Introduction

Together AI is a cloud-native platform designed to simplify and accelerate the development, fine-tuning, and deployment of generative AI applications. As demand for scalable AI infrastructure rises, organizations are increasingly seeking platforms that combine high-performance compute, access to powerful open-source models, and flexible APIs. Together AI responds to this need by offering a modular, enterprise-friendly solution that supports everything from experimentation to production-level deployment. Its positioning as a performance-optimized, transparent alternative to proprietary vendors has made it a popular choice for startups and growing AI teams.

Overview

Together AI serves as a complete stack for generative AI workloads. The platform provides users with access to hundreds of open-source models—ranging from chatbots to multimodal systems—and a suite of tools for inference, fine-tuning, and deployment. What differentiates Together AI is its emphasis on high-speed performance, flexible pricing, and production-ready infrastructure. The platform is optimized for performance using techniques such as speculative decoding, quantization, and FP8 kernels. Additionally, it supports deployment in single-tenant environments for customers requiring enhanced data governance.

Together AI's infrastructure is backed by high-end GPUs, including GB200, B200, and H100 clusters. Customers can engage with the platform via RESTful APIs or SDKs, making it highly accessible to both developers and enterprise engineering teams.

Key Features

AI Acceleration Cloud: Provides access to high-performance GPU compute clusters for both inference and fine-tuning, significantly reducing model latency and training time.

Open-Source Model Hub: Hosts over 200 models across categories, including text, code, image, and multimodal. Models include LLaMA, DeepSeek, Qwen, and MoE variants like Mixtral and DBRX.

OpenAI-Compatible Endpoints: Supports drop-in replacement for proprietary APIs, simplifying migration for teams previously using closed platforms.

High-Performance Inference Engine: Enables inference speeds up to 4× faster than traditional deployments, leveraging advanced compute optimization techniques.

Dedicated & Serverless Deployment: Offers both on-demand endpoints and serverless APIs, accommodating different deployment needs and cost structures.

End-to-End Fine-Tuning: Users can fine-tune models using full training pipelines or lighter-weight methods like LoRA, with full data control.

Enterprise-Grade Security: Provides SOC 2 and HIPAA compliance, plus private VPC deployment options for customers with strict regulatory requirements.

Ideal Use Cases

AI Product Development: Ideal for building applications such as chatbots, coding assistants, and search agents with high performance requirements and real-time response needs.

Enterprise AI Workloads: Supports internal tools for document summarization, data classification, and retrieval-augmented generation pipelines within secure, scalable environments.

Custom Model Fine-Tuning: Enables teams to adapt foundation models to proprietary data without needing to manage GPU infrastructure themselves.

Cloud-Native Startups: Offers scalable, usage-based pricing and OpenAI-compatible APIs, making it easy for teams to test, iterate, and deploy at scale.

Pricing and Commercial Strategy

Together AI employs a transparent, tiered pricing model designed to support individual developers, scaling teams, and large enterprises alike. Customers can select from different levels of throughput, GPU access, and service guarantees depending on their needs:

Build Tier: Entry-level option with free credits, access to base models, and generous request limits (e.g., 6,000 requests/min, 2 million tokens/min). Designed for early-stage developers and experimentation.

Scale Tier: Offers higher throughput (up to 9,000 req/min), private support channels, SLA-backed performance, and HIPAA compliance. Tailored for startups and growth-stage teams.

Enterprise Tier: Includes geo-redundant deployment, private VPC, unlimited tokens, priority access to GPU clusters, 99.9% SLA, and long-term monitoring data retention. Ideal for regulated industries or mission-critical AI systems.

Additionally, Together AI supports usage-based pricing for inference and training:

• Model inference is charged per million tokens, with separate input and output pricing.

• Fine-tuning is billed per million training tokens, varying by model size and method (e.g., full training vs. LoRA).

• Dedicated GPU endpoints are billed per minute of usage, with high-end GPUs (H100, H200) priced accordingly.

• Batch inference is offered at a 50% discount to encourage bulk processing.

This hybrid model - combining predictable per-token costs with flexible infrastructure pricing - gives customers precise control over usage and spend.

Competitive Positioning

Versus Fireworks AI: Both platforms emphasize open-source model access and performance. Together AI’s fine-tuning pipeline and broader model selection distinguish it in terms of flexibility. Fireworks may have an edge in multimodal support and inference latency.

Versus OpenAI and Anthropic: Together AI provides comparable quality for many generative use cases but adds transparency, open-source foundations, and infrastructure choice. For organizations seeking to avoid vendor lock-in or fine-tune models on proprietary data, Together is often more appealing.

Versus Ollama or LM Studio: While Ollama and LM Studio focus on local inference, Together AI targets production deployment and performance at scale. These platforms serve different needs—Together AI offers cloud-native flexibility, while local tools prioritize sovereignty and simplicity.

Benefits and Limitations

Future Outlook

Together AI is well-positioned to capture the growing demand for open-source model deployment at scale. As enterprises seek more control over their AI infrastructure and aim to reduce dependence on closed platforms, Together’s ability to deliver fine-tuned, performant, and secure AI workloads becomes increasingly relevant.

Future developments may include improved multimodal tooling, expanded geographic presence, and deeper integrations with enterprise security frameworks. As the platform matures, it may evolve into a key enabler of enterprise-grade open-source AI adoption.

Conclusion

Together AI offers a robust, developer-friendly solution for teams building and deploying generative AI applications in the cloud. Its combination of performance, transparency, and flexible infrastructure positions it as a compelling alternative to proprietary vendors. With broad model support, fine-tuning capabilities, and enterprise security, Together AI serves as a foundation for modern AI products across industries.