# Cognitora vs Rivet.dev

Cognitora records 0 monthly visits with — month-over-month growth and Open Source pricing. Rivet.dev records 21,359 monthly visits with -44.3% growth and Freemium, Contact for Pricing pricing.

| Metric | [Cognitora](https://openaitools.app/tool/cognitora) | [Rivet.dev](https://openaitools.app/tool/rivet-dev) |
| :--- | :--- | :--- |
| Website URL | https://cognitora.dev | https://rivet.dev |
| Categories | AI Agent, AI API, AI Developer Tools | AI Agent, AI Workflow, AI Developer Tools, AI App Builder |
| Pricing Model | Open Source | Freemium, Contact for Pricing |
| Monthly Visits | 0 | 21,359 |
| MoM Growth Rate | — | -44.3% |
| Primary Market | Global | United States |
| Direct Visits | 0 | 10,710 |
| Review Score | 4.6 | 4.5 |
| Saved Count | 0 | 0 |
| 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)

## Rivet.dev

At its core Rivet replaces the container per agent with an in-process primitive. An Actor runs inside your existing Node.js or Bun backend rather than in its own container or VM, which is why Rivet reports cold starts measured in single-digit milliseconds and a footprint of tens of kilobytes per Actor instead of hundreds of megabytes. Each Actor carries a SQLite database for memory, can access a filesystem and shell, sleeps when idle, and wakes with its durable state ready for the next request.

The platform handles the operational pieces teams usually build themselves: fault tolerance that reschedules Actors when a node goes down, multi-region placement that routes requests to wherever an Actor currently lives, end-to-end type safety where Actor definitions generate client types, and scheduling via cron or self-set timestamps. Storage is pulled apart into two tiers — a fast SSD for instant writes and object storage that settles writes at a fraction of the cost — so running many small databases stays affordable.

Rivet positions itself as infrastructure for the "agentic era" and can be deployed as a managed cloud, inside your own VPC through a bring-your-own-cloud model, or fully self-hosted down to air-gapped networks. It is open source under Apache 2.0, backed by Y Combinator and a16z Speedrun, and integrates with popular coding agents and frameworks.

**Pricing & Plans:** 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.

- **Durable actors with persistent state:** Every Actor gets a SQLite database and a filesystem, so an agent's memory and progress survive restarts and idle periods without a separate database.
- **Lightweight secure sandboxing:** Run untrusted or AI-generated code on your own infrastructure with fine-grained control over network access, filesystem permissions, and resource limits.
- **Fault tolerance and workflows:** A node failure reschedules its Actors elsewhere with state intact, and multi-step work replays after a crash — with no checkpointing harness to build yourself.
- **Realtime and multiplayer:** Address an Actor directly over HTTP or hold a live WebSocket, and use the React SDK to subscribe components to an Actor's state in real time.
- **One-binary deployment and observability:** Ship with a single stateless Rust binary, embeds storage in S3-compatible buckets, and emits OpenTelemetry traces, metrics, and logs to your existing collector.

**Strengths:** Four-digit millisecond cold starts and a very small per-Actor memory footprint let a single server host far more agents; Open source (Apache 2.0) with a single Rust binary, so it can be self-hosted, run in a VPC, or air-gapped; Durable state via per-Actor SQLite plus object-storage-backed tiers keeps agents restart-safe and cheap; Strong developer ergonomics: typed SDKs, a React SDK, WebSocket networking, and OpenTelemetry baked in

**Limitations:** Requires Node.js or Bun and a working Rust-based control plane deployment for self-hosting, so it favors backend engineers over no-code users; Advanced durability and sandboxing concepts (Actors, WarpDrive storage) carry a learning curve; Managed cloud cost scales with Actor Nodes, which can climb for many mid-sized/sized nodes; Enterprise features like multi-tenancy and hardened compliance guidance sit behind sales conversations

[Profile (Markdown)](https://openaitools.app/tool/rivet-dev.md)

## Frequently Asked Questions

### How do Cognitora and Rivet.dev compare in monthly traffic?

Cognitora has 0 monthly visits, while Rivet.dev has 21,359. Traffic estimates describe audience size, not product quality.

### 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.

## Alternatives

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| 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 | [Mastra](https://openaitools.app/tool/mastra) | 318,954 | -5% | Freemium, Contact for Pricing | The TypeScript framework for building, deploying, and observing AI agents | [.md](https://openaitools.app/tool/mastra.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 | [boat](https://openaitools.app/tool/boat) | 66,870 | +520.8% | Free Trial, Paid | Persistent Ubuntu cloud VMs built for AI agent factories | [.md](https://openaitools.app/tool/boat.md) |

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Source: https://openaitools.app/compare/cognitora-vs-rivet-dev

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