Replicate vs Groq: Run-Any-Model Catalog vs Fastest Fixed-Menu Text

Who Wins Per Workload

The two rarely compete for the same job. This table maps the decision you are actually making to the winner and why.

Quick Comparison

Replicate is flexible and pays by the second. Groq is fast and pays by the token. Morph is the column most coding agents actually need.

Billing Model: Per Second vs Per Token

The single biggest difference is how you get charged, and it shapes which workloads make sense on each.

Replicate bills GPU wall-clock time per second. Public serverless models only charge while they process your request; setup and idle time on a public model are free. But most custom or private models run on dedicated hardware, where you pay for setup time, idle time, and active processing time. So a private deployment that sits idle between bursts still bills you for every second the instance is online. The exception is fast-booting fine-tunes, which charge only for active processing.

Groq bills per token, full stop. There is no idle charge, no GPU-hour, no setup fee. You send a request, you pay for the input and output tokens it consumes. A Batch API cuts that 50% for async jobs, and prompt caching cuts cached input tokens 50%; the two stack toward roughly 25% of on-demand for cacheable batch workloads.

The practical rule: Replicate wins when a GPU stays near 100% busy, because per-second time is cheap when fully utilized. Groq wins for bursty or low-duty-cycle traffic, because you never pay for idle silicon.

Pricing: Concrete Numbers

Direct comparison is awkward because the units differ, so here are both, as of early 2026.

Replicate also bills some official models per token (for example Claude 3.7 Sonnet at $3.00 per million input tokens), but the platform's native model is per-second hardware. Multi-GPU configs (2x, 4x, 8x) scale proportionally and require committed-spend contracts.

Cost on a Real Workload

Speed: Groq's LPU vs Replicate's GPUs

On the models Groq supports, Groq is the faster of the two by a wide margin.

Groq does not use GPUs. Its LPU (Language Processing Unit) is custom silicon built for sequential, single-stream token generation, which is exactly the part of inference where GPUs leave latency on the table. The result: 394 tok/s on Llama 3.3 70B standard, 840 tok/s on Llama 3.1 8B, and up to 1,660 tok/s on Llama 3.3 70B with speculative decoding, a 6x jump over the 250 tok/s baseline on the same 14nm chip.

Replicate speed is whatever the model and GPU tier deliver. An H100 is fast, but you are still on general Nvidia hardware running a general serving stack, and you tune throughput by choosing hardware and batch settings yourself. For interactive single-user latency, Groq wins. For batched throughput on a custom model, a well-tuned Replicate H100 deployment can be competitive.

Cold Starts: The Hidden Replicate Tax

Groq has no cold-start problem; Replicate's serverless design has a real one.

Replicate scales public models to zero to save cost, so a model that has not run recently must reload into GPU memory before it responds. Custom Cog models can take 10 to 180 seconds to boot depending on weight size: roughly 60 seconds for the machine plus another 10 or more to pull the weights. To avoid that, you keep instances warm, which means paying for idle time. Replicate has driven fine-tune cold boots under one second, but general custom deployments still pay the boot tax.

Groq serves an always-on catalog, so there is no scale-to-zero and no boot latency on a request. The trade is that you cannot bring your own weights to get that behavior; the always-on guarantee only covers models Groq already hosts.

Model Catalog: Open vs Fixed

Replicate runs anything you can package; Groq runs what it has optimized.

Replicate's whole pitch is the open catalog plus Cog. You take arbitrary model code, wrap it in a Cog container, push it, and Replicate generates the API server and handles the GPU deployment. That covers language models, image generators (FLUX, Veo, Kling), audio, video, embeddings, and private fine-tunes. The cost of that flexibility is that Cog packaging is non-standard: your model is not a plain Docker image you can lift and run elsewhere.

Groq ships a curated menu: Llama 3.1/3.3, GPT-OSS 20B and 120B, Kimi K2, Qwen3 32B, and a few others, each hand-tuned for the LPU. You get speed and predictable behavior, but you cannot upload custom weights or a private fine-tune. If your model is not on the list, Groq is not an option.

Features & API Surface

Both expose modern API features, but aimed at different jobs.

Groq is built for agentic LLM use. It supports structured outputs with a JSON schema and strict: true constrained decoding that guarantees 100% schema adherence, parallel function calling with up to 128 tools, an OpenAI-compatible endpoint, a Batch API, prompt caching, and compound models with automatic built-in tool use. For tool-calling agents that need fast, schema-valid responses, this surface is strong.

Replicate is built for model deployment. Its API is prediction-centric: submit input, poll or stream the prediction, get output. It handles autoscaling on dedicated deployments, webhooks, and per-model schemas generated from your Cog definition. The strength is operational control over any model; the weakness is that you assemble the agent features (JSON schema enforcement, tool routing) yourself on top.

When to Use Replicate

• Custom or private models. If you need arbitrary weights, a private fine-tune, or an architecture Groq does not host, Cog packaging and dedicated GPUs are the reason to be here.
• Multimodal generation. Image, video, and audio models (FLUX, Veo, Kling) live on Replicate. Groq is text and speech only.
• Steady, high-utilization workloads. Per-second GPU billing is cheap when the hardware stays busy. Above roughly 60 to 70% sustained duty, dedicated instances beat per-token rates.
• Operational control. You pick the GPU tier, set autoscaling bounds, and own the serving stack. For teams that want to tune the deployment, that control is the point.
• Rapid model prototyping. Push a Cog container and get a working API in minutes without standing up your own GPU infrastructure.

When to Use Groq

• Latency-sensitive serving. 394 tok/s on Llama 3.3 70B and up to 1,660 with speculative decoding. For interactive chat or real-time agents, the LPU is the fastest of the two.
• Bursty or low-duty traffic. Per-token billing with no idle charge means you never pay for silence. Spiky workloads are far cheaper than keeping a dedicated GPU warm.
• Tool-calling agents. Constrained-decoding structured outputs and up to 128 parallel function calls make schema-valid agent responses reliable.
• Cost-sensitive open-model use. $0.05/$0.08 for Llama 3.1 8B, with Batch API and prompt caching each cutting 50%, lands among the cheapest fast serving available.
• Zero infrastructure. No instances, no cold starts, no autoscaling config. Send tokens, get tokens.

Frequently Asked Questions

Is Replicate or Groq cheaper?

Cheaper depends on utilization, not on the provider. Groq is cheaper for bursty or high-throughput open-model serving: Llama 3.3 70B is $0.59/$0.79 per million input/output tokens, Llama 3.1 8B is $0.05/$0.08. Replicate bills GPU time per second ($5.04/hr A100 80GB, $5.49/hr H100), which only wins above roughly 60 to 70% sustained GPU duty. For idle or spiky workloads, Replicate's per-second dedicated billing usually costs more.

Why is Groq so much faster than Replicate?

Groq runs custom LPU silicon designed for sequential token generation, not GPUs. It hits 394 tok/s on Llama 3.3 70B and up to 1,660 tok/s with speculative decoding. Replicate runs standard Nvidia hardware (T4, L40S, A100, H100), so speed depends on the model and tier you choose. For single-stream latency on supported models, Groq is faster.

Can I run any model on Groq?

No. Groq serves a fixed catalog (Llama 3.1/3.3, GPT-OSS 20B/120B, Kimi K2, Qwen3 32B, and a few more). You cannot upload custom weights or a private fine-tune. Replicate is the opposite: package any model with Cog and run it on dedicated GPUs, including custom architectures and private fine-tunes.

Does Replicate have cold starts?

Yes. Public serverless models scale to zero, and custom Cog models can take 10 to 180 seconds to boot depending on weight size, because the container and weights load into GPU memory on demand. Fine-tune cold boots are now under one second, but general custom deployments still pay the boot tax unless you keep instances warm and pay for idle time.

Can I move a model from Replicate to Groq or vice versa?

Only if the model is on Groq's menu. Replicate packages models as Cog containers, a non-standard format that does not lift-and-shift to any other host, including Groq, which accepts no custom weights at all. Going the other direction is easy: any open model Groq serves (Llama 3.1/3.3, GPT-OSS, Qwen3 32B) can also be packaged for Replicate, you just trade Groq's LPU latency floor for whatever a Replicate GPU tier delivers. The two are not interchangeable runtimes; they are different deployment models that happen to overlap on a few open LLMs.