# Ducky vs Shaped

Ducky records 1,966 monthly visits with -8.4% month-over-month growth and Freemium pricing. Shaped records 11,871 monthly visits with -39.2% growth and Contact for Pricing pricing.

| Metric | [Ducky](https://openaitools.app/tool/ducky) | [Shaped](https://openaitools.app/tool/shaped) |
| :--- | :--- | :--- |
| Website URL | https://ducky.ai | https://www.shaped.ai |
| Categories | AI Search Engine, AI Developer Tools, Large Language Models (LLMs), AI API | AI Search Engine, AI API, Large Language Models (LLMs), AI Social Media |
| Pricing Model | Freemium | Contact for Pricing |
| Monthly Visits | 1,966 | 11,871 |
| MoM Growth Rate | -8.4% | -39.2% |
| Primary Market | United States | United States |
| Direct Visits | 774 | 4,726 |
| Review Score | 4.3 | 4.8 |
| Saved Count | 0 | 3 |
| Supported Platforms | Website | Website |

## Ducky

Ducky is a managed retrieval layer that handles the entire AI search pipeline for you, from ingestion and indexing through chunking, reranking and answer synthesis. Instead of stitching together an embedding model, a vector database, a chunker and a reranker, teams call one API surface and let Ducky manage the moving parts. The platform markets a zero-setup onboarding path: point it at your content, rely on smart defaults, and ship a working search feature the same week.

Beyond plain keyword matching, Ducky supports multi-modal content — text, images and PDFs — so the same index can answer questions across document types. It adds metadata filtering for precise narrowing (dates, categories, tags), multi-stage reranking to push the best results to the top, and agent-friendly answer generation with source attribution. Ducky also advertises context filtering that trims irrelevant passages before they reach an LLM, which the vendor says can cut token usage substantially and reduce hallucination by grounding agents in accurate context.

The product is built for developers first, with documented APIs and Python and TypeScript SDKs, a public GitHub presence and thorough docs. A clickable in-browser demo and a book-a-demo flow let teams evaluate quality before committing. Pricing is usage-based with a free trial tier and a paid launch tier, so small prototypes can start at no cost and scale into production retrieval workloads.

**Pricing & Plans:** Usage-based: a free trial tier covers the first 100k index tokens and 100k retrieval tokens at no cost. The paid Launch tier is listed at $290 per month with 3M index and 3M retrieval tokens included, plus metered overage at $0.014 per additional 1K index tokens and $0.079 per additional 1K retrieval tokens.

- **Multi-modal search:** Index and search across text, images and PDFs with one API, so content stays searchable regardless of format.
- **Automated chunking and reranking:** Ducky splits and optimizes documents for retrieval and applies multi-stage reranking so the strongest matches surface first.
- **Metadata filtering:** Narrow results precisely by attributes such as date, category or tag to build filtered, product-grade search experiences.
- **Agent answers with attribution:** Agents can ask questions and receive synthesized answers that cite their sources, with the whole search-to-synthesis pipeline automated.
- **Python and TypeScript SDKs:** Developer-first tooling with documented APIs and first-class SDKs for the two most common product stacks.

**Strengths:** Fully managed pipeline removes the need to run a vector database or stitching layer; Multi-modal indexing covers text, images and PDFs in one system; Free trial tier and transparent per-token overage make early evaluation low-risk; Well-documented APIs plus Python and TypeScript SDKs shorten integration time

**Limitations:** Opaque internals: the ranking and chunking logic is managed, limiting fine-grained tuning; Pricing is token-metered, so heavy indexing or query volumes require cost monitoring; No self-hosted or open-source deployment option for teams with strict data-residency rules

[Profile (Markdown)](https://openaitools.app/tool/ducky.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 Ducky and Shaped compare in monthly traffic?

Ducky has 1,966 monthly visits, while Shaped has 11,871. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Ducky has a month-over-month growth rate of -8.4%; Shaped has -39.2%.

### How does pricing compare?

Ducky: Freemium. Usage-based: a free trial tier covers the first 100k index tokens and 100k retrieval tokens at no cost. The paid Launch tier is listed at $290 per month with 3M index and 3M retrieval tokens included, plus metered overage at $0.014 per additional 1K index tokens and $0.079 per additional 1K retrieval tokens. 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 | [You.com](https://openaitools.app/tool/you) | 948,349 | +7% | Freemium | Search and inference APIs that give AI models real-time web data | [.md](https://openaitools.app/tool/you.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 | [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) |
| 5 | [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) |
| 6 | [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) |
| 7 | [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) |
| 8 | [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) |
| 9 | [Qdrant](https://openaitools.app/tool/qdrant-io) | 72,992 | -38.4% | Freemium, Contact for Pricing | Open-source, high-performance vector search at any scale | [.md](https://openaitools.app/tool/qdrant-io.md) |
| 10 | [Bocha AI Search](https://openaitools.app/tool/%E5%8D%9A%E6%9F%A5ai%E6%90%9C%E7%B4%A2) | 36,221 | -0.9% | Paid, Free | A search engine built for AI applications, not just people | [.md](https://openaitools.app/tool/%E5%8D%9A%E6%9F%A5ai%E6%90%9C%E7%B4%A2.md) |

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Source: https://openaitools.app/compare/ducky-vs-shaped

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