Qdrant: Provides high-performance open-source vector similarity search, requiring custom schema design for agent memory management. Qdrant records 72,992 monthly visits with -38.4% month-over-month growth and Freemium, Contact for Pricing pricing. supermemory records 156,579 monthly visits with -7.7% growth and Contact for Pricing pricing.
Open-source, high-performance vector search at any scale
A memory and continual-learning engine that gives any AI agent persistent context
| Dimension / Metric | | |
|---|---|---|
| Official Domain | qdrant.io | supermemory.ai |
| Pricing Model | Freemium, Contact for Pricing | Contact for Pricing |
| Monthly Visits | 72.99K (72,992) | 156.58K (156,579) |
| MoM Traffic Delta | -45.42K (-38.4%) | -12.98K (-7.7%) |
| Primary Market & Share | 🇮🇳 India (46%) | 🇺🇸 United States (46.2%) |
| Direct Visits / mo | 43.79K | 71.16K |
| User Rating | 4.6 | 4.5 |
| Supported Platforms | Website | Website |
Open-source, high-performance vector search at any scale
Built entirely in Rust with SIMD and a custom Gridstore storage engine for fast, scalable vector search.
Combine dense and sparse vectors in one query, supporting BM25, SPLADE++ and miniCOIL for keyword-plus-vector retrieval.
Store metadata as JSON and apply advanced filters such as nested, text, geo and has_vector, with filters applied during HNSW traversal.
Asymmetric, scalar and binary quantization reduce memory usage by up to 64x while preserving search quality.
A memory and continual-learning engine that gives any AI agent persistent context
A dedicated model extracts, merges, infers and forgets across stored context so the memory graph keeps improving instead of simply accumulating.
Learnings link to the entities, sources and moments around them, storing not just content but a time-aware map of how facts relate.
Memory lives outside model providers and is used through APIs, SDKs, plugins and MCP, so context carries across models and tools.
Relevant memories are injected as tokens into the prompt at request time, giving agents relevant context without unbounded transcripts.
SpecialistFocuses on tool integrations and authentication for agents, complementing memory engines rather than storing cross-session context.
SpecialistFocuses on tool integrations and authentication for agents, complementing memory engines rather than storing cross-session context.
SpecialistWeb search API and crawler built for AI agents
SpecialistAI search and retrieval platform for modern apps and commerce
Free / Open-sourceOne open-source AI gateway for 100+ LLM providers
SpecialistThe universal context engine connecting agents to data, tools, and models
SpecialistOffers enterprise-grade scalable vector search and RAG storage, but lacks supermemory's automated agent memory graph abstractions.
Search foundation built from embeddings, rerankers, and reader APIs
SpecialistAI gateway, observability and guardrails for production LLM apps
Free / Open-sourceProvides open-source persistent user modelling for stateful agents, but focuses more on conversational psychology than universal vector graphs.
Free / Open-sourceLightning-fast search engine you can self-host or run in the cloud
Free / Open-sourceLightning-fast search engine you can self-host or run in the cloud
Fully managed Vector Lakebase and vector database for enterprise AI, built by the creators of Milvus
Fully managed Vector Lakebase and vector database for enterprise AI, built by the creators of Milvus
Free / Open-sourceDelivers robust open-source graph storage on object stores, but is a database rather than a turnkey agent memory API.
Free / Open-sourceDelivers robust open-source graph storage on object stores, but is a database rather than a turnkey agent memory API.
