What is an LLM inference provider?

An LLM inference provider hosts open-weight models like Llama, DeepSeek, and Qwen behind a pay-per-token API, handling the GPUs, scaling, and serving infrastructure. You send prompts and pay for tokens, instead of renting hardware yourself or being limited to one model maker's proprietary lineup.

What are cheaper alternatives to the OpenAI API?

Open models served by inference providers are the main path: DeepInfra, Novita, and OpenRouter serve Llama-class models from $0.10 per million input tokens, a fraction of frontier API rates. Whether an open model matches your quality bar is workload-specific; benchmark before switching. Frontier rates are in our model pricing comparison.

Who has the fastest LLM inference?

Custom silicon leads: Cerebras reports roughly 3,000 tokens per second on gpt-oss-120B (about 1,700 sustained per Artificial Analysis), Groq delivers consistently low first-token latency around 0.7 seconds, and SambaNova publishes 435+ tokens per second on large models. GPU-based hosts run 2 to 3x slower on comparable models.

Are cheap inference providers lower quality?

Sometimes, and the mechanism is quantization: budget tiers often serve FP8 or FP4 compressed variants under the same model name, which can raise error and retry rates on hard tasks. That's a legitimate tradeoff when disclosed. Check the precision tier, then benchmark cost per completed task, not per token.

Which inference providers have free tiers?

Most major providers offer free tiers or credits, with hard daily caps: OpenRouter runs a free tier across many models with daily limits, and Groq, Together, and Cloudflare Workers AI offer free allowances for evaluation. Free tiers are for prototyping; rate limits make them unusable as production capacity.

Do hyperscalers or specialist providers cost less for inference?

Specialists almost always post lower per-token rates: the same Llama 3.3 70B runs $0.10 to $0.32 per million tokens on DeepInfra versus roughly $0.72 blended on Amazon Bedrock. Hyperscalers charge for procurement convenience, governance, and ecosystem integration rather than competing on sticker price.

How do I track AI spend across multiple inference providers?

Route traffic through a gateway so per-request telemetry is captured once, then land provider bills and gateway usage in a cost platform that allocates spend to products, teams, and customers. Provider dashboards only see themselves; cross-provider attribution has to happen a layer above them.

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