# Shaped vs supermemory

Shaped records 11,871 monthly visits with -39.2% month-over-month growth and Contact for Pricing pricing. supermemory records 156,579 monthly visits with -7.7% growth and Contact for Pricing pricing.

| Metric | [Shaped](https://openaitools.app/tool/shaped) | [supermemory](https://openaitools.app/tool/supermemory-1) |
| :--- | :--- | :--- |
| Website URL | https://www.shaped.ai | https://supermemory.ai |
| Categories | AI Search Engine, AI API, Large Language Models (LLMs), AI Social Media | AI API, Large Language Models (LLMs), AI Developer Tools, AI Agent |
| Pricing Model | Contact for Pricing | Contact for Pricing |
| Monthly Visits | 11,871 | 156,579 |
| MoM Growth Rate | -39.2% | -7.7% |
| Primary Market | United States | United States |
| Direct Visits | 4,726 | 71,163 |
| Review Score | 4.8 | 4.5 |
| Saved Count | 3 | 0 |
| Supported Platforms | Website | Website |

## 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)

## 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 Shaped and supermemory compare in monthly traffic?

Shaped has 11,871 monthly visits, while supermemory has 156,579. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Shaped has a month-over-month growth rate of -39.2%; supermemory has -7.7%.

### How does pricing compare?

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. 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/shaped-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)