The latest AI news and updates of Together AI — AI-native cloud platform for inference, fine-tuning, and training open-source and custom models. Covering Together AI's latest product updates, company news, and analysis from the past 90 days.
Together AI23h agoTogether AI now supports running multiple LoRA experiments as separate sessions on a single shared GPU reservation. This capability allows parallel iteration across different learning rates or data mixes without reprovisioning infrastructure. The service bills per GPU-minute, and trained adapters can be compared side-by-side on a single multi-LoRA endpoint before deployment to production.
Together AIOct 26Together AI added MiniMax M3.1 Flash to its post-training service, supporting supervised fine-tuning and reinforcement learning. The platform executes training using custom data, rewards, and environments, while the training loop remains in local Python code. This integration targets the development of models for agentic workloads.
Together AIOct 23Together AI now serves MiniMax M3.1 Flash, a 428B-parameter multimodal Mixture-of-Experts model, as a day-0 launch partner. The model features a 1M-token context window and supports text, image, and video input for agentic, coding, and security workflows. It is available on Together AI serverless and dedicated infrastructure, priced at $0.15 per million input tokens.
Together AIOct 20Together AI reduced the price of post-training on NVIDIA B200 GPUs to $9 per GPU-hour, down from $11. This promotional rate applies for the first eight weeks of usage. The service supports supervised fine-tuning and reinforcement learning, allowing teams to maintain control of the training loop within their own Python code.
Together AIOct 16Together AI is offering free access to Meta's Muse Spark Open model on its serverless platform through Sunday, October 18 at 12 PM PT. After the promotion ends, the model routes to a paid endpoint priced at $1.25 per million input tokens and $4.25 per million output tokens.
Together AIOct 15Together AI launched Meta's Muse Spark Open, an 816B-parameter open-weights model optimized for agentic coding. The model features a 1M-token context window, multimodal perception, and native computer-use capabilities for desktop and mobile environments. It provides intent commentary for long-horizon tasks and is available on Together AI serverless and dedicated infrastructure at $1.25 per million input tokens.
Together AIOct 15Together AI has added NVIDIA Nemotron 3.5 Super VL to its serverless platform. The 120B-parameter Mixture-of-Experts model supports text, image, document, and video understanding, combining visual reasoning with tool use for agentic workflows. It is available via API at $0.15 per million input tokens and $0.50 per million output tokens.
Together AIOct 5Together AI launched Together Link, a tool connecting coding agents to open-source models. It features an Auto router that directs tasks to cost-effective or capable models based on complexity. The tool provides per-session spend tracking and installs with a single command, reducing agent costs by 50–80% compared to running every session on closed frontier models.
Together AISep 24Together AI released tev1-4B-experimental, a 4B parameter decision classifier fine-tuned on Qwen3.5 4B. The model processes structured state and question inputs to return a single-letter choice. Together AI published the data recipe and tutorial for the model, which cost $17 to train, and offers it on serverless inference at $0.042 per million input tokens with free output.
Together AISep 24Together AI is offering 40% off Qwen3.7-Max and Qwen3.8-Flash on its serverless platform through September 30. The platform recommends Qwen3.7-Max for long-horizon agent tasks and Qwen3.8-Flash for high-volume, cost-sensitive workloads.
Together AISep 23Together AI launched canary rollouts for Dedicated Model Inference, enabling staged traffic shifts between model versions. The platform performs health checks and post-step metric gates on p95 latency and error rates, pausing rollouts for human review if thresholds trip. The update supports canary, blue-green, and rolling strategies via the tg CLI, REST API, and Python SDK.
Together AISep 21Together AI published a case study detailing how a global fintech scaled its coding-agent workload using Dedicated Model Inference. The platform enabled self-service scaling and live configuration changes, resolving request queuing issues on its GLM-5.2 endpoint. The deployment runs at 256K context across 56 B200 GPUs, prioritizing concurrency to handle spiky, peak-load engineering traffic.
Together AISep 18Together AI reports that its serving path for DeepSeek V4.1 Flash leads on time-to-first-token for pre-warmed queries. The company notes that minimizing startup latency is critical for agent workflows, where the cost recurs on every model turn. This focus on the serving path aims to keep overhead low for long-running agentic loops.
Together AISep 16Together AI has added the MiniMax H3 model to its platform, enabling omni-modal video generation. The 33B parameter model processes text, image, video, and audio inputs to create 4–15 second clips at up to 2K resolution. It features native stereo audio generation, supporting dialogue across 11 languages, and is now available via the Together AI API.
Together AISep 11Together AI expanded its fine-tuning service with support for GLM-5.3, Kimi K2.7-Code, and other open-weight models. The update adds live experiment tracking, early stopping, dataset previews, and finer training controls like Expert LoRA. Training prices for selected models are now 30–70% lower, alongside automated pre-flight validation for uploaded datasets.
Together AISep 11Together AI introduced preemptible compute for its GPU clusters, offering the same NVIDIA infrastructure at a flat 50% of the on-demand rate. The service supports interruption-tolerant workloads like fine-tuning and batch inference, providing a five-minute drain window for checkpointing before node reclamation. Preemptible nodes automatically refill toward target capacity and are billed sub-hourly.
Together AISep 10Together AI ported its ThunderKittens kernel framework to the NVIDIA Vera Rubin NVL72 platform. The team reworked kernels to leverage the new ISA, achieving over 22 PFLOPS for NVFP4 and 12 PFLOPS for FP8 GEMMs. These results are competitive with cuBLAS and CuTe DSL, with the 16k NVFP4 GEMM reaching 22.4 PFLOPS through deepened pipelines and increased data reuse.
Together AISep 10Together AI reports that Kimi K3 scored 60% higher than Fable 5.1 on Harvey LAB-AA’s hard autonomous legal tasks. This benchmark measures the ability of models to complete complex, end-to-end legal work without human intervention.
Together AISep 2Together AI is partnering with Equinix and NVIDIA to launch Equinix Inference Exchange, a distributed platform for enterprise AI inference. The service runs on NVIDIA Enterprise Reference Architectures across Equinix data centers, supporting over 200 open-source models. It offers both multitenant and dedicated single-tenant deployment modes, with general availability scheduled for Q1 2027.
Together AISep 1Together AI reduced the price of Dedicated Inference on NVIDIA H100 GPUs from $5.49 to $3.99 per hour for September 2026. This lower rate applies automatically to both new and existing deployments. The service supports various open-weight models, including Llama 3.3, Qwen3.5, and Nemotron 3.5 Lightning, as well as custom fine-tuned LoRA checkpoints.
Together AIAug 31Together AI signed a deal with HUMAIN to construct a 250MW data center in Saudi Arabia. The project is projected to generate over $5 billion in annualized revenue. This partnership secures large-scale compute capacity for open-source AI, addressing the power constraints currently limiting infrastructure development.
Together AIAug 31Together AI reports that Z.ai’s GLM-5.3 model exhibits a lower hallucination rate than frontier competitors. The company found Claude Fable 5’s hallucination rate is over 2x higher, while GPT-5.6 Luna’s rate is over 3x higher. These findings establish the model as a reliable option for production workflows requiring factual accuracy.
Together AIAug 27Together AI introduced GLM-5.3 Flash, a natively multimodal model from Z.ai featuring 320B total parameters, 18B active parameters, and a 1-million-token context window. The model utilizes hybrid attention to match Luna’s performance on the DeepSWE benchmark while delivering twice the throughput for the same budget. It is coming soon to Together AI’s serverless API.
Together AIAug 25Together AI adds Alibaba's Qwen3.8 27B model to its platform for fine-tuning and Dedicated Model Inference. This integration supports training on custom data and deployment on dedicated production infrastructure within a single stack, removing the need to manage separate training and serving environments.
Together AIAug 23Together AI analyzed GLM-5.3 and Claude Fable 5 on the DeepSWE benchmark, finding a statistical tie in single-shot accuracy. GLM-5.3 cost 5.4x less per task and achieved an 87.6% solve rate across four attempts for approximately $16, compared to Fable 5’s 84.1% at $21.63. The results highlight GLM-5.3 as a cost-effective default for coding agents.
Together AIAug 14Together AI and Larsen & Toubro are building India's largest AI Factory in Chennai, featuring 10,000 NVIDIA B300 GPUs. The project, valued at up to $1.57 billion, provides dedicated infrastructure for large-scale open-source inference, fine-tuning, and training. This deployment marks the country's largest single-cluster AI infrastructure to date.
Together AIAug 13Together AI now hosts ByteDance’s Seedance 2.5, a video generation model capable of producing 30-second audio-video clips in a single pass. The model supports multi-round extensions for consistent long-form storytelling and offers timestamp-level editing for narrative and camera control. It accepts up to 50 multimodal reference inputs and is available on Together AI’s serverless infrastructure for $0.115 per video.
Together AIAug 13Together AI now hosts Qwen3.8-2.4T-A95B, a 2.4-trillion-parameter sparse Mixture-of-Experts model. The model features a 256K-token context window and native multimodal input, with adjustable reasoning effort levels for complex coding and long-horizon agentic tasks. It supports function calling and structured outputs for production-grade agent workflows.
Together AIAug 11Together AI, IBM, and NVIDIA signed a 240 million dollar multi-year agreement to deploy a dedicated NVIDIA HGX B300 cluster on IBM Cloud. Expected in Q1 2027, the cluster uses Spectrum-X networking to provide enterprise-grade inference for open-source models. This deployment marks the first large-scale inference cluster of its kind on IBM Cloud, designed for high-performance production workloads.
Together AIAug 11Together AI now hosts NVIDIA Nemotron 3.5 Lightning, a 30B-parameter hybrid Mixture-of-Experts model with 3B active parameters. Available through Dedicated Model Inference, the model supports 1M-token context windows for long-running agent sessions. It delivers up to 4x higher throughput and 30% faster task completion for specialized, high-volume agentic workflows.
Together AIAug 10Together AI now powers DeepSeek-V4-Flash-0731 on Ollama's cloud, providing the inference infrastructure for the 284B-parameter model. The deployment delivers over 120 output tokens per second with zero data retention and hosting in the US and Europe. This setup serves as the default for Ollama cloud users, supporting long-running sessions for coding agents.
Together AIAug 8Together AI analyzed DeepSeek-V4 Flash-0731 and GPT-5.6 Luna on DeepSWE software engineering tasks. The analysis shows DeepSeek delivers 80% of Luna’s performance at one-sixth the cost. A cascade strategy, running DeepSeek first and escalating to Luna on failure, solves 78.9% of tasks at $0.385 each, outperforming Luna alone in both accuracy and cost-efficiency.
Together AIAug 6Together AI now serves as an inference provider for Roomote, an agentic platform for multi-step workflows. The integration supports model mapping for task-specific execution, assigning distinct open models to coding, planning, vision, and review roles. Roomote provides direct selection of models like GLM 5.2 and MiniMax M3 within the platform interface.
Together AIAug 5Together AI now provides free, no-setup chat access to Moonshot AI’s Kimi K3 model through Together Chat. This interface allows direct prompting without requiring an API key or configuration. The service runs on Together AI’s secure North American infrastructure, providing a testing environment for the sparse mixture-of-experts model.
Together AIAug 4Together AI analyzed Moonshot AI’s Kimi K3 and OpenAI’s GPT-5.6 Sol on the DeepSWE benchmark. While Sol leads in single-shot accuracy, Kimi K3 wins on pass@4 at a lower cost. A Kimi-first cascade with test-suite verification reaches 85.6% accuracy, outperforming either model alone while reducing costs per solved task.
Together AIAug 4Together AI now serves DeepSeek V4 Flash on its inference platform. The company says the model delivers a major jump in coding, tool use, and long-running agent performance, with low, high, and max reasoning effort and DSpark speculative decoding for faster generation. Pricing starts at $0.14 per million input tokens and $0.28 per million output tokens.
Together AIAug 3Together AI analyzed Kimi K3 Max and Claude Fable 5 on the DeepSWE benchmark. While Fable 5 leads on pass@1 at 69.9% versus 68.5%, Kimi K3 Max wins on pass@4 at 89.4% versus 88.5%. Kimi K3 Max costs one-third as much per rollout, delivering 2.8 times more solved tasks per dollar for high-volume coding agent workloads.
Together AIAug 1Together AI now processes over 400 trillion inference tokens per month, marking a 10,000x increase from 30 billion tokens one year ago. This growth reflects a shift as enterprises and AI-native companies move production workloads to open-source models. The platform supports this scale through its production-grade inference infrastructure and provisioned throughput services.
Together AIAug 1Together AI published a deep-dive on autoscaling dedicated inference, noting that traditional CPU-style utilisation metrics often fail to capture real queue pressure. The guide recommends using in-flight requests as the default scaling metric, details how to configure scaling windows to avoid sawtooth oscillations, and provides measured cold-start costs for H100 GPUs alongside experimental results on scaling policies.
Together AIJul 31Together AI detailed its capacity-aware routing model for Dedicated Model Inference, which distributes traffic based on weight multiplied by ready replicas rather than fixed percentages. This architecture automatically scales traffic shares as deployments grow or shrink. It also supports advanced operations like rollouts, A/B tests, and shadow traffic by treating them as deployments with specific routing weights.
Together AIJul 30Together AI released ThunderAgent, a program-aware scheduler that eliminates KV cache thrashing in agentic workflows. By treating workflows as schedulable programs, it achieves 2.5x higher single-node throughput and 10x lower P50 latency at high concurrency. The open-source system supports near-linear multi-node scaling and integrates as a drop-in via a single program_id field.
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