CoreWeave vs Together AI (2026)
Updated March 9, 2026
Overview
CoreWeave is a specialized cloud provider built from the ground up for GPU-accelerated workloads. Offering NVIDIA H100 and A100 GPUs on demand, CoreWeave provides significantly lower pricing than hyperscalers for AI training and inference. The platform includes Kubernetes-native orchestration, fast networking, and flexible scaling, making it popular with AI labs and startups that need large GPU clusters without long-term commitments.
Together AI provides a cloud platform for running, fine-tuning, and training open-source AI models. The platform hosts popular models like Llama, Mistral, and Stable Diffusion with optimized inference that delivers fast generation at competitive prices. Together AI also offers GPU clusters for custom training jobs and has contributed to several breakthrough open-source AI research projects.
Key features
Core capabilities each platform advertises.
- Large-scale GPU clusters (H100, A100)
- InfiniBand networking for distributed training
- Kubernetes-native orchestration
- On-demand and reserved capacity
- Bare-metal performance
- Inference and training cloud
- Open-source model hosting
- Serverless inference endpoints
- Fine-tuning as a service
- Competitive GPU pricing
Strengths and tradeoffs
What each tool does well, and the limitations to keep in mind.
Pros
- Significantly lower GPU pricing compared to AWS, Azure, and GCP hyperscalers
- Purpose-built for AI workloads with optimized networking and Kubernetes orchestration
- Transparent pricing with no hidden fees for data egress or core services
- Up to 60% discounts available for committed usage, excellent for predictable workloads
Cons
- No free tier or trial period, requiring upfront payment commitment
- Smaller ecosystem compared to major cloud providers may limit integration options
- Less mature than hyperscalers in terms of managed services and tooling
- À la carte pricing model requires careful calculation of total costs including CPU and RAM
Pros
- 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
Cons
- 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
CoreWeave or Together AI — which should you choose?
Choose CoreWeave if you wantChoose if you want
- Large language model training
- Distributed training across GPU clusters
- High-performance inference at scale
- AI startup compute infrastructure
- Batch processing and fine-tuning
Choose Together AI if you wantChoose if you want
- Hosting open-source LLMs in production
- Fine-tuning models on custom data
- Cost-efficient inference at scale
- Training custom models
- Rapid model prototyping
Compare the models
How CoreWeave's and Together AI's models score on public benchmarks, and their live API status.
Compare CoreWeave and Together AI on your own traffic
Respan lets you trace LLM and agent calls across any model or framework, A/B test prompts on production traffic, and route requests across 1,000+ models through one gateway.
Try Respan free