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It's a split decision.
Choose Together AI if you want hosted open-model APIs, batch inference, or fine-tuning without managing serving infrastructure. Choose Lambda Cloud if the job is GPU compute: self-serve instances, production clusters, or managed superclusters. Their prices are not directly comparable: Lambda lists per-GPU-hour rates, while Together combines token-based inference pricing with hourly dedicated GPUs. Neither lists a free plan or published seat pricing, so estimate your workload and team setup before switching.
01Side by side
| Lambda Cloud | Together AI | |
|---|---|---|
| Starting price | Listed GPU rates start at $0.69/hour; Nov. 16 rates apply. | Inference is usage-based; dedicated H100 is $5.49/GPU-hour. |
| Free plan | No free plan listed. | No free plan listed. |
| Best for | Teams training models on GPU clusters or instances. | Teams building with hosted open-model APIs. |
| Core strength | GPU instances, production clusters, and managed superclusters. | Open-model APIs, inference, fine-tuning, and GPU compute. |
| Learning curve | Cluster-focused setup; managed options are available. | Serverless APIs require no inference infrastructure management. |
| Integrations/data | Not specified on the vendor's site. | Not specified on the vendor's site. |
| Team/seat costs | No seat-based pricing published. | No seat-based pricing published. |
| Support | Managed clusters and co-engineering support listed. | Support options not specified on the vendor's site. |
Lambda Cloud Choose Lambda Cloud if
- Choose Lambda Cloud if GPU clusters are your main requirement.
- Choose Lambda Cloud if you need self-serve GPU instances.
- Choose Lambda Cloud if managed superclusters or co-engineering matter.
Together AI Choose Together AI if
- Choose Together AI if you want hosted open-model inference APIs.
- Choose Together AI if you need batch inference or fine-tuning.
- Choose Together AI if serverless inference suits your workload.
02Pricing compared
Lambda Cloud lists GPU-hour pricing, not monthly or annual plans. Self-serve rates include $4.59 per GPU-hour for an HGX H100 and $7.69 for an HGX B200. Production cluster rates vary by GPU and size: H100 clusters range from $5.54 to $6.16 per GPU-hour, while B200 clusters range from $8.87 to $9.86. The pricing page says those listed prices take effect November 16, 2026; current prices are available after login until then.
Together AI mixes usage-based model API charges with hourly GPU pricing. Its listed example for Llama 3 8B Instruct Lite is $0.14 per 1 million input and output tokens; dedicated HGX H100 GPUs are $5.49 per hour and B200 GPUs $8.99. Neither product lists a free plan. These rates are not like-for-like: estimate GPU hours, token volume and capacity needs before comparing totals.
03Features and depth
Lambda Cloud is the more infrastructure-focused option. It offers self-serve GPU instances, 1-Click production clusters using H100 and B200 GPUs, managed clusters, and superclusters. The product information describes its superclusters as single-tenant NVIDIA GB300 NVL72 systems with Quantum-2 InfiniBand. Lambda also lists SOC 2 Type II certification and hardware-level isolation. That depth is relevant when the workload needs GPU capacity and cluster control, but it means choosing infrastructure rather than simply calling a hosted model API.
Together AI covers more of the model-serving workflow. Its serverless APIs host open models, batch inference handles asynchronous work, and fine-tuning uses managed training infrastructure. Teams can also reserve token-based capacity through Provisioned Throughput, which lists a 99% uptime SLA, or deploy on dedicated GPUs. The trade-off is a more varied bill: token usage, reserved throughput or GPU hours, depending on the service. Together is a better fit when you want model access and deployment options in one platform; Lambda is aimed at teams buying compute and clusters.
04Ease of use and learning curve
Together AI should require less infrastructure setup for teams using serverless inference: its APIs run open models on demand without managing serving infrastructure. Batch inference and managed fine-tuning also keep some operations inside the platform. But you still need to estimate token usage or reserved capacity, and dedicated deployments bring GPU-hour costs into the decision.
Lambda Cloud offers self-serve instances, but selecting GPU types, sizing clusters and planning GPU hours remain part of the job. Its 1-Click Clusters and managed cluster options provide more supported paths, though the published information does not detail setup steps or required expertise. In short, Together is the simpler starting point for hosted model access; Lambda makes more sense when your team is prepared to make infrastructure choices.
Questions people ask
Who are Together AI's main competitors?
The product information here does not name Together AI's competitors, so we can't provide a sourced brand-by-brand list. Lambda Cloud overlaps on GPU compute and dedicated inference. Together also offers hosted model APIs, batch inference and fine-tuning, so its alternatives depend on which part of that platform you need.
Which cloud service has the best AI infrastructure?
There is no single winner for every workload. Lambda Cloud focuses on GPU instances, production clusters and managed superclusters. Together AI combines GPU compute with open-model APIs, batch inference and fine-tuning. Compare the required workload, operating model and total cost; the listed GPU-hour prices alone do not settle the choice.
Is Lambda AI profitable?
The product and pricing information provided here does not include Lambda's revenue, costs or profitability. GPU-hour prices show what some compute options cost, not whether the company makes a profit. We can't answer its financial status from these details; that would require reliable financial disclosures or reporting.
Is Together AI a good company?
That depends on what you need. The published review gives Together AI a 7/10 overall, with feature and value scores of 9 and 7, and support at 5. Its offering spans open-model inference, GPU compute and fine-tuning, but usage-based pricing makes workload estimates important. The score is not a substitute for checking fit.