Baseten vs Groq: Groq for Lowest Latency on a Fixed Menu, Baseten for Your Own Model and Compliance

Who Wins Per Workload

The choice is rarely "which is better" in the abstract. It is which one wins for the specific job you are running.

Quick Comparison

Groq is a hosted token API on fixed custom silicon. Baseten is GPU infrastructure you point at any model. Morph is included as a reference point only for the narrow coding-agent apply loop, which neither general host targets.

Architecture: Custom Silicon vs GPU Flexibility

The split starts at the chip. Groq bet on a fixed, deterministic processor. Baseten bet on flexible Nvidia GPUs plus a software stack that squeezes them.

Groq: deterministic LPU

Groq's LPU replaces off-chip HBM with a flat SRAM mesh on the processor die. Every weight and KV-cache token sits in on-chip SRAM at all times, giving roughly 80 TB/s of bandwidth versus about 8 TB/s for GPU HBM. There are no caches, no branch predictors, and no runtime decisions: the compiler statically schedules the entire execution graph, including inter-chip communication, down to individual clock cycles.

The practical result is flat decode latency that does not vary with batch size, up to the chip's capacity. The tradeoff is that SRAM is small, so large models are sharded across many LPUs, and you run only the models Groq has compiled for the hardware.

Baseten: Nvidia GPUs plus a software stack

Baseten runs standard Nvidia GPUs (T4, L4, A10G, A100, H100, B200) and competes on software. It benchmarks TensorRT-LLM, SGLang, and vLLM per model and picks the fastest, then patches and extends them. It built production speculative decoding into the TensorRT-LLM engine builder and found EAGLE-3 most effective, lifting GPT-OSS 120B from about 400 to about 650 tok/s.

Baseten encapsulates models in Firecracker-style micro-VMs, shards weight files across the GPU fleet, and uses cold-start snapshots to bring models up to 20GB online in under 10 seconds. That flexibility is the point: any model, any supported GPU, your choice of deployment and serving engine. The cost is that a scale-to-zero deployment pays that startup on a fully cold first call, where Groq's always-loaded LPU never does. Neither is built for the coding-agent apply loop; if applying model-generated code edits is the bottleneck, that is a different tool (Morph Fast Apply, ~10,500 tok/s, with published benchmarks).

Speed: Groq Wins on Raw Latency

On a fixed model that Groq serves, Groq is faster, often by a wide margin.

Groq's flat decode latency matters for real-time use. Voice agents, autocomplete, and live tool-calling all benefit from a predictable time-to-first-token and steady tokens-per-second that does not jitter under load. The speculative-decoding Llama 3.3 70B benchmark above 1,600 tok/s is roughly 6x its non-speculative endpoint.

Baseten is not trying to beat Groq on raw token latency. Its win is that it can run GPT-OSS 120B at about 650 tok/s with EAGLE-3, on a GPU you control, alongside a custom model Groq does not host. Where the workload is one of Groq's cataloged models and latency is the only metric, Groq leads.

Pricing: Per Token vs Per GPU-Minute

Groq prices everything per token. Baseten prices Model APIs per token but dedicated deployments per GPU-minute, which changes the math at scale.

Groq cuts rates 50% on cached input tokens and 50% on the Batch API, and the two stack toward roughly 25% of on-demand for batch-friendly cached workloads. The free tier gives every model 30 RPM, 6K TPM, and 14,400 requests/day with no card required.

Baseten bills only for time the model uses compute, with scale-to-zero so idle GPUs cost nothing. The crossover: per-token pricing wins at low and bursty volume, while per GPU-minute wins once you saturate a replica, since a fully-utilized H100 at about $6.50/hr can serve far more than $6.50 of tokens at frontier-model prices.

Cost on a Real Workload

Deployment: Hosted API vs Your Infrastructure

This is the cleanest dividing line. Groq is a hosted API. Baseten lets you choose where the model runs.

Baseten supports managed cloud, self-hosted in your own VPC, and hybrid, all under SOC 2 Type II and HIPAA, with region restrictions for data residency. That makes it the default when sensitive data cannot leave your environment or when compliance dictates where inference runs.

Groq's standard product is GroqCloud, a hosted API. On-prem LPU access exists but is enterprise-only. For most teams, Groq means sending tokens to Groq's cloud and getting them back fast.

Model Selection: Fixed Catalog vs Bring Your Own

Groq serves the models it has compiled for the LPU. Baseten runs whatever you package.

Groq's catalog covers popular open models: Llama 3.x and 4, GPT-OSS 20B and 120B, Qwen3, and others, each pre-optimized for the hardware. You get speed and simplicity, but you cannot upload arbitrary custom weights on the standard tier, and you wait for Groq to add a model before you can run it.

Baseten packages any model with Truss, its open-source model-packaging standard, and runs custom, open-source, or fine-tuned weights. Its Model APIs also expose frontier open models (DeepSeek V4, Kimi K2.6, GLM 5.1, Nemotron 3) for instant prototyping, and it offers training plus one-click deployment. If your model is a private fine-tune, Baseten can serve it and Groq generally cannot.

Features for Builders

Both expose OpenAI-compatible APIs, so swapping either into an existing client is a base-URL change. The differences are in the surrounding capabilities.

Groq's strict structured-output mode (response_format with strict: true) guarantees schema compliance, which is useful for agents that must return parseable JSON every call. Baseten's feature surface depends on the model you deploy, but it covers more modalities: transcription with diarization, text-to-speech with real-time streaming, image generation including ComfyUI, and embeddings with claimed 2x throughput.

When to Use Groq

• Latency is the metric. Deterministic LPU decode gives ~394 tok/s on Llama 3.3 70B with flat latency under load. Voice agents, autocomplete, and live tool-calling benefit most.
• You run a cataloged open model. Llama, GPT-OSS, Qwen, and others are pre-optimized. No setup, no GPU management.
• Cost-sensitive at low to medium volume. Per-token pricing plus a free tier (14,400 requests/day) and a 50% Batch API discount keep spend low when you are not saturating hardware.
• You need guaranteed JSON. Strict structured outputs enforce schema compliance, which matters for agents parsing every response.
• Simplicity over control. A hosted token API with no infrastructure to run.

When to Use Baseten

• Custom or fine-tuned models. Package anything with Truss and run private weights on dedicated GPUs. Groq's catalog cannot do this.
• Compliance and data residency. SOC 2 Type II, HIPAA, and self-hosting in your own VPC, with region restrictions for sensitive workloads.
• Sustained high load. A saturated A100, H100, or B200 billed per minute beats per-token pricing once a replica stays busy.
• Multimodal pipelines. Transcription, TTS, image generation, and embeddings with optimized runtimes in one platform.
• Compound systems. Chains runs multi-model workflows where each step uses different hardware with point-to-point communication.

Frequently Asked Questions

Is Groq cheaper than Baseten?

For the same open model on serverless tokens, Groq is usually cheaper at low to medium volume. Llama 3.3 70B is $0.59 input and $0.79 output per 1M tokens, billed only for tokens used, plus a free tier. Baseten's dedicated deployments bill per GPU minute (A100 ~$0.067/min, H100 ~$0.108/min), which beats per-token pricing only once a replica stays saturated. As of early 2026.

Why is Groq so fast?

The LPU keeps every weight and KV-cache token in on-chip SRAM at roughly 80 TB/s, versus about 8 TB/s for GPU HBM. The compiler statically schedules execution down to individual clock cycles with no caches or branch predictors, giving flat decode latency regardless of batch size. Llama 3.3 70B runs at about 394 tok/s, and a speculative variant has been benchmarked above 1,600 tok/s.

Can I deploy in my own cloud?

Baseten supports managed cloud, self-hosted in your own VPC, and hybrid, all under SOC 2 Type II and HIPAA with region-restricted options. Groq is primarily GroqCloud (hosted); on-prem LPU access is enterprise-only and not part of the standard API.

Does Baseten support custom and fine-tuned models?

Yes. Baseten packages any model with Truss and runs custom, open-source, or fine-tuned weights on dedicated GPUs, with training plus one-click deployment and Chains for compound workflows. Groq serves a fixed catalog of optimized open models and does not accept arbitrary custom weights on the standard tier.

Does Groq have cold starts the way Baseten does?

No. Groq's LPU serves a fixed, always-loaded catalog, so the first call hits the same flat latency as the millionth, with no cold start. Baseten uses scale-to-zero, so an idle dedicated deployment must spin a replica back up; its cold-start snapshots bring models up to 20GB online in under 10 seconds, but a fully cold first call still pays that startup. Pick Groq's no-cold-start floor for spiky traffic that cannot tolerate any first-call delay; pick Baseten's scale-to-zero when traffic is steady or you can keep a minimum replica warm.