# Cognitora vs Salad

Cognitora records 0 monthly visits with — month-over-month growth and Open Source pricing. Salad records 713,519 monthly visits with +10.4% growth and Free Trial, Paid, Contact for Pricing pricing.

| Metric | [Cognitora](https://openaitools.app/tool/cognitora) | [Salad](https://openaitools.app/tool/salad-gpu-cloud) |
| :--- | :--- | :--- |
| Website URL | https://cognitora.dev | https://salad.com |
| Categories | AI Agent, AI API, AI Developer Tools | AI Developer Tools, AI API, AI Transcription, Large Language Models (LLMs) |
| Pricing Model | Open Source | Free Trial, Paid, Contact for Pricing |
| Monthly Visits | 0 | 713,519 |
| MoM Growth Rate | — | +10.4% |
| Primary Market | Global | United States |
| Direct Visits | 0 | 440,915 |
| Review Score | 4.6 | 4.3 |
| Saved Count | 0 | 1 |
| Supported Platforms | Website | Website |

## Cognitora

Cognitora is an open-source orchestration layer for distributed large language model inference, written in Rust. Rather than shipping a single inference engine, it joins the engines you already operate — vLLM, SGLang, llama.cpp, Apple MLX, a TRT-LLM spawn driver and its own preview cgn-infer engine — into one cluster behind a single OpenAI-compatible gateway. You can deploy it instead of an orchestrator such as NVIDIA Dynamo, and the project positions itself as supporting more engines, running on bare metal without Kubernetes, factoring power consumption into scheduling and avoiding a Python control plane.

Its core innovation is KV-aware routing. The router scores nodes by the longest-prefix overlap of sequence-chained BLAKE3 digests, using cache claims that agents report in heartbeats. In real llama.cpp runs the router reportedly added 0.9–2.0 ms at p50. Beyond routing, the platform splits prefill from decode across separate pools (handing KV cache off through NixlConnector), maintains a multi-tier RAM/SSD KV cache with cross-node QUIC fetch, and offers pluggable offload backends including nixl, lmcache, hicache and kvbm.

The stack is modular: cgn-router for routing, cgn-agent to supervise engines on each node, cgn-kvcached for tiered caching, cgn-metrics for Prometheus plus Redfish/NVML power telemetry, cgn-ctl as an admin CLI, an optional Kubernetes operator, and a preview native engine. Energy-aware scheduling drains hot nodes, a closed-loop autoscaler reacts to power readings, and gossip-based discovery removes the need for etcd. Security options include optional mTLS, SHA-256 checksummed releases and OIDC or scoped API-key auth. The project ships six static binaries for systemd installs, Helm charts for Kubernetes, and a single-file HTML cluster dashboard that visualises throughput, latency percentiles, KV utilisation and joules per token without Grafana.

**Pricing & Plans:** Cognitora is open source and installs from its repository; there is no published per-seat or per-token price. Deployment cost is your own hardware, so total spend depends on the GPUs and nodes you run.

- **KV-aware request routing:** Routes each request to the node whose cache holds the longest matching prompt prefix, using longest-prefix overlap of BLAKE3 sequence digests to avoid repeated prefill work.
- **Engine-agnostic gateway:** One OpenAI-compatible API in front of vLLM, SGLang, llama.cpp, Apple MLX and a TRT-LLM spawn driver, so you can mix engines inside a single cluster.
- **Prefill/decode disaggregation:** Runs prefill and decode on separate replica pools with KV handoff through NixlConnector, letting each phase use hardware suited to it.
- **Energy-aware scheduling:** Folds Redfish and NVML power readings into routing scores, applies soft per-node watt caps, and drains hot nodes automatically to cut energy per token.
- **Bare metal to Kubernetes:** Deploys as six static binaries under systemd without Kubernetes, or with an optional kube-rs operator exposing InferenceCluster, ModelPool and RoutingPolicy CRDs.

**Strengths:** Open source and self-hostable, with no per-token or per-seat licensing cost; KV-aware routing meaningfully reduces duplicated prefill work across a fleet; Works with many engines, so it does not lock you into one vendor's runtime; Runs on bare metal without Kubernetes, keeping the operational surface small

**Limitations:** Requires substantial LLM infrastructure knowledge and hands-on GPU hardware to evaluate; Several headline capabilities are on the main branch but not yet in a tagged release; TRT-LLM support is a spawn driver only, without a default recipe or CI GPU test

[Profile (Markdown)](https://openaitools.app/tool/cognitora.md)

## 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)

## Frequently Asked Questions

### How do Cognitora and Salad compare in monthly traffic?

Cognitora has 0 monthly visits, while Salad has 713,519. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Cognitora has a month-over-month growth rate of —; Salad has +10.4%.

### How does pricing compare?

Cognitora: Open Source. Cognitora is open source and installs from its repository; there is no published per-seat or per-token price. Deployment cost is your own hardware, so total spend depends on the GPUs and nodes you run. 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.

## 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 | [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) |
| 3 | [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) |
| 4 | [Browser Use](https://openaitools.app/tool/browser-use) | 1,206,376 | +41.1% | Freemium, Paid, Contact for Pricing | Browser agents and stealth cloud browsers for real web tasks | [.md](https://openaitools.app/tool/browser-use.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 | [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) |
| 8 | [FluidStack](https://openaitools.app/tool/fluidstack) | 154,620 | +7.3% | Paid, Contact for Pricing | Focuses on large-scale enterprise GPU clusters for training, rather than lightweight consumer container workloads. | [.md](https://openaitools.app/tool/fluidstack.md) |
| 9 | [Coder](https://openaitools.app/tool/coder) | 149,417 | -12.8% | Free Trial, Contact for Pricing, Free | Self-hosted cloud development environments and AI coding agents for enterprise teams | [.md](https://openaitools.app/tool/coder.md) |
| 10 | [Thunder Compute](https://openaitools.app/tool/thunder-compute) | 102,147 | -29% | Paid (per-minute instances, per-second sandboxes) | Features second-billed persistent SSH GPU instances, but lacks a distributed consumer hardware backbone. | [.md](https://openaitools.app/tool/thunder-compute.md) |

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

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