Lambda vs Together AI: Inference & Compute Comparison
Compare Lambda and Together AI side by side. Both are tools in the Inference & Compute category.
Updated March 10, 2026
The short answer
Choose Lambda if highly competitive pricing for H100 and A100 GPUs.
Choose Together AI if competitive pricing starting at USD 0.10 per million tokens.
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Quick Comparison
| Category | Inference & Compute | Inference & Compute |
| Pricing | Usage-based | Usage-based |
| Best For | ML engineers and researchers who want simple, reliable GPU cloud infrastructure | Developers and companies deploying open-source AI models in production |
| Website | lambdalabs.com | together.ai |
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Pros & Cons: Lambda vs Together AI
- +Highly competitive pricing for H100 and A100 GPUs
- +Fast access to GPU resources within minutes
- +AI-focused infrastructure without generic cloud complexity
- +Strong NVIDIA partnerships ensure GPU availability
- −Hidden operational costs with complex instance management
- −No live chat or phone support available
- −Heavy dependency on NVIDIA supply chain
- −Inconsistent machine performance reported by some users
- +Competitive pricing starting at USD 0.10 per million tokens
- +50 percent discounts on batch inference and prompt caching
- +Strong backing from NVIDIA and Salesforce Ventures
- +Flexible deployment with both serverless and dedicated options
- −Focused on open-source models may limit access to proprietary models
- −Per-minute billing for dedicated GPUs can be complex to predict
- −Newer platform with less extensive documentation than established providers
- −Pricing varies significantly by model making cost planning difficult
Pricing: Lambda vs Together AI
- · 80GB GPU memory
- · 8x, 4x, 2x, or 1x GPU configurations
- · NVIDIA H100 Tensor Core
- · One-minute billing granularity
- · 80GB GPU memory
- · Multiple GPU configurations
- · NVIDIA A100 Tensor Core
- · Production-ready clusters
- · NVIDIA GH200 Superchip
- · Advanced AI workloads
- · Large-scale deployments
- · Custom cluster configurations
- · 16 to 2,000+ GPUs
- · B200 or H100 clusters
- · Production scale
- · Reserved capacity pricing
- · Pay-as-you-go
- · Multiple open-source models
- · USD 5 free credit
- · Llama 3 70B at USD 0.90/M tokens
- · Bulk processing discount
- · Async execution
- · Cost optimization
- · H100, H200 GPUs
- · Reserved capacity
- · Production workloads
- · Custom pricing
When to Choose Lambda vs Together AI
- ML model training and fine-tuning
- Inference serving
- Research and experimentation
- Hosting open-source LLMs in production
- Fine-tuning models on custom data
- Cost-efficient inference at scale
About Lambda
Lambda Labs is a pioneering provider of high-performance GPU cloud infrastructure and workstations, founded in 2012 by twin brothers Michael Balaban (CTO) and Stephen Balaban (CEO). Based in San Jose, California, Lambda has grown to serve more than 50,000 customers, offering GPU clusters featuring cutting-edge NVIDIA H100 and H200 chips that customers can access within minutes. The company's infrastructure is specifically designed for machine learning and AI development, providing an environment where models can be trained, fine-tuned, and deployed without the generic complexity of traditional cloud platforms.
Lambda has established itself as a cost-effective alternative to major cloud providers, offering NVIDIA H100 GPU instances at significantly lower hourly rates. The company's ability to provide fast access to GPU resources—often within minutes compared to longer wait times from competitors—has made it a popular choice for AI researchers and developers. Lambda's success is built on strategic partnerships with NVIDIA, securing priority allocation during chip shortages, though this also creates dependency on GPU availability and pricing.
With transparent pricing based on specific GPU types and instance configurations charged hourly on-demand or through reserved capacity arrangements, Lambda offers flexible deployment options. The company provides GPU billing granularity in one-minute increments, allowing cost-effective experimentation and production workloads. Lambda's production-ready clusters range from 16 to 2,000+ NVIDIA B200 or H100 GPUs, supporting projects from proof-of-concept to large-scale production deployments.
About Together AI
Together AI is a cloud-based platform for building with open-source generative AI, founded on June 11, 2022 in San Francisco by Ce Zhang, Chris Re, Percy Liang, and Vipul Ved Prakash. The company raised USD 305 million in Series B funding in 2025 with participation from industry leaders including NVIDIA and Salesforce Ventures. Together AI provides serverless inference with pay-as-you-go pricing starting from USD 0.10 per million tokens for small models and USD 0.90 for Llama 3 70B, with a free USD 5 credit to start. The platform offers a 50 percent discount on batch inference and 50 percent savings on prompt caching for repetitive queries. For teams requiring dedicated resources, Together AI provides GPU endpoints billed per minute, with high-end H100 and H200 GPUs available. The platform specializes in open-source model deployment and provides instant GPU clusters for training and inference workloads. Together AI has become a leading platform for teams building with open-source AI models, offering both serverless convenience and dedicated infrastructure options.
What is Inference & Compute?
Platforms that provide GPU compute, model hosting, and inference APIs. These companies serve open-source and third-party models, offer optimized inference engines, and provide cloud GPU infrastructure for AI workloads.
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