Is AI21 shutting down?

No. AI21 Labs still operates and now positions itself around post-training and custom model training. What ended on 9 August 2026 was the hosted Jamba API, the Maestro API and the file library.

Do my AI21 API keys still work?

The sunset endpoints stopped serving on 9 August 2026, so calls to them no longer return results. AI21 said a new platform is on the way and existing accounts can use it; either way, production traffic needs to move to another provider now.

Which alternative is closest to Jamba?

Jamba was a hybrid Mamba-Transformer model built for long context. On hosted platforms you would look for a long-context open model, which currently means checking the catalogues at Together AI, Fireworks AI and DeepInfra. Verify that the specific model is still served before you commit.

5 AI21 Jamba and Maestro Alternatives in 2026 (APIs Sunset in August 2026)

Pricing data as of Nov 7, 2026 - verify current rates on the official site before purchase

AI21 is still a company — it cut about 60% of its staff in May 2026 and stopped selling standalone models, then sunset the Jamba API, the Maestro API and the file library on 9 August 2026 as it moved into custom model training. What ended is the hosted endpoint, not the business. If you were calling jamba-* or Maestro, the five platforms below do the same basic job: serve a model behind an HTTP API you call with a key.

1. OpenRouter

OpenRouter is the least disruptive first move. You keep one key and one API shape, and it routes to whichever provider is serving the model you ask for. If your Jamba calls leaned on long context, you can point at a long-context open model from another lab without wiring up a second SDK. The trade-off: availability and price differ per provider behind the router, so pin the model rather than relying on a default.

2. Groq

Groq is the option to pick when latency mattered more than catalogue breadth. It runs models on its own LPU hardware and exposes an OpenAI-compatible endpoint, so a client swap is usually a base-URL change. The limit is the model list — it is curated rather than open-ended, so check that the model you need is actually hosted before you plan a migration around it.

3. Fireworks AI

Fireworks AI sits between serverless and owning the deployment: you can call shared endpoints or dedicate GPUs to a specific model when traffic justifies it. That makes it the closest match for teams that liked having a named model with predictable throughput, which is what a production Jamba integration usually needed. Serverless is billed per token; dedicated deployments per GPU-hour.

4. DeepInfra

DeepInfra is the budget route for popular open models. It keeps an OpenAI-compatible surface, so the same client works, and pricing is per token with no platform fee. The trade-off is ecosystem depth: fewer tuning options and less hand-holding than the platforms above, which matters if your migration is more than a base-URL change.

5. Together AI

Together AI is the broadest catalogue on this list and the one that also sells fine-tuning, which is relevant because AI21's own direction was custom model training. If your plan is to keep a specialised model rather than rent a general one, you can start on hosted inference and move to a tuned model on the same platform instead of changing vendors twice.

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