Fireworks vs Baseten: Per-Token Serverless or Dedicated GPUs You Control

Who Wins Per Workload

The split is owning a GPU or never seeing one. Match your workload to the column.

Cells naming the loser keep the row honest. Bursty traffic goes to Fireworks because there is no GPU to strand; sustained saturation goes to Baseten because a per-minute GPU beats per-token billing once it stays busy. The cost section below shows exactly where that crossover sits.

Quick Comparison

Fireworks sells a token; Baseten sells a GPU minute. Morph sells the coding-agent loop.

Deployment Model: Serverless vs Dedicated

The core split is who owns the GPU. On Fireworks you usually do not; on Baseten you usually do.

Fireworks runs a serverless catalog of popular open models. You send a request to a per-token endpoint, and the model is already warm on shared infrastructure running the FireAttention engine. For workloads that outgrow shared limits, Fireworks also offers on-demand dedicated GPUs starting at $7.00/hr for H100 and H200, billed per second, that scale to zero. So Fireworks is serverless-first with a dedicated escape hatch.

Baseten is dedicated-first. You package a model with the open-source Truss framework and deploy it to GPUs you control, billed per minute, with full control over autoscaling and scale-to-zero. Baseten also exposes a serverless Model API for popular open models billed per token, but the platform's center of gravity is dedicated deployments managed by Multi-cloud Capacity Management (MCM), which provisions and scales GPUs across 10+ clouds and regions.

The practical read: Fireworks gets you to a token in minutes with no infra decisions. Baseten gets you a GPU you can size, pin, and saturate, which matters when you need predictable capacity or run a model that is not in anyone's catalog.

Pricing: Per-Token vs Per-Minute

Fireworks bills the output; Baseten bills the clock. Utilization decides which is cheaper.

Fireworks tiers serverless price by model size: $0.10/M under 4B, $0.20/M for 4B to 16B, and $0.90/M above 16B, with MoE models like Mixtral 8x7B at $0.50/M and larger MoE up to about $1.20/M. Headline models like DeepSeek V4 and GLM 5.1 carry their own per-model rates. Cached input tokens default to 50% off, and batch inference is half the serverless rate.

Baseten prices the GPU, not the token, for dedicated deployments: T4 at $0.63/hr, L4 at $0.85/hr, A10G at $1.21/hr, A100 80GB at $4.00/hr, H100 at $6.50/hr, and B200 at $9.98/hr, all billed per minute with no idle charge when scaled to zero. Its serverless Model APIs are priced per model, for example DeepSeek V3.1 at $0.50/$1.50 per million input/output tokens and GPT-OSS 120B at $0.10/$0.50.

Cost on a Real Workload

Take a Llama-class 70B model serving 50M output tokens per day, computed from the list prices above (early 2026).

Performance and Cold Starts

Fireworks optimizes the kernel; Baseten optimizes the cold start and the cloud.

Every Fireworks model runs on FireAttention, a production engine built on handwritten CUDA kernels plus adaptive speculative decoding. FireOptimizer trains a draft model on your production traffic: in a documented code-generation workload, the draft hit rate rose from 29% to 76%, delivering about a 2x speedup over a generic draft model. Fireworks reports 3x to 12x lower latency and up to 5.6x higher throughput than self-hosted vLLM. Because serverless endpoints stay warm on shared infrastructure, there is effectively no cold start.

Baseten's advantage shows up when you scale a dedicated deployment to zero and need it back fast. Its inference runtime is tuned to minimize cold-start latency on the first request after idle, so you can run scale-to-zero without paying the usual warm-up penalty. Baseten also ships specialized stacks: Baseten Embeddings Inference (BEI), built on TensorRT-LLM, claims over 2x higher throughput and 10% lower latency than other embedding solutions by packing tokenized inputs by sequence length rather than request count.

Fine-Tuning and Customization

Fireworks runs fine-tuning as a managed service; Baseten runs training on the same GPUs you deploy to.

Fireworks supports LoRA and full-parameter SFT, DPO, and reinforcement fine-tuning (RFT), priced per million training tokens. For models up to 16B, LoRA SFT is $0.50/M and full-parameter SFT is $1.00/M; larger tiers run up to $40/M for models over 300B. A key detail: fine-tuned models serve at the same price as the base model, so a LoRA on top of a 16B-plus model still costs $0.90/M at inference. Capabilities include streaming, function calling, structured JSON output, prompt caching, and predicted outputs for edit-and-rewrite workloads.

Baseten exposes training on the same dedicated GPUs at the same per-minute pricing, so you bring your own training code rather than calling a managed fine-tune endpoint. That is more work but more control: you can train any architecture, not just LoRA adapters on a curated base catalog. If you already package models with Truss, training and serving live in one workflow.

Compound AI and Custom Models

Baseten is the better home for anything that is not a single popular LLM.

Baseten Chains is an orchestration framework for compound systems like voice agents, RAG pipelines, and multi-step agents. It co-locates components on shared infrastructure, scales each piece independently, and cuts the network overhead that drives system-wide latency. Baseten reports Chains delivers up to 6x better GPU usage and roughly half the latency for compound workloads, and the platform serves non-LLM models including text-to-speech and embeddings through the same Truss and inference stack.

Fireworks centers on a curated catalog of popular open models plus fine-tunes of those base models. That keeps the serverless experience simple and fast, but a fully custom architecture or a multi-model pipeline of your own design is a more natural fit on Baseten, where Truss treats any model as a first-class deployable.

Compliance and Deployment Options

Both clear the standard enterprise bar; Baseten goes further on where the model runs.

Baseten supports cloud, self-hosted, and hybrid deployments, and MCM lets it run your model across 10+ cloud providers and regions for capacity and redundancy. That matters when you need data to stay in a specific environment or want a hedge against a single cloud running out of a given GPU. Fireworks offers self-hosted and dedicated options at the enterprise tier, but its default is a fully managed serverless plane.

When to Use Fireworks AI

• Zero-ops token APIs. If you want to call a popular open model and never touch a GPU, Fireworks serverless gets you a warm endpoint at $0.90/M for models above 16B with no cold start.
• Latency-sensitive serving. FireAttention kernels and adaptive speculative decoding claim 3x to 12x lower latency than self-hosted vLLM, with draft models trained on your own traffic.
• Fine-tunes of base models. LoRA and full-parameter SFT/DPO/RFT, and the fine-tune serves at the base model price rather than a premium.
• Bursty or unpredictable traffic. Per-token billing means you pay nothing when idle, so spiky workloads do not strand a reserved GPU.
• Cost-sensitive batch jobs. Batch inference at 50% of serverless and cached input at 50% cut the bill on large offline runs.

When to Use Baseten

• Custom or proprietary models. Package any architecture with Truss and deploy it to dedicated GPUs, including non-LLM models like TTS and embeddings.
• Compound AI pipelines. Baseten Chains co-locates components and scales them independently, with up to 6x better GPU usage and roughly half the latency on multi-step workloads.
• High, steady throughput. Per-minute dedicated pricing (H100 at $6.50/hr) beats per-token billing once a GPU stays saturated.
• Capacity and multi-cloud needs. MCM provisions GPUs across 10+ clouds, so you get redundancy and a hedge against single-cloud shortages.
• Strict deployment requirements. Cloud, self-hosted, and hybrid options with SOC 2 Type II and HIPAA cover regulated and data-residency cases.

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).

Frequently Asked Questions

Is Fireworks AI or Baseten cheaper?

Cheaper depends on duty cycle. Fireworks serverless bills per token (about $0.90/M for models above 16B as of early 2026), so you pay nothing when idle and the cost scales with traffic. Baseten bills dedicated GPUs per minute (T4 at $0.63/hr, H100 at $6.50/hr) with scale-to-zero. Below roughly 2,000 output tok/s sustained on one GPU, Fireworks is cheaper; above that a saturated dedicated GPU on Baseten wins. A 70B model at 50M output tokens/day costs about $1,350/mo on Fireworks versus ~$4,680/mo for an always-on H100, so that workload stays on Fireworks.

What is the main difference between Fireworks AI and Baseten?

Fireworks is serverless-first: per-token APIs on shared GPUs running FireAttention, with adaptive speculative decoding trained on your traffic. Baseten is dedicated-first: deploy any model via Truss to GPUs you control, scaled across 10+ clouds with Multi-cloud Capacity Management. Fireworks optimizes for zero-ops token APIs; Baseten optimizes for custom models, compound pipelines, and capacity.

Does Fireworks AI or Baseten handle cold starts better?

Fireworks serverless endpoints have effectively no cold start because the model stays warm on shared infrastructure. Baseten dedicated deployments scale to zero with no idle charge, which means a cold start on the first request after idle, though its runtime is built to minimize that delay. For traffic that must respond instantly with no warm-up, Fireworks serverless avoids the question.

Can I run a custom or proprietary model on these platforms?

Baseten is the stronger fit. You package any model with the open-source Truss framework and deploy it to dedicated GPUs, including non-LLM models like TTS and embeddings, and chain them with Baseten Chains. Fireworks focuses on a curated catalog of open models plus fine-tunes of those base models, so fully custom architectures are easier on Baseten.

Can I serve a fine-tuned model cheaply on Fireworks or Baseten?

On Fireworks, a LoRA or full SFT of a base model serves at the base model's price, so a fine-tune on a 16B-plus model still costs $0.90/M output with no inference premium; training is billed separately per million tokens. On Baseten, you train and serve on the same dedicated GPUs at per-minute pricing, so the fine-tune costs whatever the GPU costs to keep running, which is cheaper at sustained load and more expensive when idle. Fireworks is the better deal for a fine-tune you call intermittently; Baseten wins once that fine-tune keeps a GPU busy.