vLLM vs Groq
Both are alternatives to OpenAI API (ChatGPT). Here's how they stack up — verified facts, no spin.
Also searched as Groq vs vLLM — same comparison, one verdict.
vLLM is open source (Apache-2.0) and Groq is not (Proprietary (platform); serves open-weight models) — so the real question is whether you want to own the local & sovereign ai stack or rent it.
vLLM
High-throughput serving for production-grade local inference.
vLLM is a fast inference and serving engine built for throughput, using paged attention to serve many concurrent requests efficiently. It is the choice when a team needs to self-host models at real scale.
Groq
The speed king — open models at 500+ tokens/second on custom LPU chips.
Groq runs open models (Llama and friends) on its custom LPU hardware and is, as of mid-2026, the fastest mainstream inference API available — 500+ tokens per second, at prices mostly under $1 per million tokens (Llama 3.3 70B at $0.59/$0.79). If your product's bottleneck is latency — voice agents, live UX, rapid tool loops — Groq is the honest answer. It's a proprietary hosted platform, but like Together, the models themselves are open, so you're renting speed, not locking in your stack.
Side by side
10 points of comparison, every one read from a verified field. Green marks the side that wins a row outright. A dash means we do not hold that fact — never that it is zero.
| vLLM | Groq | |
|---|---|---|
| Sovereignty ScoreOur transparent 0–100 composite for data ownership and exit cost. | 88 | 42 |
| Open source | Yes | No |
| Self-hostable | Yes | No |
| Local-first data | Yes | No |
| License | Apache-2.0 | Proprietary (platform); serves open-weight models |
| Pricing | Free / self-host | Most models under $1 per 1M tokens; Llama 3.3 70B $0.59/$0.79; batch −50% |
| RAM to run it wellThe figure that actually matters, not the vendor's minimum. | 24 GB VRAM minimum for useful production serving | — |
| Realistic running costWhat the box costs each month if you run it yourself. | $300–900/mo for a rented A100 or L40S, against managed inference with a platform margin on every token | — |
| Setup timeHonest first-install estimate, not the marketing quickstart. | A day including CUDA | — |
| Ongoing maintenanceThe part nobody budgets for. | Moderate. CUDA and driver versions are the recurring pain, not vLLM itself. | — |
vLLM edges it on the Sovereignty Score, but the right pick depends on the trade-offs below.
What will the GPU cost? Compare every company’s hourly price
Weighing both against staying on OpenAI API (ChatGPT)? Is OpenAI API (ChatGPT) free? What it actually costs →
vLLM
Strengths
- +Excellent throughput under concurrency
- +OpenAI-compatible server mode
- +Backed by a large community
Trade-offs
- −Aimed at capable GPUs, not laptops
- −Steeper operational learning curve
Groq
Strengths
- +Fastest inference on the market (500+ tok/s)
- +Very low prices on open models
- +OpenAI-compatible API — near drop-in
Trade-offs
- −Hosted-only; custom hardware means no self-host path for the speed
- −Model catalog is narrower than Together's
Which one fits you
The trade-offs above, turned into a decision. Find the line that describes your team.
Choose vLLM
if you want the source and the option to fork it, and you would rather run it on your own hardware, and your data should stay on machines you control.
Choose Groq
if fastest inference on the market (500+ tok/s).
Neither, yet
if both carry a real cost you should weigh first — aimed at capable GPUs, not laptops, and hosted-only; custom hardware means no self-host path for the speed. If either of those is a dealbreaker for your team, the shortlist is wrong rather than the choice.
What it takes to run these yourself
Real requirements and honest running costs, not the vendor quickstart.
vLLM vs Groq — common questions
Is vLLM a better fit than Groq for local & sovereign ai?
It depends on what you are optimising for, and the honest split is this: vLLM scores 88 to Groq's 42 on data ownership and exit cost, so it is the safer choice if you care about being able to leave. Groq earns its place on a different axis — fastest inference on the market (500+ tok/s). Neither is a wrong answer for every team; the table above is the actual comparison.
What happens if we want to switch later?
vLLM keeps its data local or in open formats, so leaving is an export rather than a negotiation. Groq keeps your data on its own infrastructure, which is the part to plan for: confirm the export path before you commit, not when you are already trying to leave.
Is vLLM free?
vLLM: Free / self-host. The other side of this comparison, Groq, is priced as Most models under $1 per 1M tokens; Llama 3.3 70B $0.59/$0.79; batch −50%. "Free" here means no licence fee — if you self-host it you still pay for the server, and the running cost row above is our honest estimate of that.
Can I self-host vLLM?
vLLM can be self-hosted; Groq cannot, so it stays on the vendor's infrastructure whatever you do.
Are vLLM and Groq both alternatives to OpenAI API (ChatGPT)?
Yes — both appear in our OpenAI API (ChatGPT) comparison, which is why they are worth putting side by side. People usually arrive here already having decided to move off OpenAI API (ChatGPT) and now choosing between the two replacements, which is a narrower and much easier question.
More OpenAI API (ChatGPT) head-to-heads
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Facts verified 2026-07-04. Licenses and pricing change — spotted something out of date? That's a correction we want.