Cerebras vs Together AI: Inference & Compute Comparison
Compare Cerebras and Together AI side by side. Both are tools in the Inference & Compute category.
Updated March 10, 2026
The short answer
Choose Cerebras if revolutionary wafer-scale architecture with 10-70× speedup.
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 | Enterprises and developers who need the fastest possible LLM inference | Developers and companies deploying open-source AI models in production |
| Website | cerebras.net | together.ai |
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Pros & Cons: Cerebras vs Together AI
- +Revolutionary wafer-scale architecture with 10-70× speedup
- +Massive memory bandwidth (21PB/s) eliminates bottlenecks
- +Flexible deployment from cloud to on-premise hardware
- +Proven performance in production applications
- −Very high capital cost (USD 2-3M) for hardware systems
- −Manufacturing yield challenges with wafer-scale design
- −Requires specialized expertise to optimize workloads
- +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: Cerebras vs Together AI
- · Limited access
- · Entry-level models
- · Community support
- · API access
- · Multiple model options
- · Competitive rates
- · Scalable usage
- · Production access
- · Dedicated CS-2 access
- · Full training capabilities
- · USD 180k per month option
- · USD 1.65M annual pricing
- · 4 trillion transistors
- · 900,000 AI cores
- · 44GB on-chip SRAM
- · 21PB/s memory bandwidth
- · 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 Cerebras vs Together AI
- Ultra-fast LLM inference
- Real-time AI applications
- High-throughput text generation
- Hosting open-source LLMs in production
- Fine-tuning models on custom data
- Cost-efficient inference at scale
About Cerebras
Cerebras Systems is a pioneering AI hardware company founded in 2015 by Andrew Feldman, Gary Lauterbach, Michael James, Sean Lie, and Jean-Philippe Fricker, who previously worked together at SeaMicro (sold to AMD for USD 334 million in 2012). The company revolutionized AI computing with its Wafer-Scale Engine (WSE), the world's largest chip that uses an entire wafer instead of cutting it into individual chips. The CS-3 system contains 4 trillion transistors across 900,000 AI cores with 44GB of on-chip SRAM, delivering 21 petabytes per second of memory bandwidth—7,000× more than NVIDIA's H100.
Cerebras offers both hardware systems and cloud inference services. The CS-3 hardware system is priced at approximately USD 2-3 million per unit, targeting large enterprises, research institutions, and well-funded AI labs. For more accessible options, Cerebras provides cloud-based inference with competitive rates: a Developer Tier at USD 0.10-0.60 per million tokens depending on model choice, making cutting-edge AI accessible without massive capital investments. Cloud training on CS-2 systems is available at USD 60,000 per week or USD 1.65 million per year.
Cerebras' wafer-scale architecture delivers 10-70× faster inference speeds than GPU-based solutions and achieved 210× speedup over NVIDIA H100 in carbon capture simulations. The on-wafer interconnect bypasses latency bottlenecks of multi-GPU setups, enabling simpler programming models and handling huge models without typical GPU memory constraints. While manufacturing yields and high costs present challenges, Cerebras' breakthrough technology addresses fundamental bottlenecks in AI computing, positioning it as a serious challenger to NVIDIA's dominance in the AI accelerator market.
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.
Browse all Inference & Computetools →Other Inference & Compute Tools
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