AI21 Labs

A compact open foundation model (3 billion parameters) designed for on-device applications and agentic workflows, delivering reliability and steerability.

An efficient open foundation model that blends efficiency and steerability to deliver reliable output on core enterprise workflows.

A compact reasoning-focused model delivering record latency and a 256K context window for enterprise-grade reasoning tasks.

A family of open foundation models with a hybrid Mamba-Transformer architecture optimized for enterprise efficiency, featuring 256K context window and fast processing for document-heavy tasks.

An open foundation model optimized for private enterprise deployment with high reliability and security.

An open foundation model optimized for private enterprise deployment, combining the Jamba hybrid Mamba-Transformer architecture with a 256K context window for document processing and knowledge base search.

End-to-end enterprise AI solution design and deployment service covering architecture design, customization to enterprise data, and post-deployment optimization.

Novel scaling techniques applied at inference time to optimize agent performance across the pareto frontier of cost and quality.

Automated system that finds optimal model-harness pairing from countless configuration options to maximize agent effectiveness.

Dynamic routing system that distributes inference calls across an ensemble of models to reduce costs while maintaining frontier-quality outputs.

Frontier language model optimized for reliability and speed, available for model training and inference applications.

Family of efficient open foundation models for long-context processing. Delivers reliable outputs at high speed while maintaining enterprise data security and compliance.

AI agent orchestration platform with execution strategies, harness optimization, and intelligent model routing to optimize agent performance and cost at scale.

Stateful agent workspace system using Model Context Protocol for scaling state-modifying agents without state management conflicts.

A system for scaling state-modifying agents with shared state management using Model Context Protocol workspaces.

Orchestrated inference-time computation technique for elevating long-horizon agentic task performance, demonstrated on SWE-bench benchmarks.

Provides visibility into token usage and enables optimization of AI investments across deployments.