# Marqo vs supermemory

Marqo records 8,382 monthly visits with -25.9% 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 | [Marqo](https://openaitools.app/tool/marqo) | [supermemory](https://openaitools.app/tool/supermemory-1) |
| :--- | :--- | :--- |
| Website URL | https://www.marqo.ai | https://supermemory.ai |
| Categories | AI Search Engine, Large Language Models (LLMs), Text to Image, AI For Data Analytics | AI API, Large Language Models (LLMs), AI Developer Tools, AI Agent |
| Pricing Model | Contact for Pricing | Contact for Pricing |
| Monthly Visits | 8,382 | 156,579 |
| MoM Growth Rate | -25.9% | -7.7% |
| Primary Market | United States | United States |
| Direct Visits | 3,322 | 71,163 |
| Review Score | 4.2 | 4.5 |
| Saved Count | 0 | 0 |
| Supported Platforms | Website | Website |

## Marqo

Marqo builds AI-native product discovery for retailers rather than bolting semantic search onto a legacy keyword stack. The core idea is that a shared, generic model does not understand your catalog: Marqo trains a dedicated model, MarqTune, on your real product data and shopper behavior using techniques like generalized contrastive learning, so relevance reflects what actually converts in your store rather than generic similarity.

Implementation is designed to be light. A single-line pixel starts capturing clicks, carts, and purchases, which both feeds the model and provides a dashboard of user interaction data. Deployment then happens through an API or one-click integrations with Shopify, Adobe Commerce, and Salesforce Commerce Cloud. The platform covers more than a search box: intelligent understanding handles semantics, typo tolerance, and multilingual queries; adaptive journeys shift between results, carousels, guided discovery, or conversational search; and strategic automation manages ranking, boosts, filters, and collections to reduce manual merchandising work.

The commercial pitch is measurable uplift. Marqo publishes retailer case studies citing double-digit gains in search revenue per user, conversion rate, add-to-cart rate, and revenue per collection, and positions image and multimodal search as an area where legacy search platforms fall short. It is aimed at enterprise commerce teams that treat discovery as a revenue channel and are willing to train and tune a model on their own data to get there. Pricing is quoted per engagement, so onboarding typically starts with a demo.

**Pricing & Plans:** Enterprise pricing available on request via a demo. Marqo does not publish public tiers or per-seat rates; plans are quoted based on catalog size, traffic, and deployment scope. A free pixel and dashboard onboarding step precedes commercial rollout.

- **MarqTune custom models:** Trains a dedicated AI model on your real catalog and shopper behavior, with no shared LLM and no synthetic data, so relevance matches how your customers actually shop.
- **Semantic and multilingual search:** Goes beyond keyword matching with instant indexing, semantic relevance, typo tolerance, and multilingual comprehension across queries.
- **Image and multimodal search:** LLM-based image search is integrated into the search model, supporting reverse image and visual discovery flows that legacy keyword engines cannot match.
- **Adaptive discovery journeys:** Shifts in real time between results, carousels, guided discovery, and conversational search based on shopper intent, context, and behavior.
- **Strategic merchandising automation:** Automates ranking, boosts, filters, and collection generation using conversion signals, cutting manual merchandising effort.

**Strengths:** Dedicated model trained on your catalog and shopper data rather than a shared generic model; Publishes concrete retailer case studies with double-digit search and conversion uplift figures; One-click integrations for Shopify, Adobe Commerce, and Salesforce Commerce Cloud, plus API access; Covers search, browse, recommendations, and agentic storefront surfaces from one platform

**Limitations:** No public pricing or self-serve free tier, so evaluation requires a sales conversation; Value depends on having enough catalog and traffic data to train a meaningful model; Enterprise-focused scope and integrations may be more than a small store needs

[Profile (Markdown)](https://openaitools.app/tool/marqo.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 Marqo and supermemory compare in monthly traffic?

Marqo has 8,382 monthly visits, while supermemory has 156,579. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Marqo has a month-over-month growth rate of -25.9%; supermemory has -7.7%.

### How does pricing compare?

Marqo: Contact for Pricing. Enterprise pricing available on request via a demo. Marqo does not publish public tiers or per-seat rates; plans are quoted based on catalog size, traffic, and deployment scope. A free pixel and dashboard onboarding step precedes commercial rollout. 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 | [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 | [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) |
| 4 | [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) |
| 5 | [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) |
| 6 | [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) |
| 7 | [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) |
| 8 | [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) |
| 9 | [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) |
| 10 | [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) |

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Source: https://openaitools.app/compare/marqo-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)