The short answer, up front: the five serverless inference APIs founders actually reach for in 2026 — Groq, Fireworks, Together, DeepInfra, and Baseten — are all OpenAI-compatible. Switching between them is a base_url and API-key change, not a rewrite. So the decision isn't "which SDK" — it's which single axis you optimize: raw latency (Groq), the lowest per-token floor (DeepInfra), the widest open-model catalog plus fine-tuning (Together), speed and tuning on one platform (Fireworks), or production-grade dedicated reliability (Baseten).
That framing matters because these are no longer interchangeable commodities. Each of the five just raised a serious round on a different bet, and the money tells you where each one is aiming.
The one-screen decision#
- Your product's feel depends on tokens-per-second → Groq. Its LPU hardware is built for latency, not for hosting your fine-tune.
- Per-token cost is the whole constraint → DeepInfra. Lowest floor for shared open models — but read the fine print on quantization.
- You want the biggest model menu and room to fine-tune → Together. Broadest catalog, self-serve tuning, dedicated endpoints.
- You want speed AND fine-tuning on one platform → Fireworks. FireAttention kernels plus a full tuning stack (SFT, LoRA, DPO, reinforcement FT).
- You're scaling a production workload that needs an SLA → Baseten. Dedicated deployments are its core; Model APIs are the per-token on-ramp.
They're all OpenAI-compatible — so don't lock in#
Every one of these exposes an OpenAI-style chat-completions endpoint. Keep the provider behind an environment variable and you can A/B two of them on cost and latency the same afternoon. Here's the whole switch:
from openai import OpenAI
# Each provider is a base_url + key + model string. Nothing else changes.
PROVIDERS = {
"groq": ("https://api.groq.com/openai/v1", "GROQ_API_KEY"),
"fireworks": ("https://api.fireworks.ai/inference/v1", "FIREWORKS_API_KEY"),
"together": ("https://api.together.xyz/v1", "TOGETHER_API_KEY"),
"deepinfra": ("https://api.deepinfra.com/v1/openai", "DEEPINFRA_API_KEY"),
"baseten": ("https://inference.baseten.co/v1", "BASETEN_API_KEY"),
}
import os
base_url, key_env = PROVIDERS[os.environ["INFERENCE_PROVIDER"]]
client = OpenAI(base_url=base_url, api_key=os.environ[key_env])
resp = client.chat.completions.create(
model=os.environ["INFERENCE_MODEL"], # e.g. a Llama / Qwen / DeepSeek id
messages=[{"role": "user", "content": "Summarize this ticket in one line."}],
)
print(resp.choices[0].message.content)Confirm each base URL and the exact model id in the provider's own docs — endpoints drift — but the shape is stable, and that portability is your leverage in every pricing conversation.
What each one is really selling#
- Groq — $650M raised (June 2026) to scale its inference cloud on custom LPU silicon. The pitch is latency: very high tokens-per-second and low time-to-first-token on the hosted catalog. You don't bring your own weights; you buy speed.
- Fireworks — a $1.505B Series D at a $17.5B valuation (July 16, 2026), with reported ARR past $1B and 40 trillion tokens a day. FireAttention kernels for throughput, plus the most complete self-serve fine-tuning stack of the group. The bet: teams that want to customize a fast open model in one place.
- Together — an $800M Series C at $8.3B (July 1, 2026). The widest open-model catalog, self-serve fine-tuning, private models, and reserved/dedicated endpoints. The bet: be the default menu for open-weight inference.
- DeepInfra — the price-floor player. Positions on the cheapest per-token rates and the broadest bargain catalog. The trade-off to watch: aggressive FP4 quantization or truncated context windows on some endpoints, which is exactly where a cheap sticker rate quietly costs you quality.
- Baseten — a $1.5B Series F at valuations reported up to $13B (June 22, 2026), serving over a billion inference calls a day. Its center of gravity is dedicated, production-grade deployment; the per-token Model APIs are the on-ramp. The bet: reliability at scale. We unpacked why that raise mattered in Inference became its own $13B category.
Don't anchor on a price you read anywhere#
Here's the honest part: per-token prices on all five move monthly, and the numbers floating around aggregator blogs are frequently stale, inconsistent, or plain wrong. We deliberately don't print a price sheet you'd cite next week. Instead, do the two-minute thing that's actually correct: open each provider's live pricing page (linked in the sources below), price your model at your real input-to-output ratio, and compare like for like — same model, same context length, same quantization.
Two adjacent decisions bracket this one. If a hosted open model isn't enough and you need to serve your own fine-tune, the question becomes where to deploy it — when to leave a managed inference host for your own GPUs walks the break-even. And if you've decided to rent raw hardware instead, CoreWeave vs Lambda vs Nebius covers the GPU-cloud layer underneath all of this.
The rule to remember: pick the provider whose one bet matches your one constraint, keep the base_url in an env var so you're never trapped, and re-check prices on the source pages — never a blog — the day you commit real volume.
