You've decided to run an open-weight model — a Llama, a Qwen, a DeepSeek distill — and you don't want to own the GPUs. That's the other half of the inference decision: not which engine serves the model, but whether you rent the serving at all. Three names dominate the rent-don't-own shortlist, and the trap is comparing them on a single number. They are tuned for different things.
The good news is the switching cost is near zero. All three speak the OpenAI API, so moving between them — or putting them behind one gateway and testing them head to head — is a base-URL change, not a rewrite. That makes the real question easier: not which is best, but which axis you're optimizing.
Groq: the latency bet, in silicon
Groq's whole pitch is speed, and it's a hardware pitch. Instead of GPUs, GroqCloud runs on the company's custom LPU — a Language Processing Unit built for one job: streaming tokens out fast. Independent benchmarks from Artificial Analysis have repeatedly placed Groq at or near the top of the provider field for output speed on Llama-class models. The trade-off is range: the catalog is deliberately narrow, open-weight only, and you won't find proprietary models like GPT-5 or Claude there.
Pick Groq when latency is the product — real-time voice, interactive UX, or an agent loop that makes many sequential model calls and pays for every millisecond of each.
The clearest verdict on Groq's speed isn't a benchmark. It's that Nvidia bought the bet.
In December 2025, Nvidia and Groq announced a non-exclusive agreement licensing Groq's inference technology, with Groq founder Jonathan Ross and other leaders joining Nvidia; GroqCloud continues to operate independently under a new CEO. The terms weren't disclosed (press reports put the figure near $20 billion, unverified). Read past the number and the signal is what matters: specialized inference silicon became something the GPU incumbent wanted to absorb as a feature of its own AI-factory roadmap — not a competitor it needed to crush. The fastest inference company validated its thesis by handing it to Nvidia.
Together AI: the whole lifecycle
Together AI makes the opposite bet — breadth over a single specialty. It hosts 200+ open-weight models across text, image, audio, and embeddings, and it doesn't stop at the endpoint. Fine-tuning (LoRA and full), dedicated endpoints, and rentable GPU clusters mean Together positions itself as the platform for the whole model lifecycle, not just a place to send a prompt.
That's who it's for: teams that want the widest catalog and expect to fine-tune or train, or that will eventually want dedicated infrastructure for a custom model rather than shared serverless capacity. Together raised a $305M Series B in early 2025 at roughly a $3.3B valuation, funding exactly that full-stack ambition. If your roadmap runs from "call a model" to "train our own," Together is built to keep you on one platform across that arc.
Fireworks AI: serving, productionized
Fireworks, built by ex-PyTorch engineers, sits between the other two: fast GPU serving via its own FireAttention stack, paired with the production features that distinguish a demo from a deployment. Reliable function calling, structured and JSON output, prompt caching, speculative decoding, and batch inference are first-class, across a broad day-0 catalog that picks up new open-weight releases quickly. The company raised a $250M Series C at a ~$4B valuation in late 2025, on the strength of that serving story.
Reach for Fireworks when you want speed and the messy production primitives an agent actually leans on — when the model needs to call tools dependably and return well-formed JSON under load, not just stream prose quickly.
The decision, stripped down
Price won't decide this. Per-token rates move weekly and overlap so much that you should check the live pricing pages and assume rough parity. What's durable is the axis each provider optimizes:
- Groq — latency above all, narrow catalog, open-weight only.
- Together — widest catalog plus fine-tuning and dedicated infra; the lifecycle platform.
- Fireworks — fast serving plus production features (function calling, structured output, caching).
Because they're all OpenAI-compatible, the smart play isn't to agonize up front. Wire one in, keep the seam swappable, and let your actual workload — how much latency hurts, how much you'll fine-tune, how hard your agent leans on structured tool calls — make the call.
