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.
EU inference with nodes in Dublin, Helsinki, and Paris, zero data retention, and prompt caching on GLM-5.3.
- 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)
- USD pricing — no currency conversion for international customers
- Small model catalog
- Prepaid credits only — no post-pay billing
- Newer provider — limited third-party benchmark transparency
TensorX Models
| Model | Modality | Tier | Price | Cached /1M | TTFT | Throughput | Context | Intel. | Caps | |
|---|---|---|---|---|---|---|---|---|---|---|
GLM-5.3 Flash GLM-5.3 | Chat | efficient | $0.20/ $0.50 in / out per 1M EST Oct 2026 | — | — | — | 131K | — | ||
GLM-5.3 GLM-5.3 | Chat | advanced | $1.75/ $4.50 in / out per 1M EST Oct 2026 | $0.44 | — | — | 131K | — | ||
Kimi K3 Kimi | Chat | advanced | $3.00/ $15.00 in / out per 1M EST Oct 2026 | — | — | — | 131K | — |
TensorX — Frequently Asked Questions (FAQs)
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TensorX is an inference API provider that hosts open-weight and third-party large language models. 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. Common use cases include EU inference, Privacy-sensitive workloads, Geographic diversity. Headquartered in EU, founded 2024. All models are accessible via a REST API compatible with standard OpenAI-style request formats, enabling drop-in integration with most LLM frameworks and orchestration tools.
TensorX vs other LLM providers
TensorX competes with OpenAI, Anthropic, Google, Groq, Together AI, Mistral, Cohere, and other inference API providers across dimensions of price, throughput, latency, context window, and model intelligence. EU inference with nodes in Dublin, Helsinki, and Paris, zero data retention, and prompt caching on GLM-5.3. Use the LLM provider comparison tool to see TensorX token pricing, latency, and throughput side-by-side with any other provider. The full LLM pricing table shows all providers ranked by input token cost, output token cost, and throughput in a single sortable view.
Why choose TensorX?
TensorX stands out because: EU inference with nodes in Dublin, Helsinki, and Paris, zero data retention, and prompt caching on GLM-5.3.. The API currently offers 3 models, spanning 2 advanced, 1 efficient tiers. For teams running high-volume inference workloads, prompt caching and batch API endpoints can reduce effective input token costs by 50–90% — check the context window cost guide for a full breakdown of caching economics.
Understanding TensorX token pricing
TensorX charges separately for input (prompt) and output (completion) tokens, priced per 1M tokens in USD. Output tokens are typically 3–5× more expensive than input tokens due to the compute cost of autoregressive generation. The cheapest {meta.name} input rate is currently $0.20/1M tokens. Prices shown are sourced from TensorX's public pricing page and updated every 15 minutes. Need help estimating your spend? Read the LLM API cost calculator guide — it covers tokens, context windows, prompt caching, and batch discounts with worked examples for RAG, chat history, and document processing workloads.
TensorX context window and model capabilities
Context window size directly affects both capability and cost — every token in the context window is charged as an input token. TensorX's largest context window is 131K tokens. For RAG and document processing workloads, longer context windows enable richer retrieval but increase per-call costs proportionally. Prompt caching — where supported — stores the KV state of repeated prefixes and charges 75–90% less for cache hits, making it the most impactful cost optimization for applications with consistent system prompts or retrieved documents. See the LLM context window cost guide for a full analysis of how context length affects your API bill.
Self-hosted vs managed inference: when TensorX makes sense
Managed inference APIs like TensorX eliminate infrastructure overhead — no GPU provisioning, driver management, or model serving stack to maintain. The trade-off is cost at scale: a single H100 at ~$2.50/hr can serve ~500K tokens/min of Llama 3.3 70B, which at TensorX API rates would cost significantly more per token. The break-even point depends on your request volume, latency requirements, and engineering capacity. For teams processing fewer than ~10M tokens/day, managed APIs are almost always cheaper when total cost of ownership is considered. Above that threshold, self-hosted inference on rented GPU compute typically wins on unit economics. Read the cheapest GPU cloud guide for a full break-even analysis. Historical TensorX token price data is available in the LLM price history charts.
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