# OrcaRouter vs supermemory

OrcaRouter records 975,300 monthly visits with -5.2% month-over-month growth and Freemium pricing. supermemory records 156,579 monthly visits with -7.7% growth and Contact for Pricing pricing.

| Metric | [OrcaRouter](https://openaitools.app/tool/orcarouter) | [supermemory](https://openaitools.app/tool/supermemory-1) |
| :--- | :--- | :--- |
| Website URL | https://www.orcarouter.ai | https://supermemory.ai |
| Categories | AI API, Large Language Models (LLMs), AI Developer Tools | AI API, Large Language Models (LLMs), AI Developer Tools, AI Agent |
| Pricing Model | Freemium | Contact for Pricing |
| Monthly Visits | 975,300 | 156,579 |
| MoM Growth Rate | -5.2% | -7.7% |
| Primary Market | United States | United States |
| Direct Visits | 249,710 | 71,163 |
| Review Score | 4.3 | 4.5 |
| Saved Count | 0 | 0 |
| Supported Platforms | Website | Website |

## OrcaRouter

OrcaRouter consolidates access to more than 200 large language, image, and video models behind one API endpoint. Instead of hard-coding a single provider, developers send requests to a router model, and the gateway grades each prompt for difficulty and intent before sending it to a model that meets the required quality bar. That adaptive routing, combined with prompt caching and zero token markup, is how it claims 40%+ inference cost savings while keeping routing accuracy high and latency under roughly 10 milliseconds.

Beyond routing, OrcaRouter adds the operational layers production teams usually have to build themselves. There is automatic failover with configurable fallback chains, YAML-and-CEL routing rules expressed as code, bring-your-own-key support, and the option to attach your own GPU via Orca Connect. Observability is built in through request logs with per-call grade, model, latency, and cost, plus a spend and quality dashboard. An MIT-licensed self-hosted gateway, OrcaRouter Lite, is available for teams that want to run it on their own infrastructure.

Governance is a major focus: PII shielding and content policies are enforced before billing, an agent firewall grades every tool and MCP call, and 32 compliance framework packs generate exportable evidence. The platform also offers privacy features such as data cloaking, zero-data-retention options, quantum-safe log encryption, regional data residency, and attested TEE enclaves. A free tier exists alongside paid plans, and pay-as-you-go usage carries no token markup.

**Pricing & Plans:** Freemium

- **Adaptive routing and failover:** Each prompt is graded and routed to the model that clears your quality bar, with configurable fallback chains that retry before a response starts.
- **Zero token markup:** Pay provider prices with $0 per-token added on pay-as-you-go and subscriptions, or bring your own provider keys and credits.
- **Guardrails and agent firewall:** PII shielding, jailbreak and injection blocking, and per-call approval for tool and MCP actions run before you are billed.
- **Observability and cost insights:** Request logs, per-prompt receipts, routing and spend dashboards, and copy-as-cURL make every call auditable and attributable.
- **Compliance and privacy controls:** 32 framework packs, data cloaking, ZDR agreements, regional residency, and quantum-safe encrypted logs support regulated deployments.

**Strengths:** One OpenAI-compatible endpoint across 200+ models; Zero token markup with optional bring-your-own-key; Built-in guardrails, agent firewall, and compliance packs; Self-hostable MIT-licensed Lite gateway

**Limitations:** Advanced routing DSL and governance take time to configure well; Some features like TEE and data residency are request-only; A newer platform, so long-term ecosystem and community maturity is still building

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

OrcaRouter has 975,300 monthly visits, while supermemory has 156,579. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

OrcaRouter has a month-over-month growth rate of -5.2%; supermemory has -7.7%.

### How does pricing compare?

OrcaRouter: Freemium. Freemium 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 | [OpenRouter](https://openaitools.app/tool/openrouter-ai) | 19,941,437 | -6.2% | Paid | Provides access to a massive catalog of frontier models with flexible routing, but lacks OrcaRouter's dedicated enterprise governance. | [.md](https://openaitools.app/tool/openrouter-ai.md) |
| 2 | [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) |
| 3 | [CometAPI](https://openaitools.app/tool/cometapi) | 602,966 | +49.7% | Pay-as-you-go (credits) | One OpenAI-compatible API key for 500+ AI models | [.md](https://openaitools.app/tool/cometapi.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 | [ZenMux](https://openaitools.app/tool/zenmux) | 444,394 | +16.7% | Freemium, Paid | Routes requests across frontier models with automatic failover and analytics, serving as a straightforward alternative API proxy. | [.md](https://openaitools.app/tool/zenmux.md) |
| 6 | [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) |
| 7 | [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) |
| 8 | [AiHubMix](https://openaitools.app/tool/aihubmix) | 238,455 | -16.1% | Contact for Pricing | Gives developers a single OpenAI-compatible endpoint for multiple LLMs with failover, but offers fewer advanced routing rules. | [.md](https://openaitools.app/tool/aihubmix.md) |
| 9 | [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) |
| 10 | [Eden AI](https://openaitools.app/tool/eden-ai) | 173,828 | +6.5% | Paid, Contact for Pricing | Unifies multi-provider model access with built-in governance and cost tracking, but functions more as a broad AI marketplace. | [.md](https://openaitools.app/tool/eden-ai.md) |

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Source: https://openaitools.app/compare/orcarouter-vs-supermemory-1

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