# boat vs Cognitora

boat records 66,870 monthly visits with +520.8% month-over-month growth and Free Trial, Paid pricing. Cognitora records 0 monthly visits with — growth and Open Source pricing.

| Metric | [boat](https://openaitools.app/tool/boat) | [Cognitora](https://openaitools.app/tool/cognitora) |
| :--- | :--- | :--- |
| Website URL | https://boat.dev | https://cognitora.dev |
| Categories | AI Agent, AI Developer Tools, AI Workflow, AI API | AI Agent, AI API, AI Developer Tools |
| Pricing Model | Free Trial, Paid | Open Source |
| Monthly Visits | 66,870 | 0 |
| MoM Growth Rate | +520.8% | — |
| Primary Market | United States | Global |
| Direct Visits | 37,777 | 0 |
| Review Score | 4.6 | 4.6 |
| Saved Count | 0 | 0 |
| Supported Platforms | Website | Website |

## boat

boat targets teams running many agents at once, which it calls "agent factories". Instead of containers that reset on every session, boat hands out full Linux virtual machines with a persistent disk, so installed packages, cloned repos, and saved files survive between runs. Pre-configured images ship with Docker, VS Code, Chrome, the GitHub CLI, Rust, Node.js, and Bun, and you can bring any harness such as Claude Code, Codex, or OpenCode.

The standout capability is disk-level forking. You can snapshot a configured sandbox and clone it in seconds to run dozens or hundreds of parallel agents from an identical starting state. Sandboxes can be paused and resumed, with disk snapshots taken every minute and again at each stop, so billing only accrues while the VM runs. This makes it practical to fan out test, build, and research workloads without paying for idle time.

Billing is metered by the second against a monthly minimum, and the service advertises one of the lowest list prices for comparable VM hours, with sizes ranging from 2 vCPU to 16 vCPU. Sandboxes run in Germany, Finland, and France; there is no US or Asia region and no GPU option, so graphical work runs on the CPU. It is a strong fit for developers who want a real machine rather than a constrained execution sandbox.

**Pricing & Plans:** Per-second billing with a $20/month minimum. The default 4 vCPU / 8 GB sandbox lists at $0.036/h, with smaller (2 vCPU / 4 GB) and larger (8 vCPU / 16 GB, 16 vCPU / 32 GB) sizes available. A 7-day free trial with a card provides 25 compute hours, and stopped sandboxes incur no compute charge while the disk is retained for at least 14 days.

- **Persistent full Linux VMs:** Each sandbox is a complete Ubuntu machine with systemd, Docker, databases, and cron, so anything that runs on Linux runs here.
- **Fast snapshot forking:** Clone a configured sandbox from a disk snapshot in seconds to launch large fleets of identical parallel agents.
- **SSH, SCP, and virtual desktop:** Connect over SSH and SCP or open a 60fps virtual desktop and browser to inspect web apps, IDEs, and 3D scenes.
- **API, CLI, and SDKs:** Drive everything programmatically through the boat CLI (with --json output), an HTTP API, or Python and TypeScript SDKs.
- **Per-second metered billing:** Pay only while sandboxes run, with no compute charge when stopped and disks kept for at least 14 days.

**Strengths:** Full Ubuntu VMs rather than restricted containers, with Docker and systemd included; Very fast disk-level forking makes large parallel agent runs affordable; Transparent per-second pricing with no compute charge while stopped

**Limitations:** No GPU option and no US or Asia data regions; A $20 monthly minimum applies, which is wasteful for light or occasional use

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

## 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)

## Frequently Asked Questions

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

boat has 66,870 monthly visits, while Cognitora has 0. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

boat has a month-over-month growth rate of +520.8%; Cognitora has —.

### How does pricing compare?

boat: Free Trial, Paid. Per-second billing with a $20/month minimum. The default 4 vCPU / 8 GB sandbox lists at $0.036/h, with smaller (2 vCPU / 4 GB) and larger (8 vCPU / 16 GB, 16 vCPU / 32 GB) sizes available. A 7-day free trial with a card provides 25 compute hours, and stopped sandboxes incur no compute charge while the disk is retained for at least 14 days. 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.

## 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 | The AI developer cloud for GPU pods, serverless inference and clustering | [.md](https://openaitools.app/tool/runpod.md) |
| 2 | [Together AI](https://openaitools.app/tool/together-ai) | 1,645,930 | +64.9% | Freemium, Paid | Together AI is the AI Native Cloud — a full-stack platform for running, fine-tuning and training open-source models. It spans serverless inference APIs, dedicated endpoints, GPU clusters, post-training and storage, all b | [.md](https://openaitools.app/tool/together-ai.md) |
| 3 | [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) |
| 4 | [Modal](https://openaitools.app/tool/modal-com) | 1,196,021 | +30.2% | Freemium, Contact for Pricing | Serverless cloud infrastructure for AI and data workloads | [.md](https://openaitools.app/tool/modal-com.md) |
| 5 | [Nebius](https://openaitools.app/tool/nebius) | 1,145,711 | +10.7% | Paid, Contact for Pricing | An AI-native cloud built from silicon to API for training, tuning, and serving models | [.md](https://openaitools.app/tool/nebius.md) |
| 6 | [Salad](https://openaitools.app/tool/salad-gpu-cloud) | 713,519 | +10.4% | Free Trial, Paid, Contact for Pricing | Distributed GPU cloud that orchestrates the world's idle consumer GPUs | [.md](https://openaitools.app/tool/salad-gpu-cloud.md) |
| 7 | [Browserbase](https://openaitools.app/tool/browserbase) | 283,597 | -8% | Freemium | Cloud browsers and web APIs for AI agents | [.md](https://openaitools.app/tool/browserbase.md) |
| 8 | [Agent 37](https://openaitools.app/tool/agent-37) | 180,030 | +24.2% | Free Trial, Contact for Pricing | The low-cost cloud sandbox where AI agents actually live | [.md](https://openaitools.app/tool/agent-37.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 | [InsForge](https://openaitools.app/tool/insforge) | 60,257 | -11.3% | Freemium, Contact for Pricing, Free | Agent-native cloud infrastructure that lets coding agents run the whole backend stack | [.md](https://openaitools.app/tool/insforge.md) |

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Source: https://openaitools.app/compare/boat-vs-cognitora

More Markdown: [Categories](https://openaitools.app/categories.md) · [Traffic rankings](https://openaitools.app/rankings.md) · [llms.txt](https://openaitools.app/llms.txt)