Together.ai

What makes Together.ai different

Together.ai positions itself as an open-source–first alternative to proprietary model APIs. Rather than locking developers into closed ecosystems, Together hosts a diverse catalog of foundation models (both open and proprietary) on shared infrastructure, allowing developers to switch between models without rewriting code. The platform emphasizes transparency in both model selection and pricing—you see exactly which models are available, their performance characteristics, and their per-token cost.

The company’s collaborative fine-tuning feature sets it apart: teams can fine-tune open-source models (like Llama, Mistral, and others) on Together’s infrastructure without managing their own hardware, then seamlessly deploy those custom models to production. This lowers the barrier to entry for teams that want model customization but lack GPU infrastructure. Together also provides a public model leaderboard and hosts models from independent AI research organizations, positioning itself as infrastructure for the broader open AI community rather than a vendor pushing proprietary models.

Pricing model

Together.ai uses a usage-based pricing model measured in tokens (input and output). Pricing varies by model—smaller open-source models cost significantly less than larger ones or proprietary alternatives. For example, inference on smaller Mistral or Llama variants typically costs fractions of a cent per million tokens, while larger proprietary models command higher rates. Fine-tuning is priced separately based on GPU hours consumed.

The platform does not require long-term contracts and offers pay-as-you-go billing, making it accessible to startups and experimental projects. Volume discounts and custom enterprise agreements are available for high-throughput users. This model competes directly with OpenAI’s API and Anthropic’s pricing, but Together’s emphasis on open-source models means you can dramatically reduce costs by choosing smaller, efficient models when they fit your use case.

When it fits

• Open-source model adoption: Teams committed to using open models (Llama, Mistral, others) for cost control or data sovereignty.
• Model experimentation and comparison: Developers who want to test multiple models without managing separate APIs or infrastructure.
• Fine-tuning workflows: Organizations needing custom model training without buying their own GPUs.
• Cost-sensitive inference: Projects where per-token savings matter, such as high-volume text processing or chat applications.
• Research and prototyping: AI researchers and startups exploring model behavior before committing to production infrastructure.

When it doesn’t

Together.ai is not the right fit if you require guaranteed SLAs with financial penalties, ultra-low-latency inference on the scale of milliseconds, or proprietary models exclusively (e.g., GPT-4 or Claude). Organizations deeply invested in a single vendor’s ecosystem may find multi-model switching overhead not worth the benefit.

Inclusion criteria

Together.ai meets all three inclusion criteria:

• Transparent pricing: Token-level pricing publicly listed per model on their platform.
• Self-service signup: Instant account creation and API key generation via the website.
• Public SLA and status page: Status page available.