# Qdrant vs Shaped

Qdrant records 72,992 monthly visits with -38.4% month-over-month growth and Freemium, Contact for Pricing pricing. Shaped records 11,871 monthly visits with -39.2% growth and Contact for Pricing pricing.

| Metric | [Qdrant](https://openaitools.app/tool/qdrant-io) | [Shaped](https://openaitools.app/tool/shaped) |
| :--- | :--- | :--- |
| Website URL | https://qdrant.tech/ | https://www.shaped.ai |
| Categories | AI Search Engine, AI API, AI Developer Tools, AI Agent | AI Search Engine, AI API, Large Language Models (LLMs), AI Social Media |
| Pricing Model | Freemium, Contact for Pricing | Contact for Pricing |
| Monthly Visits | 72,992 | 11,871 |
| MoM Growth Rate | -38.4% | -39.2% |
| Primary Market | India | United States |
| Direct Visits | 43,793 | 4,726 |
| Review Score | 4.6 | 4.8 |
| Saved Count | 0 | 3 |
| 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)

## Shaped

Shaped positions itself as "the only vector database with a feedback loop." Instead of a static embedding store, it continuously learns from every user interaction — clicks, purchases, watch time — so results get sharper the more your product is used. A single ShapedQL query can retrieve by text, user ID, or item ID, apply filters, score with ML models, and re-rank for diversity all at once.

The platform is built on a three-layer architecture. The query layer handles real-time retrieval and ranking with sub-50ms latency. The intelligence layer manages dynamic embeddings, user and item context, and continuous model training. The data layer connects to 30+ sources including Snowflake, BigQuery, Redshift, Kafka, PostgreSQL, Shopify, and Amplitude, unifying batch and streaming data under a single schema without ETL.

Shaped advertises enterprise-grade security with SOC 2 Type II certification, GDPR/CCPA/HIPAA compliance, and a 99.95% uptime SLA, operating at 5B+ document scale. The site notes that Shaped has been acquired by Whatnot, while the product continues to operate under the Shaped brand with a free $100 credit tier and no credit card required to start.

**Pricing & Plans:** Contact for pricing. New users receive $100 in free credits with no credit card required; plans for Shaped Cloud are quoted based on usage and scale.

- **ShapedQL unified query language:** Retrieve, filter, score, and re-rank across semantic search, keyword search, and behavioral models in a single SQL-like query.
- **Personalized hybrid search:** Blends semantic and keyword retrieval, then re-ranks results by predicted click-through and engagement for each individual user.
- **Continuous learning feedback loop:** ML models retrain on live interaction signals so ranking quality improves automatically as your product generates traffic.
- **30+ native connectors:** Stream data directly from warehouses, analytics tools, and event pipelines like Snowflake, Kafka, BigQuery, and Shopify with no ETL.
- **Sub-50ms serving with enterprise scale:** Handles 5B+ documents at 99.95% uptime SLA, with SOC 2 Type II, GDPR, CCPA, and HIPAA compliance.

**Strengths:** Unifies vector search, ML re-ranking, and training in one query instead of a fragile multi-vendor stack; Continuous learning from real interaction signals keeps results improving automatically; Broad connector ecosystem ingests batch and streaming data without custom ETL; Strong compliance posture (SOC 2 Type II, GDPR, HIPAA) and a published 99.95% uptime SLA

**Limitations:** Pricing is quote-based, so costs are hard to estimate before contacting sales; The ShapedQL and pipeline concepts carry a learning curve for teams new to relevance engineering; Now part of Whatnot, so long-term standalone roadmap clarity may depend on the acquirer

[Profile (Markdown)](https://openaitools.app/tool/shaped.md)

## Frequently Asked Questions

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

Qdrant has 72,992 monthly visits, while Shaped has 11,871. 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%; Shaped has -39.2%.

### 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. Shaped: Contact for Pricing. Contact for pricing. New users receive $100 in free credits with no credit card required; plans for Shaped Cloud are quoted based on usage and scale.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [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) |
| 2 | [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) |
| 3 | [Pinecone](https://openaitools.app/tool/pinecone) | 286,732 | -31% | Freemium, Paid, Contact for Pricing | The managed vector database and knowledge engine for production AI | [.md](https://openaitools.app/tool/pinecone.md) |
| 4 | [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) |
| 5 | [supermemory](https://openaitools.app/tool/supermemory-1) | 156,579 | -7.7% | Contact for Pricing | A memory and continual-learning engine that gives any AI agent persistent context | [.md](https://openaitools.app/tool/supermemory-1.md) |
| 6 | [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) |
| 7 | [Zilliz](https://openaitools.app/tool/zilliz) | 108,886 | -16.4% | Freemium, Paid, Free, Contact for Pricing | Fully managed Vector Lakebase and vector database for enterprise AI, built by the creators of Milvus | [.md](https://openaitools.app/tool/zilliz.md) |
| 8 | [HydraDB OSS](https://openaitools.app/tool/hydradb-oss) | 29,635 | +5.8% | Freemium, Contact for Pricing | Graph database built on object storage for agent memory and context | [.md](https://openaitools.app/tool/hydradb-oss.md) |
| 9 | [MyScale](https://openaitools.app/tool/myscale-com) | 19,350 | +21.3% | Contact for Pricing, Free | SQL-native vector database for production GenAI applications | [.md](https://openaitools.app/tool/myscale-com.md) |
| 10 | [SvectorDB](https://openaitools.app/tool/svectordb) | 4,760 | +6.2% | Freemium | Serverless vector database purpose-built for AWS | [.md](https://openaitools.app/tool/svectordb.md) |

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

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