Throughput (tokens / sec)
avg. 150tokens/s
Your requests run on GPUs we own, in our own EU data center, run by a European company.
The throughput you measure on day one is the throughput you plan production around.
Automatic failover, spending limits, and EU-only data handling keep high-volume inference controlled and safe to scale.
GPU routing tuned for throughput and latency — the same speed and reliability, at consistently lower cost.
Faster token generation and sub-second responses, with identical output.
Infrastructure built for sustained inference load, not occasional demo traffic.
Reused context bills at a lower cache-read rate, so repeated workloads cost less.
Compare Entrim’s pricing, powered by an optimized inference runtime, against other providers using the same token counts per request.
Estimate your Savings
Select a model
Token usage(Usage split, 10:1: 9B input - 909M output)
| Provider | Input / 1M | Output / 1M | Monthly est. |
|---|---|---|---|
| Deepinfra | $0.32 | $3.20 | $5,818.18 |
| Chutes | $0.30 | $2.00 | $4,545.45 |
| io.net | $0.29 | $1.99 | $4,445.45 |
| Entrim | $0.10 | $0.40 | $1,272.73 Save up to 80% |
High-throughput agentic work with 1M context
Tool-heavy coding at 3B-active cost
Dense agentic coding with thinking control
avg. 150tokens/s
avg. 820ms
99.9%
Measured across the last 30 days.
99.8%
Successful API responses across the last 30 days.
200B+
Built to handle sustained high-volume inference workloads.
Code examples
curl -X POST "https://api.entrim.ai/v1/chat/completions" \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $ENTRIM_API_KEY" \ -d '{ "model": "your-model-name", "messages": [ { "role": "user", "content": "Why do they call it a building if it is already built?" } ] }'SDKs & docs
Use familiar SDK patterns with quickstart examples for common production setups.
OpenAI-compatible
Keep the request format your team already knows.
No cold starts
Production requests are served without model spin-up delays.
Prompts and outputs are processed for inference, not stored after completion, and never used to train models.
Prompts, outputs, and request metadata stay on EU soil — no transfer to third countries, no sub-processors outside the EU. DPA available.
Requests are encrypted, access is isolated by API key, and traffic is handled with tenant-level separation.
Controls are being built around recognised security frameworks, with limited operational metadata used for billing, monitoring, latency, and abuse prevention.
For companies where LLM inference is part of the product COGS — AI assistants, vertical SaaS, copilots, companion apps, and generative workflows running at user scale.
For product teams shipping AI into existing apps — summaries, search, recommendations, automation, and user-facing AI actions that need predictable cost per feature.
For conversational AI, support automation, and agent workflows where latency, retry loops, and per-interaction economics directly affect margins.
For RAG, document extraction, contract review, claims processing, internal knowledge assistants, and other high-volume text-heavy pipelines.
Use $25 free credits to run the prompts, token sizes, model options, and output targets that match your use case.
Measure output quality, latency, throughput, and request cost against your baseline, target, or production requirement.
Move the workload into production and scale usage with predictable reliability, stable performance, and cost-efficient inference as volume grows.