How do their growth rates compare?

Cognitora has a month-over-month growth rate of —; Rivet.dev has -44.3%.

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. Rivet.dev: Freemium, Contact for Pricing. Rivet Cloud uses a plan-plus-nodes model. The control plane is Free ($0) for prototyping, Hobby at $10/month, Team at $120/month, and Enterprise at $900/month, each raising control-plane redundancy, Actor Node caps, and support. Actor Nodes are billed per month on top, from $4.20 (R-5) to $94.50 (R-160). Self-hosting is free and open source under Apache 2.0, and startups from Y Combinator or a16z Speedrun get 50% off the Team plan for 12 months.

What are alternatives to Cognitora and Rivet.dev?

Other tools in related categories include RunPod, Together AI, Browser Use, Modal, Nebius.

Cognitora vs Rivet.dev — Traffic (0 vs 21.36K), Pricing & Verdict

Open AI Tools

Cognitora vs Rivet.dev — Traffic (0 vs 21.36K), Pricing & Verdict | Open AI Tools

Side-by-Side Traffic & Specification Matrix

Dimension / Metric	Cognitora	Rivet.dev
Official Domain	cognitora.dev	rivet.dev
Pricing Model	Open Source	Freemium, Contact for Pricing
Monthly Visits	0 (0)	21.36K (21,359)
MoM Traffic Delta	0 (—)	-16.97K (-44.3%)
Primary Market & Share	🌐 Global (—)	🇺🇸 United States (39.4%)
Direct Visits / mo	0	10.71K
User Rating	4.6	4.5
Supported Platforms	Website	Website

Cognitora

Open-source distributed LLM inference orchestration for your own fleet

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.