Description
AI21 builds infrastructure for companies running AI agents in production, rather than shipping its own end-user app. Its pitch is that most teams pay for far more tokens than their agents actually need, and that a generic model swap alone can't fix that without hurting quality.
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Its main product, AI21 Gateway, currently in beta, is a drop-in endpoint that sits between an agent and its models. Integration is a one-line change, pointing an agent's API base URL at the gateway, and it works with tools like Claude Code, Codex, LangChain or a custom harness without further code changes. The gateway builds a cost profile from an agent's own traffic, then trims and restructures redundant context, routes each turn to the right model based on task difficulty, and adjusts caching, weighing the whole run rather than optimizing single calls in isolation. AI21 reports a 68% cost reduction on an autonomous code-review agent and advertises token bills cut by up to 40% more broadly, with every savings recommendation checked against coding benchmarks before it reaches production. AI21 Gateway is free to use during its beta preview; enterprise features such as shared workspace access require contacting AI21 directly.
The second product, Post-Training, has AI21 work alongside a customer's own team to train a dedicated model on their data, then hand over the training recipe so the team can run further rounds themselves. AI21 pitches this as the cheaper path once a company's inference bill already exceeds what it would cost to host and train its own model.
AI21 has trained language models since 2021, when it released Jurassic-1, and says it was the second company in the world to ship a large language model. Its work includes the open-source Jamba model family and 11 published research papers on data mixtures, architecture and scaling. It's led by CEO Ori Goshen and chairman Prof. Yoav Shoham, a Stanford professor emeritus and former Google principal scientist.
AI21's tools are built for engineering and ML teams operating AI agents at scale who want lower inference costs or a model tuned to their own data, rather than for individual developers building a first prototype.
- CategoryMachine Learning Platforms
- Pricing ModelFreemium
- DeploymentCloud
- PlatformsWeb
AI21's Core Features
AI21 Gateway: a drop-in endpoint that reduces token cost on AI agent traffic
One-line integration; works with Claude Code, Codex, LangChain or a custom harness
Builds a per-agent cost profile from live traffic rather than generic heuristics
Trajectory-aware savings that weigh a whole agent run, not single calls in isolation
Context compaction and adaptive caching to cut redundant token spend
Savings recommendations validated against coding benchmarks before reaching production
Post-Training: custom model training on a company's own data, with the recipe handed over afterward
In-house pre-training research stack, including the open-source Jamba model family
How to use AI21?
Connect: point your agent's API base URL at the AI21 Gateway
Run traffic: let the Gateway build a cost profile from your agent's real usage
Review: check the savings recommendations the Gateway surfaces for your agent
Approve: confirm which changes to apply before they reach production
Get custom models: work with AI21's Post-Training team to train a dedicated model on your own data
AI21's Use Cases
- Agent cost reduction
- Coding agents
- Custom enterprise models
- Multi-framework agents
