# Salad vs supermemory

Salad records 713,519 monthly visits with +10.4% month-over-month growth and Free Trial, Paid, Contact for Pricing pricing. supermemory records 156,579 monthly visits with -7.7% growth and Contact for Pricing pricing.

| Metric | [Salad](https://openaitools.app/tool/salad-gpu-cloud) | [supermemory](https://openaitools.app/tool/supermemory-1) |
| :--- | :--- | :--- |
| Website URL | https://salad.com | https://supermemory.ai |
| Categories | AI Developer Tools, AI API, AI Transcription, Large Language Models (LLMs) | AI API, Large Language Models (LLMs), AI Developer Tools, AI Agent |
| Pricing Model | Free Trial, Paid, Contact for Pricing | Contact for Pricing |
| Monthly Visits | 713,519 | 156,579 |
| MoM Growth Rate | +10.4% | -7.7% |
| Primary Market | United States | United States |
| Direct Visits | 440,915 | 71,163 |
| Review Score | 4.3 | 4.5 |
| Saved Count | 1 | 0 |
| Supported Platforms | Website | Website |

## Salad

Salad attacks the cost of GPU compute from a different direction. Rather than financing new data-centre capacity, it coordinates supply that already exists: consumer GPUs sitting idle in PCs and workstations worldwide. Since 2018, more than 450,000 GPUs have contributed to the network, and it currently reports 16,000+ daily active GPUs spread across 190+ countries. Pricing starts around $0.015 per GPU-hour, which the company positions as up to 90% below hyperscaler rates.

The orchestration layer is the core product. Salad Container Engine schedules OCI/Docker containers onto nodes it has never seen before, verifies and isolates them behind a hypervisor, streams logs, and reallocates work the moment a node drops off the network — critical because interruptible consumer hardware leaves unpredictably. A proprietary trust rating scores node performance and availability, and priority pricing matches cheap interruptible supply to workloads designed to tolerate it. The same engine runs on Community Cloud, reserved Salad Dedicated hardware (RTX PRO 6000 Blackwell among the first), and on a Private Cloud inside your own organisation, where idle workstation GPUs are pooled into an OpenAI-compatible token service without company data leaving company devices.

Several managed products sit on top: an OpenAI-compatible AI Gateway for open-weight models, a Transcription API, and Gateway Service for residential IP tunnels. Published per-workload benchmarks back the marketing — for example 5,243 Flux.1-Schnell images per dollar, 95.1% CommonVoice accuracy from $0.16 per audio hour, and flat hourly GPU rates or per-token pricing from $0.06 per million input tokens for LLM inference. Production users include Civitai, Blend, Klyne.ai, Undetectable AI, Pareto and Datasembly.

**Pricing & Plans:** Pay-as-you-go with no contracts or minimums on SaladCloud: per-second billing on containers and per-token pricing on the AI Gateway, with GPU rates starting around $0.015 per GPU-hour and tiered volume discounts. Reserved capacity and enterprise contracts are available through Salad Dedicated and Private Cloud, quoted on request.

- **Salad Container Engine:** Schedules, monitors and reallocates Docker/OCI workloads across thousands of distributed GPU nodes, with failover when nodes disappear.
- **Community Cloud GPU pool:** On-demand access to a distributed pool of idle consumer GPUs across 190+ countries with priority pricing from about $0.015 per GPU-hour.
- **OpenAI-compatible AI Gateway:** A managed token endpoint for open-weight models priced per token, with your data never used for training.
- **Private Cloud on your hardware:** Run the same orchestration stack inside your organisation, pooling idle workstation GPUs into an internal OpenAI-compatible service.
- **Workload-tuned APIs:** Managed services including a Transcription API and virtual kubelet support for scheduling Kubernetes pods onto the network.

**Strengths:** Structurally low pricing — GPU rates start near $0.015 per GPU-hour; One orchestration layer spans on-demand, reserved and private infrastructure; Handles interruption natively, which matters on consumer hardware; OpenAI-compatible endpoints simplify migration from other providers

**Limitations:** Interruptible consumer nodes are not suited to latency-critical or always-on workloads as-is; Distributed hardware varies more in performance than uniform data-centre racks; No conventional free tier — costs scale directly with usage

[Profile (Markdown)](https://openaitools.app/tool/salad-gpu-cloud.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 Salad and supermemory compare in monthly traffic?

Salad has 713,519 monthly visits, while supermemory has 156,579. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Salad has a month-over-month growth rate of +10.4%; supermemory has -7.7%.

### How does pricing compare?

Salad: Free Trial, Paid, Contact for Pricing. Pay-as-you-go with no contracts or minimums on SaladCloud: per-second billing on containers and per-token pricing on the AI Gateway, with GPU rates starting around $0.015 per GPU-hour and tiered volume discounts. Reserved capacity and enterprise contracts are available through Salad Dedicated and Private Cloud, quoted on request. 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 | [RunPod](https://openaitools.app/tool/runpod) | 2,881,043 | +5.9% | Paid | Offers reliable dedicated and serverless GPU rentals, but lacks Salad's ultra-low-cost peer-to-peer consumer hardware pool. | [.md](https://openaitools.app/tool/runpod.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 | [Together AI](https://openaitools.app/tool/together-ai) | 1,645,930 | +64.9% | Freemium, Paid | Specializes in managed serverless inference APIs for open-source models, rather than raw GPU infrastructure rentals. | [.md](https://openaitools.app/tool/together-ai.md) |
| 4 | [Vast.ai](https://openaitools.app/tool/vast-ai) | 1,257,554 | +2.8% | Paid | Provides an extensive peer-to-peer GPU marketplace with lower prices, but requires more manual configuration. | [.md](https://openaitools.app/tool/vast-ai.md) |
| 5 | [Modal](https://openaitools.app/tool/modal-com) | 1,196,021 | +30.2% | Freemium, Contact for Pricing | Delivers effortless Python serverless execution for AI pipelines, but focuses on function-as-a-service rather than raw VMs. | [.md](https://openaitools.app/tool/modal-com.md) |
| 6 | [Nebius](https://openaitools.app/tool/nebius) | 1,145,711 | +10.7% | Paid, Contact for Pricing | Supplies enterprise-grade NVIDIA GPU clusters and managed ML tooling, but at a higher professional price point. | [.md](https://openaitools.app/tool/nebius.md) |
| 7 | [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) |
| 8 | [DeepInfra](https://openaitools.app/tool/deep-infra) | 457,810 | -8.9% | Paid | Offers extremely cheap serverless model hosting via API, but does not provide raw containerized GPU instances. | [.md](https://openaitools.app/tool/deep-infra.md) |
| 9 | [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) |
| 10 | [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) |

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Source: https://openaitools.app/compare/salad-gpu-cloud-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)