An AI API is a service that runs a model for you and returns the answer over an HTTP call. You send a prompt, someone else's hardware does the inference, and tokens come back. The whole category competes on two axes that a buyer can feel immediately: how fast the tokens arrive, and how much each call costs. Pick for your latency budget and the models you actually need, then watch speed and cost on your own traffic before you commit.
This page is about the compute layer: whose hardware runs the model. It is not about the interface layer, the "call many models through one endpoint" question. That is a separate decision, and it lives in our LLM gateways roundup. A provider like Together shows up in both conversations, but for different reasons: there, as an OpenAI-compatible endpoint you can route through; here, judged on raw speed and price. If your problem is "I want one interface over a dozen models," start with the gateway page or a broad aggregator like OpenRouter. If your problem is "I have picked open models and need whose API runs them fastest and cheapest," you are in the right place.
What an AI API is, and what it isn't
The plain version: you rent inference. The provider holds the model weights on GPUs or custom chips, keeps them warm, and bills you per token (or per second of compute). You never touch a container.
That is different from three neighbors people confuse it with:
- The interface layer. Gateways and unified APIs give you one endpoint over many providers, plus routing, failover, and caching. They usually do not own the hardware. They sit in front of the APIs on this page.
- The frontier labs' own APIs. OpenAI, Anthropic, and Google sell access to their closed models. Fast, excellent, and the only place to get those specific models, but you cannot run Llama or DeepSeek on them, and you cannot move the workload elsewhere for a better price.
- Renting raw GPUs. You can rent an H100 by the hour and serve the model yourself. Total control, total operational burden. The providers below exist so you do not have to.
The open-model API market sorts into three camps. Custom-silicon speed (Groq, Cerebras) builds its own chips to make token generation absurdly fast. Software-optimized serving (Fireworks, Together) runs standard GPUs but wrings speed and breadth out of the serving stack. Deploy-your-own hosting (Baseten) hands you managed, autoscaling infrastructure to run whatever weights you bring.
What to look for
Five criteria separate a good fit from a bad one:
- Speed, split in two. Time to first token (how long before the answer starts) matters for anything interactive. Throughput, or tokens per second once it is going, matters for long generations. A provider can win one and lose the other, so know which your app feels.
- Price per use. Usually dollars per million input and output tokens. Output tokens cost more. For open models the spread between providers is real, so the cheapest credible option is worth finding.
- Which models it runs. The catalog, plus whether you can fine-tune or get a dedicated endpoint. A provider that runs the exact model you need beats a faster one that does not.
- Reliability at scale. Rate limits, capacity during traffic spikes, uptime, and how quickly a hot new model actually appears. Benchmarks are run on quiet endpoints; production is not quiet.
- Whether you can see what each call did. Speed and price are only real once you measure them on your own workload, not on a leaderboard. Logging every call, with latency and token cost attached, is what lets you compare providers honestly. Our LLM monitoring roundup covers the tools that do this.
A note on benchmarks: they move every week as providers tune their stacks and new models land. Rather than freeze numbers that will be wrong by the time you read this, we point to the live source, Artificial Analysis, which tracks speed and price across hundreds of endpoints. Use it to check the current state before you decide.
The five APIs, compared
Groq
Groq builds a custom inference chip it calls the LPU, and the whole company is organized around one promise: consistently low-latency, high-speed responses on popular open models. In practice that means answers that start fast and stream fast, run after run, which is exactly what real-time products need.
Approach: custom silicon tuned for token generation. Speed and price: among the fastest for interactive use, with competitive per-token pricing; check Artificial Analysis for the current ranking. Models: a curated catalog of popular open models (Llama, Qwen, Gemma, and similar), not a bring-your-own-weights platform. Best for: latency-sensitive apps, voice, and agent loops where every round trip counts. Limit: you are limited to the models Groq chooses to host, and there is no deploy-your-own path.
Cerebras
Cerebras runs inference on its wafer-scale engine, a single chip the size of a dinner plate, and it regularly tops the raw-throughput charts for small and mid-size open models. When the number you care about is tokens per second on a Llama-class model, Cerebras is usually at or near the front.
Approach: wafer-scale hardware built for maximum throughput. Speed and price: frequently the raw-speed leader on the models it serves; verify against the live leaderboard. Models: narrower than the software-serving providers, focused on the popular open models it can run fastest. Best for: workloads that need maximum throughput and can use the models on offer. Limit: a smaller catalog, and capacity you should confirm for your volume.
Fireworks AI
Fireworks is the software-optimization camp done well: standard GPUs, a heavily tuned serving stack, and a focus on running large, frontier-class open models quickly. It pairs that with production features like fine-tuning, dedicated deployments, and on-demand GPUs.
Approach: software-optimized serving of large open models. Speed and price: fast for its weight class, competitive pricing on big models like DeepSeek and the larger Llamas. Models: a broad catalog that leans toward the frontier open models, plus multimodal. Best for: teams that want the biggest open models served fast, with room to fine-tune and scale. Limit: it wins on software, not a single fast chip, so it will not always top a raw-speed chart against custom silicon.
Together AI
Together has the widest open-model catalog of the group: Llama, Mixtral, DeepSeek, Qwen, and hundreds more, with fast time-to-first-token, fine-tuning, and dedicated endpoints. If your requirement is "I need this specific open model, and maybe a fine-tuned version of it," Together most often has it.
Approach: broad, software-optimized serving plus customization. Speed and price: fast and priced competitively across a large catalog; not the single-model speed champion. Models: the broadest open-model selection here, including image models. Best for: breadth and customization, when model choice and fine-tuning matter more than winning a latency benchmark. Limit: on any one model, a specialist may be faster.
Baseten
Baseten is the deploy-your-own option. Instead of only offering a fixed menu, it gives you managed, autoscaling infrastructure to run open models or your own weights on dedicated hardware, with a packaging framework to get a model into production. It also offers ready-made APIs for popular open models.
Approach: managed hosting and autoscaling for models you choose to deploy. Speed and price: depends on the hardware and model you run; you are paying for dedicated capacity and control. Models: open models plus anything you can package, including custom and private weights. Best for: teams that want control over how their model is deployed and scaled, not just a token endpoint. Limit: more setup than a pure API, because control is the point.
Honorable mentions. Replicate is the easiest way to run or deploy almost any model from a huge community catalog, billed per second. Lepton, now part of NVIDIA's cloud, offers fast, efficient inference. Perplexity exposes its search-grounded Sonar models through an API, useful when you want answers with live web context built in.
Side-by-side
Awards below are the thing each provider is best at, not a single overall ranking. Numbers move weekly, so treat Artificial Analysis as the live source of truth for current speed and price.
| Criterion | Groq | Cerebras | Fireworks | Together | Baseten |
|---|---|---|---|---|---|
| Consistent low latency | Best | Strong | Good | Good | Depends on setup |
| Raw throughput | Strong | Best | Good | Good | Depends on setup |
| Frontier open models fast | Limited | Limited | Best | Strong | Bring your own |
| Model breadth | Curated | Narrow | Broad | Broadest | Anything you deploy |
| Fine-tuning / dedicated | Limited | Limited | Yes | Yes | Full control |
| Deployment control | No | No | Some | Some | Best |
| Best for | Real-time apps | Max throughput | Big open models | Breadth + tuning | Custom deployment |
How to pick
The decision tree is short:
- You need the lowest, most consistent latency (voice, agents, anything a human waits on): Groq.
- You need maximum throughput on small or mid-size open models: Cerebras.
- You need large, frontier open models served fast, with fine-tuning: Fireworks.
- You need the widest model selection or a specific fine-tuned model: Together.
- You need control over how the model is deployed and scaled, or you are bringing your own weights: Baseten.
Two things hold across all five. First, if you want to keep your options open, put a gateway or unified interface in front of the API so switching providers is a config change, not a rewrite. Second, whatever you pick, log every call with its latency and token cost so you can compare providers on your own traffic instead of on someone's leaderboard. The provider that wins a benchmark on a quiet endpoint is not always the one that wins on your workload at your volume.
FAQs
Is an AI API the same as a unified or one-interface API? No. An AI API runs the model on its own hardware. A unified API or gateway is an interface layer that routes your call to one of many providers through a single endpoint. You often use both: the gateway decides where the call goes, the AI API runs it. Together and Groq appear in both discussions because they offer an OpenAI-compatible endpoint you can route to, but that is the interface question, not the speed-and-price one.
How do I compare providers on my own workload? Run a slice of your real traffic through each candidate and record time to first token, tokens per second, and cost per call for your prompts and models. Leaderboards use short, controlled prompts on idle endpoints; your prompts, context lengths, and traffic patterns are different. Any decent monitoring tool will attach latency and token cost to every call so the comparison is apples to apples.
What is the cheapest API for running open models? It depends on the model and whether you optimize for input or output tokens, and prices change often. The spread between providers on the same open model is real, so check the current per-token pricing on Artificial Analysis rather than trusting a number that ages in weeks. For steady, high volume on one model, a dedicated endpoint (Fireworks, Together, or Baseten) can beat per-token pricing.
What is the fastest API to run Llama? For raw tokens per second on Llama-class models, Cerebras and Groq trade the top spots depending on the exact model and the week. For the largest Llama variants, Fireworks and Together are strong on the software-serving side. The honest answer is to check the live leaderboard for the specific model size you plan to run.
How do I watch speed and cost across several providers at once? Route everything through one interface and instrument each call, so provider, model, latency, and cost all land in the same view. That turns "provider B got slower last Tuesday" into a chart instead of a support ticket. The tools that do this are in the LLM monitoring roundup.