Together AI
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About
Together AI is a comprehensive, research-driven platform designed specifically for the needs of AI-native companies and developers. At its core, it provides an infrastructure referred to as the AI Native Cloud, which aims to streamline the entire generative AI lifecycle, including training, fine-tuning, and production-scale inference. By leveraging frontier research, the platform allows users to interact with a library of open-source models—such as Llama, DeepSeek, Mistral, and Qwen—through OpenAI-compatible APIs, providing an alternative to closed-model ecosystems. The platform architecture is built for performance and operational reliability. It offers serverless inference for text, vision, image, and video models, alongside dedicated endpoints for requirements involving guaranteed performance and custom model support. For large-scale workloads, the service provides GPU clusters ranging from instant, self-service H100 instances to large-scale Frontier AI Factories designed for up to 100,000 NVIDIA GPUs. These clusters utilize high-speed interconnects like NVIDIA InfiniBand and NVLink, which are utilized to minimize latency during distributed training and inference tasks. This infrastructure is intended for developers, machine learning researchers, and enterprises requiring high-performance compute without vendor lock-in. It is used by organizations that prioritize open-source transparency and data privacy. Current implementations include companies like Hedra and Cursor, as well as researchers at Salesforce, who utilize the platform to manage inference latency and operational costs. The system is built to handle trillions of tokens with consistent performance at production scales. A defining characteristic of the platform is its integration with industry-standard AI research. The team behind the tool has contributed to technical advancements such as FlashAttention and the RedPajama datasets. This research background is applied to the platform's performance metrics, which include reported increases in inference and training speeds compared to standard frameworks. Additionally, the service ensures that users retain ownership of their fine-tuned models, allowing for greater flexibility in terms of data residency and provider choice.
Pros & Cons
Delivers up to 3.5x faster inference for top open-source models compared to standard frameworks.
Provides significant cost efficiency with some users reporting up to 60% savings on AI video generation.
Supports high-scale training with InfiniBand networking and clusters of over 100,000 GPUs.
Ensures full user ownership of fine-tuned models to prevent restrictive vendor lock-in.
Features a massive library of the latest open-source models including Llama, DeepSeek, and Qwen.
Pricing for image and video generation models can be complex as it fluctuates based on resolution and duration.
Fine-tuning jobs for certain specialized models like DeepSeek-R1 incur minimum charges per session.
Reserved Blackwell GPU clusters for frontier-scale training require direct sales contact and custom quotes.
Parallel filesystem storage is billed as a separate monthly cost of $0.16 per GiB.
Use Cases
AI-native startups can utilize serverless inference to handle viral traffic spikes for video or image generation with low latency.
Enterprise researchers can fine-tune open-source models on proprietary datasets while maintaining full ownership and data privacy.
Software developers can integrate high-speed coding assistants into their platforms using optimized models like Qwen-Coder.
Machine learning teams can rent dedicated H100 or H200 clusters with InfiniBand for large-scale model pre-training tasks.
Compliance officers can implement automated content filtering using integrated safety models like Llama Guard to monitor user inputs.
Platform
Task
Features
• openai-compatible apis
• flashattention optimization
• secure code execution sandbox
• infiniband & nvlink networking
• instant gpu clusters
• custom fine-tuning (lora & full)
• dedicated gpu endpoints
• serverless inference api
FAQs
Which models are available through the Serverless Inference API?
Together AI supports a wide array of open-source models including Llama 3.3, DeepSeek-V3, Mistral Small, Qwen3, and GLM-5. It also provides access to specialized models for image generation like FLUX.1 and video models like MiniMax Hailuo.
How does the platform ensure there is no vendor lock-in?
The platform focuses on open-source standards and guarantees that users own the models they fine-tune. This allows organizations to migrate their models to other providers or local environments at any time without being restricted by proprietary formats.
What kind of performance improvements can I expect for inference?
The platform utilizes research-backed accelerators like ATLAS to deliver up to 3.5x faster inference for top open-source models. Customers like Salesforce have reported a 2x reduction in time-to-first-token latency.
Does Together AI support custom model fine-tuning?
Yes, the platform supports both Supervised Fine-Tuning and Direct Preference Optimization (DPO). Users can choose between LoRA for efficiency or Full Fine-Tuning for maximum model customization across various model sizes.
What security and moderation tools are available?
Together AI provides integrated moderation models such as Llama Guard 3 and VirtueGuard. These models allow developers to filter and classify text and vision content for safety and compliance directly through the API.
Pricing Plans
Serverless Inference
USD0.18 / per 1M tokens• Access to Llama, Mistral, and Qwen
• Image and video generation APIs
• OpenAI-compatible integration
• Low-latency global endpoints
• Pay-as-you-go billing
• Vision and multimodal support
Dedicated Endpoints
USD2.10 / per hour• Single-tenant GPU instances
• Guaranteed performance
• Support for custom models
• Autoscaling capability
• Choice of H100 or L40S hardware
• Traffic spike handling
Instant GPU Clusters
USD2.99 / per hour per GPU• NVIDIA HGX H100 SXM access
• InfiniBand and NVLink networking
• Free network ingress and egress
• Choice of Kubernetes or Slurm
• Self-service deployment
• High-bandwidth parallel storage
Job Opportunities
AI Researcher, Core ML (Turbo)
Accelerate the generative AI lifecycle with high-performance GPU clusters and a serverless inference platform optimized for low-latency, cost-effective tasks.
Benefits:
competitive compensation
startup equity
health insurance
Education Requirements:
Advanced degree in Computer Science, EE, or a related field, or equivalent practical experience.
Experience Requirements:
3+ years of experience working on ML systems, large-scale model training, inference, or adjacent areas.
Demonstrated experience owning complex technical projects end-to-end.
Other Requirements:
Strong expertise in Systems-first profile, RL-first profile, or Model architecture design.
Comfortable working from algorithms to engines.
Solid research foundation in area(s) of depth.
Strong coding ability in Python.
Responsibilities:
Advance inference efficiency end-to-end
Unify inference with RL / post-training
Own critical systems at production scale
Provide technical leadership (Staff level)
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Customer Support Engineer, India
Accelerate the generative AI lifecycle with high-performance GPU clusters and a serverless inference platform optimized for low-latency, cost-effective tasks.
Benefits:
competitive compensation
startup equity
health insurance
Experience Requirements:
5+ years of experience in a customer-facing technical role with at least 1 year in a support function in AI
Strong technical background with knowledge of AI, ML, and GPU technologies
Familiarity with infrastructure services like Kubernetes and SLURM
Familiarity with operating storage systems in HPC environments such as Vast and Weka
Strong knowledge of Python, TypeScript, and/or JavaScript
Other Requirements:
Foundational understanding in compute clusters.
Excellent communication and interpersonal skills.
Ability to operate in dynamic environments.
Strong sense of ownership and willingness to learn.
Responsibilities:
Engage directly with customers to tackle and resolve complex technical challenges
Become a product expert in all Gen AI solutions
Collaborate seamlessly across Engineering, Research, and Product teams
Transform customer insights into action by identifying patterns in support cases
Maintain detailed documentation of system configurations and FAQs
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Director, Data Center Strategy and Site Selection
Accelerate the generative AI lifecycle with high-performance GPU clusters and a serverless inference platform optimized for low-latency, cost-effective tasks.
Benefits:
competitive compensation
startup equity
health insurance
Experience Requirements:
8+ years in data center strategy, site selection, or infrastructure planning at a hyperscaler or large colocation provider
Other Requirements:
Strong technical grasp of DC fundamentals (power architecture, cooling, rack density).
Experience leading large complex multi-party negotiations.
Knowledge of standard data center, power, and real estate contractual frameworks.
Financial fluency in TCO modeling and lease vs. own analysis.
Responsibilities:
Develop Together's global data center strategy
Own site selection and vendor relationships
Lead technical site diligence process
Negotiate and interface with executive and senior level management
Drive high-impact commercial and strategic transactions
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Ratings & Reviews
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