DiffSharp

Automatic differentiation library for machine learning and optimization.

DecliningOpen SourceLow lock-in

Pricing

Free tier

Flat rate

Adoption

Cooling

License

Open Source

Data freshness

Aging · Jun 8, 2026

Overview

What is DiffSharp?

DiffSharp is an automatic differentiation library that provides exact and efficient derivatives for various applications in machine learning and optimization. It supports nested operations, enabling higher-order derivatives and functions internally using differentiation.

Key differentiator

DiffSharp stands out by offering precise automatic differentiation capabilities, especially for nested operations and exact higher-order derivatives, making it ideal for research and complex optimization tasks.

Capability profile

Capability Radar

Ease of StartEcosystemValueMaturityFlexibilityScale Ready

Honest assessment

Strengths & Weaknesses

↑ Strengths

Exact and efficient derivatives for machine learning applications.medium

Supports nested operations for higher-order derivatives.medium

Provides various derivative types including gradients, Hessians, Jacobians.medium

↓ Weaknesses

Limited language supporthigh

DiffSharp primarily supports F# and has limited support for other languages, which can restrict its usability in multi-language projects.

Small community and limited resourcesmedium

The open-source nature of DiffSharp means that it relies on a smaller community for contributions and support, potentially leading to slower development cycles and fewer feature updates.

Complex setup processhigh

Setting up DiffSharp requires understanding F# and its ecosystem, which can be challenging for developers unfamiliar with the language or its tooling.

Documentation is not comprehensivemedium

The documentation may lack detailed examples and explanations for advanced features, making it difficult for new users to fully leverage the library's capabilities.

Fit analysis

Who is it for?

✓ Best for

Developers working on complex optimization tasks that require exact derivatives.

Researchers needing to experiment with higher-order derivatives in their models.

✕ Not a fit for

Projects requiring real-time differentiation due to computational overhead.

Applications where approximate methods are sufficient and faster.

Cost structure

Pricing

Free Tier

Available

Open source — free to use

Starts at

$0

Model

Flat rate

Enterprise

None

Performance benchmarks

How Fast Is It?

Ecosystem

Relationships

Next step

Get Started with DiffSharp

Step-by-step setup guide with code examples and common gotchas.

View Setup Guide →