Together AI

Together AI is a high-performance cloud platform designed for developers, researchers, and engineering teams who need scalable, production-grade access to cutting-edge open-source AI models via simple APIs. Whether you're integrating Llama 3 into a customer support bot, fine-tuning Qwen for domain-specific tasks, or running batch inference at scale, Together AI delivers speed, flexibility, and cost efficiency without vendor lock-in.

What is Together AI?

Together AI is a unified inference and fine-tuning platform that abstracts away infrastructure complexity while providing direct, low-latency access to over 100 rigorously benchmarked open-source models—including Llama 3 (8B–405B), Mistral 7B/8x22B, Qwen 2.5 (1.5B–72B), Mixtral 8x7B, Stable Diffusion XL, and Phi-3. Unlike general-purpose cloud providers or closed-model APIs, Together AI specializes exclusively in open weights: every model is fully inspectable, customizable, and deployable with consistent API contracts. Its distributed inference engine—built on optimized CUDA kernels and custom quantization—enables sub-200ms p95 latency for 7B models and efficient multi-GPU scaling for trillion-parameter MoEs. This focus on open models, combined with transparent token-based pricing and native fine-tuning tooling, makes it a strategic choice for teams prioritizing reproducibility, compliance, and long-term model ownership.

Key Features

• 100+ Production-Ready Open Models: Curated catalog spanning LLMs, multimodal models, and text-to-image generators—all pre-optimized, versioned, and updated regularly with community and vendor releases.
• Unified REST/gRPC API: Single endpoint interface with consistent request/response schemas, streaming support, and built-in retry logic—reducing integration time from days to minutes.
• One-Click Fine-Tuning: Web UI and CLI-driven fine-tuning using LoRA, QLoRA, and full-parameter methods; supports custom datasets, validation metrics, and automatic checkpointing.
• Real-Time Inference Dashboard: Monitor latency, throughput, token usage, and error rates across models and endpoints with granular filtering and exportable logs.
• Enterprise-Grade Infrastructure: SOC 2 Type II compliant, private VPC options, dedicated GPU clusters, and optional model caching for ultra-low-latency repeated queries.

Who Should Use Together AI?

Together AI is ideal for software engineers building AI-native applications, ML researchers conducting model comparisons or ablation studies, and DevOps/SRE teams managing inference workloads in production environments. It’s especially valuable for organizations in regulated industries (finance, healthcare, government) requiring model transparency and data residency controls—or startups needing predictable, usage-based pricing instead of opaque monthly commitments. While basic API familiarity is required, its comprehensive documentation, Python SDK, and Postman collections lower the barrier for mid-level developers—not just PhD-level ML practitioners.

Pricing

As of 2026, Together AI operates on a transparent, consumption-based model: new users receive a $25 free credit valid for 30 days. Paid usage starts at $0.10 per million input tokens and $0.20 per million output tokens for Llama 3 8B; larger models scale linearly (e.g., Llama 3 70B: $0.70/$1.40 per million tokens). Enterprise plans begin at $1,500/month and include priority support, SLA guarantees (99.9% uptime), custom model hosting, and private endpoint routing—no minimum commitment beyond the first billing cycle.

Pros and Cons

Bottom Line

Together AI excels when you need production-ready, high-throughput access to open-weight models with fine-grained control, auditability, and predictable pricing—making it a top-tier choice for engineering-led AI initiatives. Developers and ML teams gain maximum value if they’re already comfortable with REST APIs, containerized deployments, or CI/CD-integrated model pipelines. Compared to alternatives like Anthropic or OpenAI, it trades convenience and polished UX for openness and cost control; versus self-hosting, it eliminates GPU provisioning, monitoring, and scaling overhead—delivering the agility of SaaS with the sovereignty of open source.