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 14 core MCP tools (86+ available via profiles) — all in a self-hosted 44 MB Rust binary scoring 88.2% 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.
Frequently Asked Questions
Both Dakera and Qdrant are written in Rust — what makes them different?
Shared language, different goals. Qdrant is a vector search engine optimized for throughput, advanced payload filtering (nested, geo, range operators), quantization, and native clustering. Dakera is a memory engine that adds BM25 full-text search fused with HNSW vectors, cross-encoder reranking, memory decay, knowledge graphs, and session management. Qdrant finds similar vectors fast; Dakera remembers intelligently.
Does Qdrant's vector search outperform Dakera's?
For raw ANN throughput and filtering at scale, yes — Qdrant is specifically optimized for vector search with scalar/product/binary quantization and SIMD acceleration. Dakera trades some vector-only speed for hybrid retrieval quality: combining BM25 keyword matching with HNSW vectors via RRF fusion and cross-encoder reranking. Dakera scores 88.2% on LoCoMo, which measures retrieval quality for agent memory rather than raw vector throughput.
Can I use Qdrant as the vector backend and Dakera for memory logic?
Not currently — Dakera uses its own embedded HNSW index and cannot delegate vector search to an external engine. They are separate systems solving overlapping but distinct problems. If you need Qdrant's advanced filtering and scaling for a RAG pipeline plus Dakera's memory features for agents, you would run both independently for their respective workloads.
Does Dakera have a managed cloud option like Qdrant Cloud?
Not yet — Dakera Cloud is planned but currently Dakera is self-hosted only. Qdrant Cloud offers a fully managed, horizontally-scaled service with enterprise SLAs. If managed deployment with zero ops is critical today, Qdrant Cloud is available. Dakera's self-hosted model means a single binary on any Linux server deployable in under 5 minutes.
How does Qdrant's payload filtering compare to Dakera's metadata filtering?
Qdrant's filtering is significantly more advanced — nested conditions, geo-radius, datetime ranges, full-text on payloads, and match-any/match-except operators. Dakera's metadata filtering supports key-value matching on recall operations but is simpler in scope. If complex, multi-dimensional filtering is core to your use case, Qdrant is stronger. Dakera compensates with hybrid BM25+vector retrieval and memory-specific features like decay and importance scoring.
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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