AI Inference Providers 2026: Honest Comparison Guide
Custom silicon against GPU platforms, and the question that settles it: what is your user doing while the tokens stream? Speed is worth paying for in exactly two of the four common workloads.
The inference layer is where the AI bill actually gets paid. You can spend a week choosing a gateway, an observability platform, and a durable orchestrator, and the per-token meter still runs against whoever is underneath all of it.
Numbers below were re-checked on 21 August 2026. Treat any per-token price in any post, including this one, as indicative rather than current.
The split that explains most arguments
Custom silicon. Groq’s LPU, Cerebras’ wafer-scale engine, SambaNova’s RDU. Chips designed top down for transformer inference, hitting numbers GPUs do not reach on the workloads they support. Groq runs a 70B-class open model around 750 tokens a second; Cerebras runs the same class near 2,100 and has published figures above 2,600 on smaller Llama 4 variants. Independent measurement puts both roughly an order of magnitude ahead of equivalent GPU inference.
GPU platforms. Together, Fireworks, Baseten, Modal, DeepInfra, Hyperbolic and the rest. Slower per token, and they host every model anyone publishes, they let you fine-tune, they expose batch APIs, and they do not tie you to one chip vendor’s roadmap.
Once that split is clear, most of the “which is better” arguments evaporate. Custom silicon is extraordinary when your workload fits the catalog. GPU platforms win the moment you need flexibility, and most production products eventually do.
Speed sells, and it is worth paying for twice out of four times
Before paying a premium for tokens per second, ask what the user is doing while they stream.
In a chat interface a human reads around five words a second, so anything past roughly fifty tokens a second is invisible. What is not invisible is the dead air before the first token. That is where custom silicon still feels like a different product: time to first token under 100ms on Groq, in the 80 to 150ms range on Cerebras, against 400 to 600ms as a typical GPU figure.
For voice the math tightens rather than flips. A natural-feeling turn has about 600ms end to end for everything, so the model’s share has to be small, and the two custom-silicon providers are the obvious candidates. Benchmark on your actual prompts before committing, because first-token and decode speed do not rank the same way.
For an agent loop with fifteen calls to plan, execute, reflect, and replan, nobody is watching any individual call. You want throughput per dollar. Batch tiers and on-demand GPU pricing win here.
For indexing, labeling, and offline classification, latency is irrelevant and the async discount is the entire game. Together’s batch tier at half price is the lowest sustained per-token cost you will find on hosted open weights outside reserved capacity.
Pricing: the mid tier converged, the big tier did not
Hosted 70B-class inference has converged hard. Most providers sit within a narrow band per million input tokens, with output at two to three times that, and the commodity end will undercut anyone if you do not need developer-experience features. At that point you are choosing on reliability and tooling, not price.
The 400B-plus tier is where real spread remains, and where the fastest option is meaningfully more expensive than the cheapest rather than marginally so.
Closed-weight frontier models live in a different universe entirely, and you buy those from the lab or your hyperscaler, not from an inference provider.
Two things move the bill more than the list price. Prompt caching structures differ enormously between providers, and if your workload has a stable system prompt above a couple of thousand tokens, which describes most agents and most retrieval apps, the cache discount can dwarf the base rate. And batch versus real-time is usually a bigger lever than switching vendors.
Free tiers now cover a real prototype
This changed around 2025 and plenty of teams have not noticed. Cerebras and Groq both give daily quotas generous enough to run a small internal tool indefinitely, and Google’s studio tier remains the cheapest way to test anything that needs vision.
One trap: the free API surface sometimes diverges from paid. Batch endpoints in particular are not always exposed on free tiers, and quotas are often per account rather than per key. Do not build a prototype on a free-tier-only behavior and then discover paid works differently.
Fine-tuning is where GPU platforms quietly win
Custom-silicon providers largely do not do it. Groq serves what Groq deploys. If you need to fine-tune and then serve cheaply, you are on a GPU platform, and both Together and Fireworks have this working end to end: upload a dataset, run a LoRA or a full fine-tune through the API, serve the result on the same OpenAI-compatible endpoint your app already calls. Fireworks is faster through the flow; Together has a wider catalog of base models to start from.
The honest order of operations is prompt first, retrieval second, fine-tune last. Most teams reach for fine-tuning when the actual problem is a bad retrieval pipeline. If the model is wrong about your domain, fix retrieval. If it has the facts and the wrong shape, fix the prompt or use structured output. Fine-tuning earns its place for tone and behavior you cannot reach any other way.
Catalog coverage
Together hosts the widest catalog and tends to have new open-weight releases within days. Fireworks covers fewer models with more polish per endpoint. The commodity providers compete hard on price for the popular ones. Groq and Cerebras run deliberately curated catalogs, which is the direct cost of purpose-built silicon: fewer models, and the ones they have run faster than anywhere else.
Check quantization as well as model name. “We host it” and “we host it at the precision you benchmarked” are different claims, and the second one is the one that matters.
How I would pick
Prototyping, or a chat product where first-token latency is the felt experience: Groq or Cerebras, on their free tiers until you outgrow them.
A voice agent: custom silicon, benchmarked on your own prompts, and budget for the fact that the model is only one part of a 600ms budget.
An agent loop or anything with a long tool chain: a GPU platform with good batch pricing, and spend the effort on caching rather than on shaving milliseconds nobody experiences.
Offline work: whichever async tier is cheapest this quarter, because there is nothing else to optimize.
Anything that needs a fine-tune: Together or Fireworks, and only after you have exhausted prompting and retrieval.
The meta-advice is the same as with gateways. Put something in front of this layer that lets you change providers in a config file, because the price and speed ranking in this category has reshuffled roughly every six months for three years, and nothing suggests it has finished.