Lambda Labs vs Together AI Comparison
Detailed comparison of features, pricing, and capabilities
Last updated May 1, 2026
Overview
Compare key metrics and features at a glance
Lambda Labs
https://lambdalabs.com
Lambda Labs (also known as Lambda) is a cloud computing and hardware company specializing in GPU-based infrastructure for AI and machine learning workloads. The company offers on-demand and reserved GPU cloud instances, as well as on-premise GPU servers and workstations, designed for training and deploying deep learning models. Lambda serves researchers, startups, and enterprises seeking high-performance compute at competitive pricing compared to hyperscale cloud providers.
Together AI
https://www.together.ai
Together AI is a cloud platform that enables developers and enterprises to run, fine-tune, and deploy open-source large language models (LLMs) at scale with high performance and cost efficiency. The platform provides access to a wide range of open-source models including LLaMA, Mistral, and others through a unified API, along with tools for custom model fine-tuning and inference optimization. Together AI also conducts AI research and has developed its own inference infrastructure designed to deliver fast and affordable generative AI capabilities.
Quick Comparison
| Detail | Lambda Labs | Together AI |
|---|---|---|
| Category | AI Cloud Infrastructure | AI Cloud Infrastructure |
| Starting Price | $496.8/mo | Free |
| Plans Available | 9 | 6 |
| Features Tracked | 15 | 15 |
| Founded | 2012 | 2022 |
| Headquarters | San Francisco, USA | San Francisco, USA |
Features
Detailed feature-by-feature comparison
Feature Comparison
| Feature | | |
|---|---|---|
| api | ||
| API Monitoring | ||
| OpenAI-Compatible APIs | ||
| core | ||
| 1-Click Clusters | ||
| Autoscaling GPU Clusters | ||
| Bare Metal Instances | ||
| Block Storage | ||
| Dedicated Model Inference | ||
| Fine-Tuning Workflows | ||
| Full-Stack Observability | ||
| GPU Instances | ||
| High-Performance Inference | ||
| Instant GPU Clusters | ||
| Kubernetes & Slurm | ||
| Lambda Stack | ||
| NVIDIA GPU Support | ||
| NVIDIA InfiniBand | ||
| No Egress Fees | ||
| Pay by the Minute | ||
| Pay-As-You-Go Pricing | ||
| Private Cloud | ||
| Self-Healing Clusters | ||
| Serverless Inference | ||
| Superclusters | ||
| Zero Egress Fees | ||
| Zero Throttling | ||
| integration | ||
| Open-Source Model Hub | ||
| SDK Support | ||
| security | ||
| Biometric Access | ||
| Single-Tenant Clusters | ||
| support | ||
| Dashboard Monitoring | ||
Pricing
Compare pricing plans and value for money
Lambda Labs
From $496.8/mo
Price Components
- GPU Hour: $9.86/hour
- Reserved Capacity: $0/cluster
- GPU Hour: $6.16/hour
- GPU Hour: $6.69/hour
- GPU Hour: $3.99/hour
Best For
ML researchers and startups running large-scale distributed training jobs who prioritize cost efficiency and hardware control over managed service breadth.
Together AI
From $0/mo
Price Components
- GLM-5.1 Input Tokens: $1.4/1M tokens
- GLM-5.1 Output Tokens: $4.4/1M tokens
- Llama 3.3 70B: $0.88/1M tokens
- 1x H100 80GB: $3.99/hour
- 1x H200 141GB: $5.49/hour
Best For
Developers and enterprises needing fast, cost-efficient deployment and fine-tuning of open-source LLMs with flexible GPU clusters and serverless APIs.
Integrations
See which third-party services are supported
Supported Integrations
Coming Soon
Integration comparison data for Lambda Labs, Together AI is being collected and will be available soon.
Strengths & Limitations
Key strengths and limitations of each service
Lambda Labs
ML researchers and startups running large-scale distributed training jobs who prioritize cost efficiency and hardware control over managed service breadth.
- Per-second billing with no egress fees undercuts hyperscale providers on total cost of ownership for GPU workloads
- Bare metal access and Quantum-2 InfiniBand networking enable efficient distributed training across hundreds of GPUs
- Lambda Stack pre-installation eliminates environment setup friction, reducing time-to-training from days to minutes
- Smaller scale and regional availability compared to AWS, Google Cloud, and Azure limits enterprise multi-region deployments
- Limited managed services ecosystem; users handle more infrastructure complexity than with hyperscale competitors
Together AI
Developers and enterprises needing fast, cost-efficient deployment and fine-tuning of open-source LLMs with flexible GPU clusters and serverless APIs.
- Serverless inference with OpenAI-compatible APIs and up to 4x faster performance via custom optimizations differentiates from generic cloud providers.
- Instant self-service GPU clusters up to 64 NVIDIA H100/H200 GPUs deploy in minutes with zero egress fees and autoscaling.
- Fine-tuning for 200+ open-source models like LLaMA and Mistral using proprietary data, with dedicated $2,872/month inference options.
- Full-stack observability via Grafana dashboards and pay-as-you-go token-based pricing for cost-efficient scaling.
- Young company founded in 2022 with 51-200 employees may lack the enterprise maturity and global scale of hyperscalers like AWS.
- Focus on open-source models limits access to proprietary LLMs from providers like OpenAI or Anthropic.
- High entry for dedicated options at $2,872/month suits enterprises but may deter small teams preferring fully serverless.
Company Info
Company details and background
Lambda Labs
Together AI
Comparison FAQ
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