Think the precision of HAL 9000 combined with the loyalty and quiet brilliance of Alfred, Bruce Wayne best wingman. Behind the character is a real service: a developer-focused platform designed to simplify access to large language models (LLMs) through intelligent routing, prebuilt SDKs, and profile-based abstraction. Created by a small, passionate team of developers, Halfred was born from a need, not to revolutionize the AI model race itself, but to make working with it actually enjoyable. If you’re building AI-powered features, Halfred handles the integration, model selection, usage tracking, and pricing complexity for you so you can ship faster and stay focused on your product. Halfred is not a chatbot. It’s the infrastructure layer that serves the best model for the job, with zero friction.

What does Halfred offer and how does it work?

Halfred is an AI infrastructure service that gives developers instant access to powerful large language models (LLMs) through a single, unified API. Instead of selecting, maintaining, or comparing AI providers manually, Halfred routes requests to the best available model based on your selected profile, whether you need speed, cost efficiency, or deep reasoning.

Which AI models does Halfred support?

Halfred dynamically connects to models from major providers like OpenAI (GPT-4o, GPT-3.5), Anthropic (Claude 3), Google (Gemini), Mistral, DeepSeek, and more. Each usage profile (Standard, DeepThink, Lite, etc.) is backed by a curated selection of models and updated regularly to ensure performance and availability.

How does Halfred handle AI model pricing and costs?

Halfred uses a prepaid credit system. You pay in advance and consume credits with each request, depending on the model used. This approach ensures full transparency, there’s no hidden cost or post-billing. Each profile is optimized for different usage needs and price-performance ratios.

What is a token, and why does it matter?

A token is a small unit of text that AI models process to understand and generate language. Tokens are how usage is measured and billed in Halfred. Each AI model has a context limit, which is the maximum number of tokens it can handle in a single request, including both the input and the response. The more tokens you send, the more credits are used. Halfred handles model selection based on your profile, keeping token limits and efficiency in check.

Do I need to understand AI models to use Halfred?

No. Halfred was built to remove the complexity of selecting or managing AI models. By choosing a usage profile (e.g., “Lite” for low cost, “DeepThink” for advanced reasoning), Halfred automatically handles routing to the best model behind the scenes. One SDK, one integration, no AI expertise required.

About Us

We're building Halfred to solve a pain we've all felt:
> Using LLMs shouldn't require becoming an LLM expert.

Halfred is a simple, reliable layer on top of multiple LLM providers, so teams can ship real product value instead of fighting with models, APIs and billing dashboards.

The Team

Stéphane Mora

CEO

Product & strategy nerd.

Makes sure Halfred actually solves real problems for devs, PMs and technical founders - not just looks good in a deck.

Product & strategy nerd.

Makes sure Halfred actually solves real problems for devs, PMs and technical founders - not just looks good in a deck.

John Walter

CTO

Architecture and reliability guardian.

Owns the core platform, from infrastructure to security, so you can plug into Halfred and trust it in production.

Architecture and reliability guardian.

Owns the core platform, from infrastructure to security, so you can plug into Halfred and trust it in production.

Khouloud Dahmane

Head of R&D

Living in the LLM jungle so you don't have to.

Tests, compares and integrates models, making sure Halfred stays sharp, fast and stable as the ecosystem evolves.

Living in the LLM jungle so you don't have to.

Tests, compares and integrates models, making sure Halfred stays sharp, fast and stable as the ecosystem evolves.

Romain Taillandier

Head of Engineering

Leads the engineering team and delivery.

Turns ideas into shipped features, keeps the codebase clean, and cares a lot about developer experience.