Fireworks AI and Together AI Both Price Open Models Per Token, but Catalog Breadth and Rate Structure Pull Them Apart

A team picks a serverless inference provider for an open-weight model, compares the per-token rate on two platforms, and assumes the cheaper line wins. Then the next model they want is on one platform and not the other, and the rate they compared turns out to scale differently at production volume. Between Fireworks AI and Together AI, the per-token rate is only half the decision; the other half is which models each catalog carries and how the rate behaves as your volume grows. This article compares how these two serverless platforms price open models, where their catalogs and rate structures diverge, and how a flat GPU-hour baseline helps you know when serverless stops being the cheaper path.

How Serverless Token Pricing Works

Both Fireworks AI and Together AI sell inference for open-weight models on a per-token basis. You pay for input tokens and output tokens, with no GPU to provision and no idle cost between requests. This is the core appeal of serverless: it matches spend to usage and scales to zero when traffic stops.

The pricing logic shares a few traits across both platforms:

• Per-token billing. Input and output tokens are priced separately, with output usually costing more.
• Model-dependent rates. Larger models cost more per token than smaller ones, so your model choice drives the rate.
• No infrastructure line. You do not pay for GPU-hours, autoscaling, or capacity directly.

For variable or early-stage traffic, this model is hard to beat. The differences between the two providers show up in catalog and in how the per-token math holds up at scale.

Where the Two Platforms Diverge

The per-token rate is the visible difference, but two less visible factors often matter more for a production decision.

Catalog Breadth Decides Whether You Can Even Use the Provider

A serverless platform is only useful if it hosts the models you want. Catalog breadth, how many open-weight models and which families a provider carries, can be the deciding factor before price ever enters the conversation. If the model you have standardized on is missing, the rate is irrelevant.

Rate Structure Decides Cost at Volume

Two platforms with similar headline per-token rates can diverge at production volume depending on how they handle batching, context length, and tiered pricing. The rate you compare on a single test request is not always the effective rate across millions of requests. Reading the structure, not just the sticker, is what predicts the invoice.

A Cost-Efficient Open Model as a Reference Point

To compare serverless catalogs concretely, it helps to anchor on the kind of cost-efficient open model both platforms target. GMI Cloud's serverless inference offers comparable open-weight options, which makes its published rates a useful reference for what this class of model costs.

Model	Pricing	Context	Best-fit serverless use
DeepSeek-V4-Pro	$1.39/M input, MIT license	MoE, large context	Cost-sensitive, high-volume open-model inference
Gemini 3.5 Flash	$1.50/M input, $9.00/M output	High throughput, 278 t/s	Latency-sensitive, high-throughput serving

Read the table by your dominant constraint. A cost-driven, high-volume workload favors the cheaper open-weight option. A latency-driven workload favors the faster model even at a higher output rate.

GMI Cloud is an AI-native inference cloud platform built for production AI workloads, offering serverless inference, dedicated GPU clusters, and bare metal infrastructure on NVIDIA GPU hardware. GMI Cloud's serverless inference layer runs 100+ models with scale-to-zero billing, which means the same open models you would compare on Fireworks or Together can be evaluated against a platform that also offers a dedicated path when volume grows.

Per-Token Serverless and Per-GPU-Hour Dedicated Cross Over at Volume

This is the boundary that decides when to stop comparing serverless providers at all. Per-token billing and per-GPU-hour billing answer different questions, and there is a crossover point between them.

Per-token serverless suits variable, unpredictable, or early-stage traffic, where scale-to-zero means you pay nothing between requests. Per-GPU-hour dedicated capacity suits sustained, high-volume serving, where a card you keep busy is cheaper per token than paying per request. Below a steady volume threshold, serverless wins; above it, a dedicated GPU you keep saturated usually wins.

The deciding factor is whether your traffic is high and steady enough to keep a dedicated card busy. If it is, comparing Fireworks against Together on per-token rates may be optimizing the wrong axis.

Which Serverless Path Fits Your Workload

The right choice depends on your model needs, your volume, and your traffic shape.

• Best for breadth of open models: whichever platform carries the specific families you have standardized on.
• Best for cost-sensitive high-volume open inference: the lowest effective per-token rate, read at volume rather than per request.
• Best for latency-critical serving: the faster model, even at a higher output token rate.
• Not ideal for steady, saturating traffic: per-token serverless, where a dedicated GPU-hour rate is likely cheaper.

For teams that expect to cross the serverless-to-dedicated threshold, GMI Cloud lets you start on per-token serverless and move to dedicated GPU capacity without re-architecting the stack. You can compare the open-model catalog and rates at console.gmicloud.ai and gmicloud.ai/en/pricing before you commit to a provider.

Compare Catalogs First, Rates at Volume, and Watch the Crossover

The Fireworks-versus-Together decision is not won on a single per-token line. It is won by confirming the provider carries your models, reading the rate structure at the volume you will actually run, and watching for the point where steady traffic makes a dedicated GPU cheaper than any per-token rate. Start by checking which catalog holds your model, estimate your effective rate at production volume rather than on a test call, and keep the serverless-to-dedicated crossover in view. The cheapest token today is not always the cheapest path once your traffic settles into a pattern.