Overview

The Challenge: Bringing Rust Performance to a C# Ecosystem

LanceDb is engineered for speed, concurrency, and efficient semantic search—thanks to its Rust foundation and async-first architecture. However, its lack of a network service mode and primary focus on Python made it difficult to adopt for teams outside the Python ecosystem.

Our client’s AI infrastructure was built in C#. Replacing their stack wasn’t an option. They needed a way to access LanceDb’s performance while keeping their .NET development model intact.

The Goal: Performance Without Compromise

The engineering team wanted more than just access to LanceDb—they needed a stable, idiomatic integration that worked with their tools and supported:

• Seamless async Rust interoperability from .NET
• Clean, familiar C# interfaces
• Predictable, production-grade behavior under load

Our Solution: Engineering the Bridge

Phase 1: A C-Compatible Rust Library

We wrapped LanceDb in a Rust library with a C-style interface, carefully managing async initialization, threading, and memory boundaries. This allowed .NET code to invoke high-performance operations in Rust with safety and clarity.

Key features included:

• Thread-safe request routing to async Rust tasks
• Clean error propagation and resource management across the FFI boundary
• Minimal, well-documented function surfaces to simplify usage

Phase 2: Native C# Integration Layer

On the .NET side, we built a dedicated C# wrapper library that connected to the Rust binary. It handled memory ownership, marshaling, and surfaced LanceDb functionality through a modern, ergonomic API.

We focused on:

• Efficient data translation across native boundaries
• Catching and reporting Rust errors cleanly in C#
• Supporting safe parallel usage in real-world applications

Beyond the Bridge: Enhancing the Platform

LanceDb’s most advanced features were available only in its Python SDK. These were essential for production use, so we extended the Rust core and exposed the necessary functionality to C#.

This ensured the engineering team could work entirely within their preferred environment—without compromises or workarounds.

Outcomes That Made a Difference

Rapid Prototyping

Teams were able to ship features faster using tools they already knew, without sacrificing performance.

Operational Confidence

The integration was reliable from day one. With robust error handling, resource cleanup, and predictable behavior, it was ready for production immediately.

High-Performance AI at Scale

The combination of Rust’s async engine and .NET’s rich development ecosystem enabled efficient, high-concurrency inference with minimal latency and overhead.

Building for the Future

From the outset, we designed the solution for long-term sustainability, not just short-term results. We worked closely with the client’s engineering leadership to:

• Document every integration point
• Build reusable examples and helper libraries
• Enable future extensions with minimal friction

The result: full ownership for their internal teams, along with the clarity and confidence to evolve the platform independently.

Rethinking What’s Possible in AI Infrastructure

This project shows what’s possible when systems-level expertise meets application-level pragmatism.

Integration isn’t just about making things connect—it’s about unlocking performance, enhancing maintainability, and empowering developers to move fast without rewriting their workflows.

Rust and C# aren’t usually part of the same system. But with careful design and deep collaboration, they can work together to push the boundaries of what AI infrastructure can achieve.