Replicate vs Together vs Fireworks vs Modal: inference
Four platforms for running open and custom models compared on what you deploy, how you pay and when to switch to dedicated capacity.
Teams that want open models, fine-tuned models or custom pipelines without running their own GPUs usually look at Replicate, Together AI, Fireworks AI and Modal. They overlap but sit at different levels of abstraction: Replicate and the serverless APIs of Together and Fireworks let you call ready-made models, while Modal lets you run your own code on serverless GPUs.
The short answer
- Choose Replicate to call a huge catalogue of community and proprietary models, especially for images, video and audio, with minimal setup.
- Choose Together AI for open language and multimodal models at scale, from serverless to dedicated endpoints and GPU clusters.
- Choose Fireworks AI for fast serverless inference of large open models and fine-tuning served at base-model prices.
- Choose Modal to run your own Python code, pipelines, sandboxes or training jobs on serverless GPUs billed per second.
Side by side
| Replicate | Together AI | Fireworks AI | Modal | |
|---|---|---|---|---|
| What you run | Catalogue models or your packaged model | Hosted open models; fine-tunes; clusters | Hosted open models; fine-tunes; dedicated GPUs | Your own code in containers |
| Billing | Per second of hardware or per input/output | Per token serverless; GPU hours dedicated | Per token serverless; per GPU second dedicated | Per second of CPU and GPU |
| Fine-tuning | Yes, on your data | Yes | SFT and DPO per million training tokens | Run any training framework yourself |
| Scaling up | Custom deployments | Dedicated endpoints, provisioned throughput, batch | Dedicated deployments | Autoscaling containers, sandboxes |
| Free start | Pay as you go | Pay as you go | Prepaid credits | US$30 a month of free compute on Starter |
Models as a service or compute as a service?
If you only need to call a model, the serverless APIs of Replicate, Together and Fireworks are the shortest path: no containers, just an API call and a per-use bill. Modal is different: you define containers and GPUs in Python and run whatever you like, from inference servers to batch jobs and code sandboxes for agents. That flexibility suits teams with custom pipelines, at the cost of writing more code.
When dedicated capacity pays off
All four let you move beyond pay-per-call when traffic becomes steady. Together offers dedicated endpoints and committed throughput, Fireworks dedicated GPU deployments billed per second, Replicate custom deployments, and Modal autoscaling on reserved hardware types. Dedicated capacity usually only saves money at sustained volume, so measure real traffic first.
How to decide
- Decide whether you need hosted models, your own code, or both.
- Benchmark the same model and prompts on two platforms for latency and output quality.
- Calculate cost per thousand requests at your real input and output sizes.
- Check licences of community and open models before commercial use.
- Plan a path from serverless to dedicated capacity as usage grows.
Tools to explore
Sources
Official vendor pages consulted for this analysis in October 2026. Plans, limits and features change: check the vendor’s page before you buy.
- Replicate: official site replicate.com
- Replicate: pricing replicate.com
- Together AI: official site together.ai
- Together AI: pricing together.ai
- Fireworks AI: official site fireworks.ai
- Fireworks AI: pricing fireworks.ai
- Modal: official site modal.com
- Modal: pricing modal.com