# Pinecone vs supermemory

Pinecone records 286,732 monthly visits with -31% month-over-month growth and Freemium, Paid, Contact for Pricing pricing. supermemory records 156,579 monthly visits with -7.7% growth and Contact for Pricing pricing.

| Metric | [Pinecone](https://openaitools.app/tool/pinecone) | [supermemory](https://openaitools.app/tool/supermemory-1) |
| :--- | :--- | :--- |
| Website URL | https://www.pinecone.io | https://supermemory.ai |
| Categories | AI API, AI Search Engine, AI Agent, AI Developer Tools | AI API, Large Language Models (LLMs), AI Developer Tools, AI Agent |
| Pricing Model | Freemium, Paid, Contact for Pricing | Contact for Pricing |
| Monthly Visits | 286,732 | 156,579 |
| MoM Growth Rate | -31% | -7.7% |
| Primary Market | United States | United States |
| Direct Visits | 118,638 | 71,163 |
| Review Score | 4.8 | 4.5 |
| Saved Count | 2 | 0 |
| Supported Platforms | Website | Website |

## Pinecone

Pinecone describes itself as the trusted AI knowledge company. At its core is a fully managed vector database built for AI workloads: you write embeddings and query them at scale without provisioning servers or hand-tuning index parameters. Writes are acknowledged in under 100ms and become searchable within seconds, indexing adjusts automatically as data grows, and queries are parallelized so p99 latency stays stable rather than degrading.

The platform has expanded beyond raw vector search. Pinecone Nexus is a knowledge engine that compiles enterprise data into governed knowledge once and serves it through a single query, which Pinecone says reduced tokens per query by 85-97% and improved accuracy by 14-94% versus agentic RAG in benchmark testing. A Marketplace and one API key connect the index to tools like Claude Code, Cursor, Copilot, Codex, Gemini CLI, and MCP, so retrieval, upsert, and rerank all flow from the same index.

Adoption is broad: Pinecone says its platform powers AI for 10,000+ customers and over 1 million developers, with named users including Adobe, Workday, Microsoft, OpenAI, and Fox. It is a strong fit for teams that need production-grade retrieval without operating their own infrastructure, from a free first index through to enterprise governance and SLAs.

**Pricing & Plans:** Freemium and usage-based. You can create your first index for free and pay as you go as workloads scale, with higher tiers for dedicated capacity and enterprise agreements. Check the official pricing page and cost estimator for current rates.

- **Fully managed vector database:** Store and query vector embeddings with automatic indexing that requires no manual tuning or algorithm selection.
- **Fast writes and consistent queries:** Writes are acknowledged in under 100ms and become searchable within seconds, while all data is searched in parallel so speed holds at scale.
- **One API key, many integrations:** Connect agents to Pinecone through plugins for Claude Code, Cursor, Copilot, Codex, Gemini CLI, MCP, or a direct REST API with one key.
- **Pinecone Nexus knowledge engine:** Serve compiled, governed enterprise knowledge to agents through a single query, cutting token usage and latency versus multi-step RAG loops.
- **Console, metrics, and cost estimation:** A browser console and API let teams monitor metrics, explore records, manage namespaces, and estimate workload cost before committing.

**Strengths:** Fully managed, so no index tuning or server ops; Stable low-latency performance as datasets grow; Broad ecosystem integrations and MCP support; Nexus reduces token cost and latency for agent retrieval

**Limitations:** Usage-based billing can grow with heavy query volume if left unmonitored; Requires some engineering effort to integrate and design schemas properly; Enterprise compliance features sit behind higher-priced arrangements

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

Pinecone has 286,732 monthly visits, while supermemory has 156,579. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Pinecone has a month-over-month growth rate of -31%; supermemory has -7.7%.

### How does pricing compare?

Pinecone: Freemium, Paid, Contact for Pricing. Freemium and usage-based. You can create your first index for free and pay as you go as workloads scale, with higher tiers for dedicated capacity and enterprise agreements. Check the official pricing page and cost estimator for current rates. 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 | [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) |
| 3 | [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) |
| 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 | [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) |
| 6 | [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) |
| 7 | [Meilisearch](https://openaitools.app/tool/meilisearch) | 119,713 | -21.5% | Free Trial, Contact for Pricing | Lightning-fast open-source search engine optimized for keyword and text retrieval, but less tailored for dense vector embeddings. | [.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 Milvus vector database built for massive enterprise scale, offering deeper SQL support than Pinecone. | [.md](https://openaitools.app/tool/zilliz.md) |
| 9 | [Qdrant](https://openaitools.app/tool/qdrant-io) | 72,992 | -38.4% | Freemium, Contact for Pricing | Provides high-performance open-source vector similarity search, requiring custom schema design for agent memory management. | [.md](https://openaitools.app/tool/qdrant-io.md) |
| 10 | [HydraDB OSS](https://openaitools.app/tool/hydradb-oss) | 29,635 | +5.8% | Freemium, Contact for Pricing | Delivers robust open-source graph storage on object stores, but is a database rather than a turnkey agent memory API. | [.md](https://openaitools.app/tool/hydradb-oss.md) |

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