Infercom
EU inference on SambaNova chips — Gemma 4, gpt-oss-120b, Whisper, embeddings
Infercom is an EU-based inference provider running on SambaNova RDU chips, offering Gemma 4 31B, gpt-oss-120b, Whisper Large v3, and E5-Mistral embeddings. Prices are in EUR per 1M tokens, excl. VAT, on a PAYG basis. Suited for European workloads requiring EU infrastructure without the overhead of a sovereign gateway.
EU inference on SambaNova chips covering chat, speech-to-text, and embeddings at competitive EUR rates.
- EU-based infrastructure on SambaNova RDU chips
- Competitive EUR pricing on Gemma 4 31B and gpt-oss-120b
- Covers multiple modalities: chat, speech-to-text, embeddings
- PAYG — no minimum spend
- EUR pricing excluded from USD cross-provider comparisons
- Small model catalog
- Newer provider — limited ecosystem integrations
Infercom Models
| Model | Modality | Tier | Price | Cached /1M | TTFT | Throughput | Context | Intel. | Caps | |
|---|---|---|---|---|---|---|---|---|---|---|
Whisper Large v3 Whisper | Speech | efficient | €0.10/ €0.00 in / out per 1M · Priced in EUR EST Oct 2026 | — | — | — | — | — | ||
E5-Mistral embeddings E5-Mistral | Embedding | efficient | €0.13/ €0.00 in / out per 1M · Priced in EUR EST Oct 2026 | — | — | — | 33K | — | ||
Gemma 4 31B Gemma 4 | Chat | efficient | €0.20/ €0.35 in / out per 1M · Priced in EUR EST Oct 2026 | — | — | — | 131K | — | ||
gpt-oss-120b GPT-OSS | Chat | efficient | €0.22/ €0.59 in / out per 1M · Priced in EUR EST Oct 2026 | — | — | — | 131K | — |
Infercom — Frequently Asked Questions (FAQs)
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Infercom is an inference API provider that hosts open-weight and third-party large language models. Infercom is an EU-based inference provider running on SambaNova RDU chips, offering Gemma 4 31B, gpt-oss-120b, Whisper Large v3, and E5-Mistral embeddings. Prices are in EUR per 1M tokens, excl. VAT, on a PAYG basis. Suited for European workloads requiring EU infrastructure without the overhead of a sovereign gateway. Common use cases include EU inference, Embeddings, Transcription, Cost-sensitive workloads. 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.
Infercom vs other LLM providers
Infercom 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 on SambaNova chips covering chat, speech-to-text, and embeddings at competitive EUR rates. Use the LLM provider comparison tool to see Infercom 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 Infercom?
Infercom stands out because: EU inference on SambaNova chips covering chat, speech-to-text, and embeddings at competitive EUR rates.. The API currently offers 4 models, spanning 4 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 Infercom token pricing
Infercom 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. Prices shown are sourced from Infercom'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.
Infercom 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. Infercom'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 Infercom makes sense
Managed inference APIs like Infercom 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 Infercom 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 Infercom token price data is available in the LLM price history charts.
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