSW vector search with cross-encoder reranking, 6 memory decay strategies, knowledge graphs with GLiNER extraction, AES-256-GCM encryption, and 83 MCP tools — all in a self-hosted 44 MB Rust binary scoring 87.6% on LoCoMo. Qdrant is one of the best vector search engines available — its Rust performance, advanced payload filtering, and native clustering make it excellent for large-scale similarity search and recommendation systems. Choose Dakera when your AI agents need memory with sessions, decay, and knowledge graph reasoning. Choose Qdrant when you need a blazing-fast, horizontally-scalable vector store with advanced filtering for RAG or recommendations.
Try Dakera Free
Memory engine with hybrid retrieval, decay, and knowledge graphs — built in Rust like Qdrant, but designed for agent memory.
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