Baseten vs TensorX: Token Pricing, Speed & Intelligence
Full comparison of Baseten and TensorX — live token pricing, latency, throughput, context window, strengths, weaknesses, and best use cases. Rates sourced from public APIs — verify before use.
Baseten
Production ML infrastructure — deploy any model with custom serving, autoscaling, and GPU selection
Baseten is a production ML infrastructure platform for deploying custom and open-weight models with full control over serving configuration, GPU selection, autoscaling, and cold-start behavior. Unlike managed inference APIs, Baseten lets you bring your own model weights, custom preprocessing, and serving logic. It supports Truss (Baseten's open-source model packaging format), vLLM, TensorRT-LLM, and custom Python serving code. Ideal for teams that need more control than a managed API but less overhead than raw Kubernetes.
TensorX
EU inference — Dublin, Helsinki, Paris — GLM-5.3, Kimi K3, zero data retention
TensorX is an EU inference provider with nodes in Dublin, Helsinki, and Paris. It offers GLM-5.3, Kimi K3, and GLM-5.3 Flash with zero data retention and prepaid credits. USD pricing. Suited for EU workloads that need geographic diversity within Europe and a zero-retention policy.
Key metric comparison
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Live token pricing
Strengths and weaknesses
Baseten
TensorX
Key differentiators
The production ML platform that gives you managed-API convenience with custom-serving flexibility — deploy any model with your own preprocessing logic, GPU selection, and autoscaling without managing Kubernetes.
EU inference with nodes in Dublin, Helsinki, and Paris, zero data retention, and prompt caching on GLM-5.3.
Frequently asked questions
Baseten FAQs
What is Baseten?
Baseten is a production ML infrastructure platform for deploying AI models with custom serving logic, GPU selection, and autoscaling. It supports any open-weight model or custom fine-tune via the Truss packaging format.
How does Baseten differ from Together AI or Replicate?
Baseten gives you full control over the serving stack — custom preprocessing, postprocessing, GPU selection, and autoscaling behavior. Together AI and Replicate are managed APIs with fixed serving; Baseten is for teams that need custom model serving logic.
What GPUs does Baseten support?
Baseten supports H100 SXM, H100 PCIe, A100 80GB, A10G, and T4 GPUs with per-second billing. You select the GPU type per deployment based on your model's memory and throughput requirements.
TensorX FAQs
What is TensorX?
TensorX is an EU inference provider with nodes in Dublin, Helsinki, and Paris. It offers GLM-5.3, Kimi K3, and GLM-5.3 Flash with zero data retention and prepaid credits. See tensorx.ai/pricing for current rates.
Does TensorX retain my data?
No. TensorX operates a zero data retention policy.
Provider resources
Key strengths compared
Baseten
- Full control over model serving: custom preprocessing, postprocessing, and business logic
- Bring your own weights — deploy any HuggingFace model or custom fine-tune
- GPU selection: H100, A100, A10G, T4 with per-second billing
- More setup required than managed APIs — not a drop-in OpenAI replacement
- Cold starts on scale-to-zero deployments (configurable warm pool available)
TensorX
- EU nodes in Dublin, Helsinki, and Paris — geographic diversity
- Zero data retention policy
- Prompt caching on GLM-5.3 (cached $0.44/1M vs $1.75/1M standard)
- Small model catalog
- Prepaid credits only — no post-pay billing
Key weaknesses compared
Baseten
- More setup required than managed APIs — not a drop-in OpenAI replacement
- Cold starts on scale-to-zero deployments (configurable warm pool available)
- Pricing is higher than shared inference APIs for low-volume workloads
TensorX
- Small model catalog
- Prepaid credits only — no post-pay billing
- Newer provider — limited third-party benchmark transparency
Provider category context
Baseten is a inference api, founded in 2019. TensorX is a inference api, founded in 2024. Both are inference api providers — the comparison is primarily about pricing, model selection, and feature differentiation within the same tier.
How to choose between them
Both Baseten and TensorX host open-weight models. The key differentiators are latency, throughput, and which specific model versions each provider offers. Check the speed metrics above — inference API providers often differ significantly on tokens-per-second for the same model. Pricing is typically competitive between them; availability of specific model versions (e.g., Llama 3.1 405B, DeepSeek V3) may be the deciding factor.