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Together AI
Together AI offers cloud services for inference, fine-tuning, training, and GPU infrastructure for AI models.
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Overview
It supports open models and documents an OpenAI-compatible API, but API shape, model availability, service features, privacy settings, and licenses can vary.
Deep Dive
Together AI offers inference APIs, fine-tuning, and GPU-cluster services. Its OpenAI-compatibility documentation describes supported SDK methods and lists unsupported or partially supported endpoints, so compatibility is not necessarily complete. The company helped release RedPajama datasets; the Data-v2 announcement describes a filtered and deduplicated 30-trillion-token corpus with quality annotations. Together documents speculative decoding and other inference optimizations, but speed depends on model, workload, hardware, and service configuration. Its privacy policy states that it does not use user data to train models without explicit opt-in and describes a Zero Data Retention setting for prompts and responses; review current product settings and terms for the chosen service. RedPajama is a separate open-data project associated with Together; RedPajama-Data-v2 is a dataset release, while Together’s hosted inference APIs are a different service.
Real-World Implementation
Point a compatible client at Together’s documented base URL after checking endpoint and parameter compatibility.
Compare serverless inference, dedicated model inference, and GPU clusters for a specific workload.
Review the privacy settings and terms before submitting prompts, files, or fine-tuning data.
Consult the RedPajama dataset documentation when evaluating its research data rather than assuming it is a hosted model.
The Future of Together AI
Together’s model catalog, GPU hardware, pricing, and API support change over time. Check current documentation and terms before selecting a model or deployment; evaluate compatibility, data handling, latency, and cost for the intended workload.
Putting it into practice
Why it matters
- Vendor strategy. Vendor roadmaps influence what features your team can build next.
- Cost and budget. Commercial terms and deployment options affect long-term cost and risk.
- Risk and safety. Company incentives shape product defaults, safety posture, and openness.
Watch out for
- Launch announcements may outpace stability in real production workflows.
- API pricing or policy shifts can break assumptions overnight.
- Single-vendor dependency increases lock-in and migration costs.
Next steps
- Evaluate providers using your own tasks and datasets.
- Review privacy, security, and legal terms before integration.
- Maintain a fallback plan across models or vendors.
- Monitor release notes so roadmap changes do not surprise teams.
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Frequently asked questions
What is Together AI?
Together AI offers cloud services for inference, fine-tuning, training, and GPU infrastructure for AI models. It supports open models and documents an OpenAI-compatible API, but API shape, model availability, service features, privacy settings, and licenses can vary.
How should Together AI’s hosted model catalog be described?
Together hosts open-weight models, but openness and license terms are specific to each checkpoint and do not make every model open source under the same terms.
What is RedPajama in Together AI’s project history?
Together describes RedPajama as its open-data effort and separately announces releases such as RedPajama-Data-v2. That project identity is distinct from the hosted inference API.
What does “OpenAI-compatible” mean for a Together API integration?
Together documents compatibility for supported endpoint and request formats; users still need to verify the base URL, model ID, endpoint, and parameter support.
Which inference technique does Together discuss for high-throughput long-context inference?
Together’s speculative-decoding article discusses that technique for inference throughput; observed performance depends on the model, workload, and serving setup.
What should an enterprise verify before sending prompts or files to a hosted model service?
Open-weight access and API compatibility do not by themselves determine data handling. Together documents privacy settings, retention options, and service-specific terms that users should review.
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