FastInfra
Inference platform + GPU pods — 785+ models, Amazon Nova, H200 capacity
FastInfra is a hybrid inference platform combining a model router (785+ models) with its own H200 GPU capacity. It offers Amazon Nova Lite and Nova Micro at very competitive rates, with prepaid credits from $5. Part inference router, part dedicated GPU cloud.
Hybrid inference platform with 785+ models via router and own H200 capacity, offering Amazon Nova at very competitive rates.
- 785+ models via router — broad catalog coverage
- Own H200 GPU capacity for low-latency inference
- Very competitive Amazon Nova Lite ($0.06/$0.25) and Nova Micro ($0.04/$0.15) rates
- Prepaid from $5 — low barrier to entry
- Newer provider — limited ecosystem integrations and third-party reviews
- Hybrid router/own-capacity model means latency may vary by model
- Limited benchmark transparency
FastInfra Models
| Model | Modality | Tier | Price | Cached /1M | TTFT | Throughput | Context | Intel. | Caps | |
|---|---|---|---|---|---|---|---|---|---|---|
Amazon Nova Micro Amazon Nova | Chat | budget | $0.04/ $0.15 in / out per 1M EST Oct 2026 | — | — | — | 128K | — | ||
Amazon Nova Lite Amazon Nova | Chat | efficient | $0.06/ $0.25 in / out per 1M EST Oct 2026 | — | — | — | 300K | — |
FastInfra — Frequently Asked Questions (FAQs)
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Full comparison tableFastInfra LLM pricing overview
FastInfra is an inference API provider that hosts open-weight and third-party large language models. FastInfra is a hybrid inference platform combining a model router (785+ models) with its own H200 GPU capacity. It offers Amazon Nova Lite and Nova Micro at very competitive rates, with prepaid credits from $5. Part inference router, part dedicated GPU cloud. Common use cases include Cost-sensitive workloads, Broad model access, Amazon Nova inference. Headquartered in US, 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.
FastInfra vs other LLM providers
FastInfra 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. Hybrid inference platform with 785+ models via router and own H200 capacity, offering Amazon Nova at very competitive rates. Use the LLM provider comparison tool to see FastInfra 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 FastInfra?
FastInfra stands out because: Hybrid inference platform with 785+ models via router and own H200 capacity, offering Amazon Nova at very competitive rates.. The API currently offers 2 models, spanning 1 efficient, 1 budget 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 FastInfra token pricing
FastInfra 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 $0.04/1M tokens. Prices shown are sourced from FastInfra'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.
FastInfra 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. FastInfra's largest context window is 300K 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 FastInfra makes sense
Managed inference APIs like FastInfra 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 FastInfra 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 FastInfra token price data is available in the LLM price history charts.
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