Together
Together AI helps enterprises build and deploy open-source generative AI models faster and cheaper.
Together AI is an AI Acceleration Cloud platform that enables developers and enterprises to train, fine-tune, and deploy generative AI models with 2-3x faster inference than hyperscalers. Supporting 200+ open-source models across all modalities, it provides the complete AI lifecycle from training to inference without vendor lock-in or infrastructure management burden. The platform powers 450,000+ AI developers and enterprises including Salesforce, Zoom, and SK Telecom with enterprise-grade security, model ownership, and performance.
Problem solved
Organizations need a unified platform to train, fine-tune, and deploy generative AI models without managing their own data centers, vendor lock-in constraints, or slow inference speeds.
Target customer
AI-native companies, enterprise engineering teams, and developers building generative AI applications who want to avoid vendor lock-in and reduce infrastructure complexity.
Founders
V
Vipul Ved Prakash
CEO & Co-Founder
Serial entrepreneur who co-founded Sense/Net, Cloudmark, and Topsy Labs (acquired by Apple); recognized as MIT TR 100 under 35 at age 25 for anti-spam innovations.
C
Ce Zhang
CTO & Co-Founder
Expert in decentralized and distributed training, learning systems, and data-centric MLOps.
C
Chris Ré
Co-Founder
Leading AI researcher in model architectures and infrastructure; previously founded Lattice.io (acquired by Apple in 2017).
P
Percy Liang
Co-Founder
Leading AI researcher specializing in model architectures and infrastructure.
Funding history
Seed
$20M
May 2023
Led by Unknown
· Unknown
Series A
$102.5M
November 2023
Led by Prosperity7 Ventures
· Unknown
Series B
$106M
March 2024
Led by Salesforce Ventures
· Unknown
Series B Extended
$305M
February 2025
Led by General Catalyst
· Prosperity7, Salesforce Ventures, DAMAC Capital, NVIDIA, Kleiner Perkins, March Capital, Emergence Capital, Lux Capital, SE Ventures, Greycroft, Coatue, Definition, Cadenza Ventures, Long Journey Ventures, Brave Capital, SK Telecom, John Chambers
Total raised:
$534M
Pricing
Usage-based across three buckets: Serverless Inference charged per million tokens ($0.06/M input tokens minimum, 50% discount via Batch API), Fine-Tuning charged per token processed, and GPU Clusters on hourly or reserved basis (6+ days). Free tier includes $25 in credits; startup accelerator offers $15K-$50K in credits.
Notable customers
Salesforce, Zoom, SK Telecom, 450,000+ AI developers and AI-native companies
Integrations
FlashAttention-3 kernels, advanced quantization techniques
Tech stack
GSAP (JavaScript frameworks)
Swiper (JavaScript libraries)
jQuery (JavaScript libraries)
core-js (JavaScript libraries)
Prism
Open Graph
LottieFiles
Google Analytics (Analytics)
Google Workspace (Email)
Unpkg (CDN)
jsDelivr (CDN)
cdnjs (CDN)
Cloudflare (CDN)
Webflow (Page builders)
Sendgrid (Email)
Website
Competitors
OpenAI API
Proprietary models with vendor lock-in versus Together's open-source model flexibility and lower inference costs.
AWS SageMaker
Broader AWS ecosystem integration versus Together's specialized focus on open-source model acceleration and faster inference.
Hugging Face
Model hub and community versus Together's end-to-end platform with inference optimization, fine-tuning, and GPU infrastructure.
Anthropic Claude API
Proprietary frontier models versus Together's 200+ open-source models across all modalities with cost and performance advantages.
Why this matters: Together AI has achieved unicorn status ($3.3B valuation) in 18 months, backed by leading institutional investors including Salesforce Ventures, General Catalyst, and NVIDIA. The company addresses a critical gap between open-source AI commoditization and enterprise demands for performance, security, and cost—positioning it as infrastructure-layer winner in the generative AI stack.
Best for: AI teams and enterprises that need fast, cost-effective inference on open-source models, want to avoid vendor lock-in, or require end-to-end training, fine-tuning, and deployment capabilities without managing their own infrastructure.
Use cases
Enterprise AI Application Deployment
Large organizations like Salesforce deploy generative AI features in production without vendor lock-in. Together's 2-3x faster inference and 50% cost savings via Batch API enable real-time and batch AI workloads at enterprise scale while maintaining model ownership and security.
Custom Model Fine-Tuning
AI teams fine-tune open-source models on proprietary data for domain-specific tasks (customer support, content generation, code analysis). Together's token-based pricing and LoRA support make iterative fine-tuning cost-effective compared to training from scratch.
Rapid AI Experimentation
Startups and researchers test 200+ models across modalities (chat, vision, audio, code, embeddings) without procurement friction. The $25 free credit tier and $15K-$50K startup grants lower barriers to building and validating AI products.
Synthetic Data Generation for ML Pipelines
Data teams generate labeled synthetic datasets using Together's generative models to train downstream ML models, reducing annotation costs and time-to-model without exposing sensitive data.
Alternatives
Replicate
Simpler inference API for running community models versus Together's full-stack platform with training, fine-tuning, and dedicated GPU infrastructure.
Modal
General-purpose serverless compute for any workload versus Together's specialized optimization for generative AI inference and training workflows.
Lambda Labs
On-demand GPU compute rental versus Together's integrated platform with optimized inference engines, model libraries, and end-to-end AI lifecycle management.
FAQ
What does Together AI do? +
Together AI is an AI Acceleration Cloud that enables developers and enterprises to train, fine-tune, and deploy 200+ open-source generative AI models. It provides end-to-end capabilities from serverless inference to GPU clusters, with proprietary optimizations delivering 2-3x faster inference than hyperscalers. The platform eliminates infrastructure management complexity while avoiding vendor lock-in.
How much does Together AI cost? +
Pricing is usage-based: Serverless Inference starts at $0.06 per million input tokens (50% discount via Batch API), Fine-Tuning is charged per token processed, and GPU Clusters are billed hourly or on reserved basis. New users get $25 free credits; startups can access $15K-$50K in credits through the accelerator program.
What are alternatives to Together AI? +
Replicate offers simpler inference APIs for community models; Modal provides general-purpose serverless compute; Lambda Labs offers raw GPU rental. OpenAI, AWS SageMaker, and Hugging Face are alternatives, but Together differentiates through open-source focus, faster inference, lower costs, and end-to-end platform capabilities.
Who uses Together AI? +
Target customers include AI-native startups, enterprise engineering teams, and developers building generative AI applications. Notable users include Salesforce, Zoom, SK Telecom, and 450,000+ registered AI developers building production AI systems.
How does Together AI compare to AWS SageMaker? +
Together specializes in open-source generative AI with 2-3x faster inference via proprietary kernels and quantization, while SageMaker is a broader ML platform within AWS ecosystem. Together avoids vendor lock-in and offers simpler pricing for inference-heavy workloads; SageMaker is better for teams already in AWS needing full ML lifecycle tooling across all model types.
Tags
generative ai
model training
fine-tuning
inference optimization
open-source models
GPU cloud
ai infrastructure
model deployment
vendor lock-in avoidance
machine learning