# Qdrant vs supermemory

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.

| Metric | [Qdrant](https://openaitools.app/tool/qdrant-io) | [supermemory](https://openaitools.app/tool/supermemory-1) |
| :--- | :--- | :--- |
| Website URL | https://qdrant.tech/ | https://supermemory.ai |
| Categories | AI Search Engine, AI API, AI Developer Tools, AI Agent | AI API, Large Language Models (LLMs), AI Developer Tools, AI Agent |
| Pricing Model | Freemium, Contact for Pricing | Contact for Pricing |
| Monthly Visits | 72,992 | 156,579 |
| MoM Growth Rate | -38.4% | -7.7% |
| Primary Market | India | United States |
| Direct Visits | 43,793 | 71,163 |
| Review Score | 4.6 | 4.5 |
| Saved Count | 0 | 0 |
| Supported Platforms | Website | Website |

## Qdrant

Qdrant is an open-source vector database and similarity search engine written in Rust. It stores vectors alongside JSON metadata and answers nearest-neighbor queries so applications can retrieve relevant items by meaning rather than exact keywords. The engine includes a custom storage layer (Gridstore), real-time indexing without full rebuilds, and quantization techniques that reduce memory use dramatically for large collections.

For retrieval quality, Qdrant supports native hybrid search that blends dense and sparse vectors in a single query — with BM25, SPLADE++ and miniCOIL — plus multivector storage, one-stage filtering during HNSW traversal, and reranking options such as score boosting, ColBERT-style late interaction and MMR diversification. Common applications include RAG and GenAI pipelines, AI agents with persistent memory, semantic search, recommendation systems, and anomaly detection.

Deployment is flexible: run the open-source engine yourself, or use Qdrant Cloud (managed, usage-based, on AWS, GCP or Azure), Hybrid Cloud (managed clusters in your own Kubernetes), Private Cloud (air-gapped and compliant setups) or the newer Qdrant Edge for low-latency local search. Free-tier cloud clusters, SOC 2 and HIPAA aligned posture, and enterprise tooling like SSO, RBAC and Prometheus/Grafana integrations make it suitable for both prototypes and production.

**Pricing & Plans:** The open-source engine is free to self-host. Qdrant Cloud offers a free-forever tier (single node, 0.5 vCPU / 1GB RAM / 4GB disk), usage-based Standard pricing for production (compute, memory, storage plus paid inference tokens), and sales-led Premium, Hybrid and Private Cloud plans.

- **High-performance vector engine:** Built entirely in Rust with SIMD and a custom Gridstore storage engine for fast, scalable vector search.
- **Native hybrid search:** Combine dense and sparse vectors in one query, supporting BM25, SPLADE++ and miniCOIL for keyword-plus-vector retrieval.
- **Expansive metadata filtering:** Store metadata as JSON and apply advanced filters such as nested, text, geo and has_vector, with filters applied during HNSW traversal.
- **Memory-efficient quantization:** Asymmetric, scalar and binary quantization reduce memory usage by up to 64x while preserving search quality.
- **Developer-friendly APIs and Web UI:** Use REST, gRPC or official clients (Python, JavaScript and more), a built-in Web UI, and native cloud inference for text and image embeddings.

**Strengths:** Open-source core with a generous free cloud tier for prototyping; Strong performance and Rust-based engineering trusted by 35k+ GitHub stars; Native hybrid search and one-stage filtering simplify production retrieval; Deploy anywhere: cloud, hybrid, private or edge, plus SOC 2, HIPAA, SSO and RBAC tooling

**Limitations:** Requires engineering effort to set up and operate compared with turnkey managed services; Advanced capabilities like quantization, hybrid search and multivector retrieval assume solid technical understanding; Premium and private cloud tiers are sales-led with no published price list

[Profile (Markdown)](https://openaitools.app/tool/qdrant-io.md)

## supermemory

Most LLM applications treat memory as an afterthought: chat history is stuffed into the prompt until the window fills up, and everything is lost on the next session or the next model switch. supermemory attacks that problem as infrastructure. Its learner-1 model reads incoming material — conversations, files, URLs, raw data — and performs update, merge, infer and forget operations against a vector-graph database, so learnings stay dense, interconnected and time-aware rather than piling up as an ever-growing transcript.

At request time the relevant memories are injected as tokens directly into the agent's context, which keeps the approach model-agnostic and harness-agnostic. Because learning and memory live outside the model providers, the same accumulated understanding travels with an application whether it runs on Claude, GPT-family models, Gemini or open-weight models, and whether it is driven by an SDK, a plugin or MCP.

The company positions this as the hard part of agent infrastructure and backs it with published numbers: sub-second recall on production traffic, roughly a trillion tokens processed per month, benchmarks on LongMemEval, LoCoMo and ConvoMem, and cost comparisons against markdown-file memory at large corpora. Enterprise features include SOC 2 Type II certification, a HIPAA BAA on higher tiers, GDPR compliance and deployment options ranging from cloud to VPC, on-premises and air-gapped environments.

**Pricing & Plans:** Pricing is split between self-serve tiers and enterprise contracts, which is why the site lists a contact-for-pricing model. Free, Pro and Max tiers cover standard PDF extraction and personal use, while Scale and Enterprise add advanced document extraction with OCR and figure/diagram descriptions, higher limits, security guarantees such as SOC 2 Type II and HIPAA BAA, and deployment choices including your VPC, your data center or air-gapped setups. Exact figures should be confirmed on the pricing page for current rates.

- **learner-1 memory model:** A dedicated model extracts, merges, infers and forgets across stored context so the memory graph keeps improving instead of simply accumulating.
- **Vector-graph memory store:** Learnings link to the entities, sources and moments around them, storing not just content but a time-aware map of how facts relate.
- **Model, harness and MCP agnostic:** Memory lives outside model providers and is used through APIs, SDKs, plugins and MCP, so context carries across models and tools.
- **Real-time context injection:** Relevant memories are injected as tokens into the prompt at request time, giving agents relevant context without unbounded transcripts.
- **Enterprise deployment and compliance:** SOC 2 Type II, HIPAA BAA on higher tiers, GDPR compliance, plus deployment in the cloud, your VPC, your data center or air-gapped.

**Strengths:** Memory is decoupled from model providers, so accumulated context survives model swaps; Rich engineering evidence, including published benchmarks, latency figures and cost comparisons; Multiple integration surfaces — REST API, SDKs, plugins and MCP; Strong enterprise posture with SOC 2 Type II, HIPAA BAA, GDPR and air-gapped deployment options

**Limitations:** Self-serve pricing on the public site is thin, and exact tier costs may require a sales conversation; It is infrastructure rather than an end-user app, so there is real integration work before you see value; Benchmark results and adoption figures are vendor-reported and worth validating against your own workload

[Profile (Markdown)](https://openaitools.app/tool/supermemory-1.md)

## Frequently Asked Questions

### How do Qdrant and supermemory compare in monthly traffic?

Qdrant has 72,992 monthly visits, while supermemory has 156,579. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Qdrant has a month-over-month growth rate of -38.4%; supermemory has -7.7%.

### How does pricing compare?

Qdrant: Freemium, Contact for Pricing. The open-source engine is free to self-host. Qdrant Cloud offers a free-forever tier (single node, 0.5 vCPU / 1GB RAM / 4GB disk), usage-based Standard pricing for production (compute, memory, storage plus paid inference tokens), and sales-led Premium, Hybrid and Private Cloud plans. supermemory: Contact for Pricing. Pricing is split between self-serve tiers and enterprise contracts, which is why the site lists a contact-for-pricing model. Free, Pro and Max tiers cover standard PDF extraction and personal use, while Scale and Enterprise add advanced document extraction with OCR and figure/diagram descriptions, higher limits, security guarantees such as SOC 2 Type II and HIPAA BAA, and deployment choices including your VPC, your data center or air-gapped setups. Exact figures should be confirmed on the pricing page for current rates.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [Composio](https://openaitools.app/tool/composio) | 2,782,603 | +22.8% | Freemium, Contact for Pricing | Focuses on tool integrations and authentication for agents, complementing memory engines rather than storing cross-session context. | [.md](https://openaitools.app/tool/composio.md) |
| 2 | [Exa](https://openaitools.app/tool/exa-ai) | 1,062,574 | +16.8% | Freemium, Contact for Pricing | Web search API and crawler built for AI agents | [.md](https://openaitools.app/tool/exa-ai.md) |
| 3 | [Algolia](https://openaitools.app/tool/algolia) | 689,970 | -11% | Freemium, Contact for Pricing | AI search and retrieval platform for modern apps and commerce | [.md](https://openaitools.app/tool/algolia.md) |
| 4 | [LiteLLM](https://openaitools.app/tool/litellm) | 576,967 | +7.9% | Freemium, Contact for Pricing | One open-source AI gateway for 100+ LLM providers | [.md](https://openaitools.app/tool/litellm.md) |
| 5 | [Merge](https://openaitools.app/tool/merge-dev) | 313,201 | +1.7% | Freemium, Contact for Pricing | The universal context engine connecting agents to data, tools, and models | [.md](https://openaitools.app/tool/merge-dev.md) |
| 6 | [Pinecone](https://openaitools.app/tool/pinecone) | 286,732 | -31% | Freemium, Paid, Contact for Pricing | Offers enterprise-grade scalable vector search and RAG storage, but lacks supermemory's automated agent memory graph abstractions. | [.md](https://openaitools.app/tool/pinecone.md) |
| 7 | [Jina AI](https://openaitools.app/tool/jina-ai) | 280,782 | -16.5% | Freemium, Paid | Search foundation built from embeddings, rerankers, and reader APIs | [.md](https://openaitools.app/tool/jina-ai.md) |
| 8 | [Portkey](https://openaitools.app/tool/portkey-ai) | 195,650 | -9.4% | Freemium, Paid, Contact for Pricing | AI gateway, observability and guardrails for production LLM apps | [.md](https://openaitools.app/tool/portkey-ai.md) |
| 9 | [Honcho](https://openaitools.app/tool/honcho) | 137,752 | +2.2% | Paid, Contact for Pricing | Provides open-source persistent user modelling for stateful agents, but focuses more on conversational psychology than universal vector graphs. | [.md](https://openaitools.app/tool/honcho.md) |
| 10 | [Meilisearch](https://openaitools.app/tool/meilisearch) | 119,713 | -21.5% | Free Trial, Contact for Pricing | Lightning-fast search engine you can self-host or run in the cloud | [.md](https://openaitools.app/tool/meilisearch.md) |

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Source: https://openaitools.app/compare/qdrant-io-vs-supermemory-1

More Markdown: [Categories](https://openaitools.app/categories.md) · [Traffic rankings](https://openaitools.app/rankings.md) · [llms.txt](https://openaitools.app/llms.txt)