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It's a split decision.
Choose Lambda Cloud when the workload needs dedicated NVIDIA GPU instances or managed, single-tenant clusters for training and inference. Choose Together AI when the codebase needs hosted model inference APIs, batch processing, fine-tuning or GPU clusters under one platform. Their pricing also differs: Lambda’s self-serve instances bill per GPU-hour, while Together lists token-based inference as well as GPU-hour rates. Check the specific model, hardware, capacity terms and current rates before estimating production cost.
01Side by side
| Lambda Cloud | Together AI | |
|---|---|---|
| Pricing model | GPU-hour instance and cluster rates | Token-based inference and GPU-hour rates |
| Free tier | No free plan listed | Free plan listed |
| API and SDKs | GPU instances and cluster infrastructure | Inference APIs; drop-in API compatibility |
| Integrations | No third-party integrations listed | Drop-in API compatibility; no named integrations |
| Deployment model | Self-serve GPUs and managed single-tenant clusters | Serverless, provisioned and dedicated inference |
| Platforms | Web | Web |
| Limits and scaling | Production clusters listed from 16 to 256+ GPUs | Batch inference up to 30 billion tokens per model |
| Security and service terms | SOC 2 Type II; hardware-level isolation | Provisioned throughput includes a 99% uptime SLA |
Lambda Cloud Choose Lambda Cloud if
- Choose Lambda Cloud if you need single-tenant GPU infrastructure.
- Choose Lambda Cloud if your team wants managed GPU clusters.
- Choose Lambda Cloud if GPU-hour pricing fits your workload.
Together AI Choose Together AI if
- Choose Together AI if your application calls hosted inference APIs.
- Choose Together AI if you need batch inference or fine-tuning.
- Choose Together AI if you want inference and GPU compute in one platform.
02Pricing at real usage
Lambda Cloud has no free plan listed. Its self-serve GPU instances bill per GPU-hour: HGX H100 is $4.59 and HGX B200 is $7.69. Listed production-cluster rates vary by size: a 16-GPU H100 cluster is $6.16/GPU-hour, while a 16-GPU B200 cluster is $9.86/GPU-hour. Lambda says self-serve prices take effect November 16, 2026; current rates are available after login until then.
Together AI lists a free plan, but the vendor does not publish its included usage. Serverless inference bills per million tokens; Kimi K3 is $3 per million input tokens and $15 per million output tokens. Dedicated inference lists HGX H100 at $5.49/GPU-hour and B200 at $8.99/GPU-hour. Provisioned throughput uses PTU/minute, with no rate shown. Estimate costs against your model mix, token volume and GPU hours.
03API, SDKs and integration
Lambda Cloud is GPU infrastructure: the homepage directs developers to launch a GPU instance and describes instances, 1-Click Clusters and managed clusters. Its listed platform is Web. The homepage does not name third-party integrations, SDKs, programming languages or a CLI, so plan to confirm the control-plane and provisioning interface against your automation requirements.
Together AI exposes model inference as APIs and says it offers drop-in API compatibility for production workloads. Its platform also includes batch inference, provisioned throughput, dedicated inference, fine-tuning and GPU clusters. The site links to technical documentation, cookbooks, demos and a model library, but does not name SDK languages or a CLI. Treat API compatibility as an integration starting point, not proof that every model, parameter or response format matches an existing provider. Check the target model and endpoint behavior before switching production traffic.
04Open source, self-hosting and lock-in
Neither vendor states an open-source license for its service or a self-hosting option. Neither publishes a data-export path on its homepage, so ask about exporting model artifacts, stored data and deployment configuration before committing.
The architectural distinction still matters. Lambda sells cloud GPU capacity, including single-tenant clusters and managed infrastructure; Together AI combines hosted inference APIs with GPU compute and managed model services. Lambda’s hardware-oriented approach leaves more of the workload running on GPU infrastructure your team provisions. Together’s API approach can reduce infrastructure management, but application code may depend on its endpoints, model availability and service-specific features. For either platform, document how you would move weights, datasets, logs and inference traffic, and verify export and migration terms with the vendor.
Questions people ask
Who are Together AI’s main competitors?
The answer depends on which part of Together AI you mean. Its offerings span hosted inference APIs, batch inference, fine-tuning and GPU compute, so alternatives should be compared against the specific service and workload. Lambda Cloud is a closer fit for GPU infrastructure than for Together’s full inference API offering.
Which cloud service has the best AI infrastructure?
There is no single best choice across these two architectures. Lambda Cloud lists GPU instances and managed, single-tenant clusters; Together AI offers model inference APIs, batch workloads, provisioned throughput and GPU compute. Compare required hardware, model coverage, capacity terms, uptime needs and total workload cost.
Is Lambda AI profitable?
Lambda Cloud’s product pages do not establish whether Lambda is profitable. Profitability requires financial information, not GPU specifications or infrastructure pricing. Developers assessing the vendor should ask about its financial disclosures, service commitments and support arrangements rather than infer company health from the listed products.
Is Together AI a good company for production workloads?
Together AI may fit teams that want hosted model inference, batch processing or managed GPU compute. Its provisioned-throughput offering lists a 99% uptime SLA, but that does not describe every service. Check the relevant service’s capacity terms, model pricing, API behavior and support commitments against your production requirements.