This review is built from published pricing, provider documentation and third-party benchmarks rather than sustained hands-on use across all six platforms. Inference pricing moves frequently, so verify current rates before routing production traffic.
Cerebras charges more per token than Groq and is still arguably cheaper. That sounds wrong until you look at what you are actually buying.
On Llama 3.3 70B, Groq lists roughly $0.59 input and $0.79 output per million tokens and delivers around 750 tokens per second. Cerebras lists roughly $0.85 and $1.20 and delivers around 2,100 tokens per second. Per token, Cerebras is about 50% more expensive. Per second of output, the two are close to level.
That is the insight most AI inference API comparisons skip, and it decides which provider you should pick.
In this guide:
- What an AI Inference API Actually Is
- The Speed Tier: Groq and Cerebras
- The Cost-Per-Second Argument
- The Volume Tier: Together, Fireworks, DeepInfra
- What None of Them Do
- The Groq Fine-Tuning Trap
- Inference API or Rent a GPU?
- Who Should Use What
- Frequently Asked Questions About AI Inference APIs
- Verdict
- Sources and Further Reading
What an AI Inference API Actually Is
A serverless AI inference API hosts open-weight models and exposes them over an endpoint, usually OpenAI-compatible, so you pay per token rather than per hour. No machine to provision, no serving stack to maintain.
That sits between two things this site has covered before. At one end, renting a GPU gives you raw hardware and total control. At the other, frontier APIs from OpenAI and Anthropic give you the strongest models on someone else’s infrastructure. An AI inference API is the middle: someone else’s infrastructure, open-weight models, per-token billing.
By 2026 the market has consolidated around roughly seven serverless providers, with a second group offering bring-your-own-container hosting. The numbers that matter are stark: pricing on the same model spreads around 6x across providers, P50 latency spreads 5 to 7x, and throughput on specialty hardware runs up to 10x over commodity H100 endpoints.
A 6x AI inference API price spread on identical output means the default you picked casually is probably wrong.
The Speed Tier: Groq and Cerebras
Two AI inference API providers skip NVIDIA hardware entirely, and both are faster than anything running on GPUs.
Groq built the LPU, a deterministic single-purpose chip optimised for the decode phase of generation, the token-by-token step GPUs handle less efficiently than prompt processing. It delivers 500 or more tokens per second on supported models with time-to-first-token around 0.7 seconds. NVIDIA thought enough of the approach to structure a non-exclusive licensing deal for the underlying technology in December 2025, reportedly worth around $20 billion.
Cerebras uses the WSE-3, a wafer-scale engine with roughly 900,000 cores and 44GB of on-chip SRAM. That architecture is what produces its throughput figures: around 2,100 tokens per second on Llama 3.3 70B, and it serves Llama 3.1 405B faster than any GPU provider.
On gpt-oss-120B, Cerebras reports roughly 3,000 tokens per second while Artificial Analysis measured around 1,700 sustained in July 2026. The gap between vendor claim and independent measurement is worth noting, and 1,700 is still far ahead of the field.
| Groq | Cerebras | |
|---|---|---|
| Hardware | LPU | WSE-3 wafer-scale |
| Llama 3.3 70B throughput | ~750 tok/s | ~2,100 tok/s |
| Llama 3.3 70B price (in/out per 1M) | $0.59 / $0.79 | $0.85 / $1.20 |
| Catalog size | 6 non-enterprise models | Small, gpt-oss-120B plus previews |
| Free tier | Yes, no card required | Check current terms |
The Cost-Per-Second Argument
Here is the reasoning that changes the decision.
Per-token AI inference API pricing answers “what does this text cost.” For a latency-bound product, the question is different: “what does a response cost, and how long does the user wait.” Those diverge when throughput differs by 3x.
Cerebras is roughly 44% more expensive per output token on Llama 3.3 70B and roughly 2.8x faster. Cost per second of generated output ends up comparable, while the user waits a third as long. If your product is a voice agent, a live coding assistant or a streaming chat interface, that is not a marginal difference.
The inverse holds too. For a batch job processing documents overnight, throughput is nearly irrelevant and per-token price is everything. Paying Groq or Cerebras rates for work nobody is waiting on is straightforwardly wasteful.
The rule: latency-bound work buys throughput, batch work buys price. Most teams run both kinds and should use different providers for each, which is the routing argument from our LLM API cost optimization guide applied to open-weight models.
The Volume Tier: Together, Fireworks, DeepInfra
GPU-based AI inference API providers run 2 to 4x cheaper per token than the speed tier and correspondingly slower, typically 100 to 150 tokens per second on 70B-class models.
Together AI is the price leader at scale. Batch and reserved tiers are reported as the cheapest in the market for steady-state production. It also supports training and serving through the same API, which matters when iterating on a fine-tune.
Fireworks AI is the developer-experience leader: cleanest API, fastest integration of new models, best fine-tuning workflow. It costs slightly more than the floor and is usually worth it if your team’s time is the binding constraint.
DeepInfra hosts the widest catalog of current open-source models, including the Kimi K2 family, Qwen3.5, GLM-5, DeepSeek V4, MiniMax-M2, gpt-oss-120B and Nemotron. It also prices aggressively, with gpt-oss-120B at $0.039 input and $0.19 output per million as verified in June 2026, roughly $0.05 blended.
If you want to run GLM-5.3-Flash or another recent open-weight release without self-hosting, catalog breadth is the deciding factor and DeepInfra leads on it.
OpenRouter deserves a mention as a different shape: one API that routes to underlying providers, adding a hop and a small markup in exchange for avoiding lock-in.
What None of Them Do
The constraint that rules an AI inference API out for some teams, stated plainly.
Every AI inference API here hosts open weights only. No GPT-6 Astra, no Claude, no Gemini. If your application depends on a specific frontier model, an AI inference API is not a substitute and no amount of price or speed advantage changes that. Our AI models compared roundup covers where the closed frontier sits against open alternatives.
AI inference API catalogs vary enormously. Groq lists six non-enterprise models. Cerebras keeps a small production catalog. DeepInfra hosts dozens. Check the specific model you need is served before committing, and check it on the provider’s own page rather than a comparison article.
AI inference API rate cards move. Every figure in this review is a snapshot. Prices shift with model choice, cache usage and traffic volume.
The Groq Fine-Tuning Trap
Worth its own section because it is the AI inference API trap that catches people out most expensively.
Groq added LoRA fine-tune hosting in 2026. It ships gated entirely behind Enterprise-tier approval and a request process, not a self-serve API endpoint a solo developer or small team can hit.
The failure mode is specific: you fine-tune a model expecting Groq-speed inference on it, then discover a standard account cannot host it. The fix is hosting that fine-tune on Fireworks, Together or Baseten and reserving Groq for the parts of your stack running unmodified supported models.
If custom fine-tunes are central to your product, start with a provider that serves them self-serve. Together and Fireworks both support training and serving through one API, which is the cleaner path for anyone iterating on their own weights. Our fine-tuning vs RAG guide covers whether you need a fine-tune at all before you pick where to host one.
Inference API or Rent a GPU?
The AI inference API versus hardware comparison matters for anyone who has read this site’s reviews.
Published analysis comparing Groq’s per-token rate against self-hosted vLLM on an H100 found that Groq’s pricing on its own supported models already beats a decently optimised self-hosted deployment in many scenarios. That is a genuinely surprising result, and it reframes the decision.
The real argument for self-hosting is not cost per token. It is model choice, fine-tuning access, control over the serving stack, and data residency. Those are strong reasons. Raw economics, at typical volumes, increasingly is not.
| AI inference API | Rented GPU | Owned hardware | |
|---|---|---|---|
| Billing | Per token | Per hour | Capital |
| Model choice | Provider’s catalog | Anything | Anything |
| Fine-tunes | Varies, often gated | Full control | Full control |
| Data residency | Provider’s terms | Provider’s region | Yours |
| Idle cost | Zero | Full rate | Depreciation |
That last row is the one people underestimate. A rented GPU bills while idle; an inference API does not. For bursty workloads that asymmetry usually decides it, which is the same logic as our Lambda vs Hetzner comparison between hourly and monthly billing.
Who Should Use What
| If you are | Start with | Why |
|---|---|---|
| Building voice agents or live chat | Groq | Sub-second TTFT and 500+ tok/s; latency is the product |
| Pushing very high token volume fast | Cerebras | ~2,100 tok/s on 70B, best on 405B-class |
| Running steady-state production at scale | Together AI | Batch and reserved tiers are the price floor |
| Prioritising developer time | Fireworks AI | Cleanest API, fastest new-model support |
| Needing a specific recent open model | DeepInfra | Widest catalog, aggressive pricing |
| Avoiding lock-in across providers | OpenRouter | One API, small markup, easy switching |
| Dependent on GPT or Claude specifically | None of these | Open weights only, no substitute available |
| Running your own fine-tune | Together, Fireworks or Baseten | Groq gates fine-tune hosting behind Enterprise |
Frequently Asked Questions About AI Inference APIs
Which AI inference API is cheapest?
DeepInfra and Together AI are consistently near the floor, with DeepInfra at roughly $0.05 blended on gpt-oss-120B as of June 2026. But the same model spreads around 6x across providers, so the answer depends on which model you run and whether you can use batch or reserved pricing.
Is Groq actually faster than a GPU?
Yes, by a wide margin on supported models. The LPU is purpose-built for the decode phase of generation, producing 500 or more tokens per second where a typical H100 endpoint runs 100 to 150.
Why is Cerebras more expensive but still competitive?
Because it ships roughly 2.8x more tokens per second on Llama 3.3 70B. Per token it costs more; per second of output the two land close together. Which matters depends on whether a user is waiting.
Can I run Claude or GPT through these?
No. Every provider here hosts open-weight models only. For frontier closed models you go to the original vendor or a router that includes them.
Should I use one of these instead of renting a GPU?
For most bursty workloads, yes. Published comparisons find per-token API pricing already beats optimised self-hosting in many scenarios, and an API costs nothing when idle. Self-host for model choice, fine-tuning control or data residency, not to save money.
Do these support fine-tuned models?
Together, Fireworks and Baseten do, through self-serve APIs. Groq added LoRA hosting in 2026 but gates it behind Enterprise approval, which is a meaningful limitation for smaller teams.
Testing Before You Commit
A short AI inference API evaluation that avoids the usual mistakes.
Benchmark on your own prompts, not theirs. Published throughput figures use standard workloads. Your prompt lengths and output lengths will produce different numbers, sometimes substantially.
Measure time to first token separately from throughput. They are different properties and different products care about different ones. A streaming interface lives or dies on TTFT; a batch job does not care.
Price a realistic month, not a thousand tokens. Include cached input rates and batch discounts, which vary more between providers than headline rates do.
Check the specific model on the provider’s page. Catalogs change and comparison articles date quickly, this one included.
Try the free tiers. Groq’s requires no credit card, which makes a same-day comparison across two or three providers genuinely practical.
Verdict
The serverless AI inference API market split cleanly in 2026. Groq and Cerebras sell speed on custom silicon and are the correct default for anything where a person is waiting on output. Together, Fireworks and DeepInfra sell tokens cheaply on GPUs and are the correct default for volume work nobody is watching. Picking between those two groups on per-token price alone is the most common mistake, because it ignores that Cerebras costing 44% more per token while running 2.8x faster is close to a wash on cost per second.
The honest caveat is that all of these serve open weights only, and several have narrow catalogs. Groq lists six non-enterprise models and gates fine-tune hosting behind Enterprise approval, which is a real trap for a small team that has just trained something. If your product depends on a frontier closed model or on your own weights, check those constraints before the price comparison rather than after.
Concrete next step: take one representative prompt from your actual application, run it against Groq’s free tier and one GPU-based provider, and measure time to first token and total completion time alongside the cost. That single comparison tells you which half of this market you belong in, and it takes an afternoon rather than a migration.
Sources and Further Reading
- Braintrust’s 2026 AI API comparison — per-provider pricing on a shared reference model with positioning notes
- Infrabase inference provider comparison — catalog breadth, throughput measurements and the bring-your-own-container tier
- Groq vs Cerebras head to head, the throughput and pricing figures behind the cost-per-second argument
- Spheron on Groq pricing versus GPU rental, the self-hosting economics comparison using vLLM benchmarks
- Digital Applied Q2 2026 pricing matrix, the 6x price spread and 5 to 7x latency spread across the field
For related coverage on this site, see the RunPod review and Vast.ai review for renting GPUs instead, Lambda vs Hetzner for hourly versus monthly billing, LLM API cost optimization for routing traffic between cheap and fast providers, and best local LLMs for running the same open weights yourself.
