River

River

River builds personal AI that individuals own and shape — offering an API and tools to fine-tune and run personalized models locally and in the cloud, with a focus on privacy, ownership, and continual specialization.

River is ai tools software teams evaluate for personal & entertainment. Use this page to review pricing, integration signals, and the best alternatives before you commit.

Paid API Enterprise 80/100
#446 in AI Agents (446 tools)
Added 1 month ago
Data reviewed Jul 16, 2026

Profile facts come from the vendor source. AiMatch labels unknown pricing or API details instead of estimating them.

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Quick Overview

Best for: Personal & Entertainment

What it does

AI Tools software for decision-makers comparing workflow fit and alternatives.

Best fit

Personal & Entertainment

Pricing snapshot

Paid from Pay per token

Next step

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River

River is building personal intelligence that individuals own, control, and run themselves. The company focuses on interfaces, models & algorithms, infrastructure, and personal hardware to enable highly personalized, continually learning AI that aligns with a user's values. River provides an API and tooling to fine-tune and train models (LoRA fine-tuning and reinforcement learning) across open-source models ranging from ~35B to 1T parameters, intending to let users bring their data, shape model weights, and own the intelligence they create.

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Key Features

LoRA fine-tuning

Supports LoRA-based fine-tuning to adapt open-source models to user data and tasks.

Reinforcement learning

Offers reinforcement learning workflows to teach models new skills and turn them into agents.

Wide model support

Runs on a variety of open-source models ranging from approximately 35B to 1T parameters.

Python client and API

Provides a small Python client and an API to fine-tune, train, and deploy personalized models.

Pay-per-token billing

Uses token-based billing (pay per token) for usage.

Personal hardware & infrastructure

Emphasizes personal hardware and infrastructure built for continual specialization at scale.

Pricing

Pay-as-you-go

Pay per token
  • Token-based billing

Use Cases

Personalized assistants

Create an AI that is customized to an individual's preferences, values, and data so it behaves responsibly and aligned to the user.

Task-specific agents

Teach models new skills and convert them into cost-efficient agents for specific tasks via fine-tuning and reinforcement learning.

Local and private intelligence

Run and own intelligence on personal hardware to maintain ownership of data, models, and compute.

Integrations

Open-source models

Supports fine-tuning and reinforcement learning on a variety of open-source models.

Python client

Provides a small Python client to interact with the API and training workflows.

Benefits

Ownership: Users own the hardware, the data, and the intelligence they create.
Personalization: Models can be continuously specialized and aligned to individual values and needs.
Privacy & control: Emphasis on running intelligence locally on personal hardware and owning the stack.
Flexible model scale: Supports a wide range of open-source model sizes (≈35B–1T).
Accessible tooling: Small Python client and API enable developers to fine-tune and deploy models.

Limitations

No detailed pricing tiers or costs beyond 'pay per token' are listed on the page.
No API rate limits or detailed technical quota information are provided on the page.

Frequently Asked Questions

Claim this listing to publish FAQs.

Getting Started

  1. 1 Sign up on the River site
  2. 2 Explore the API documentation and examples available on the site
  3. 3 Use the Python client to fine-tune models (LoRA) or run reinforcement learning workflows with your data

Support

docs

Explore the API and examples via links on the site.

social

Company presence and contact options referenced as Connect: X and LinkedIn.

site contact

A 'Contact' link is present on the site (visit the main site for contact entry points).

API

Available: Yes
Documentation:

Explore the API and see examples via the site links ("Explore the API → See an example →").

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