Model Hosting Compared: Together vs Replicate vs Modal

If you are picking a platform to host open-source models in production, three names come up in every team conversation: Together AI, Replicate, and Modal. They overlap in the middle of the Venn diagram — all three let you run inference on open-source models without owning hardware — but they were designed for different problems and show it clearly once you move past a hello-world request.

This comparison covers what each platform actually does well, where each one breaks down, and how to match the platform to your workload. Prices are from their public pages as of January 2026; rates change, so verify before committing to a budget.

How we evaluated

Our criteria, in order of weight:

1. Cold start latency and throughput — How fast does the first token arrive? What is peak throughput on a sustained load?
2. Pricing structure — Is billing per token, per second, or per request? Which structure favors your traffic pattern?
3. Model selection — What open-source models are available without self-deploying weights?
4. Deployment control — Can you customize the server, add middleware, or run pre/post-processing alongside the model?
5. Operational overhead — What do you have to manage yourself?

Platform overview

Together AI started as a research collective focused on open-source AI infrastructure. Today it operates a serverless inference API that runs most major open-source text, code, and multimodal models. You send a request, you get a response, you pay per token. There is no server to manage. The value proposition is fast inference at a price that is competitive with OpenAI’s API for comparable capabilities, using open-source weights.

Replicate is organized around the concept of a model as a deployable artifact. Developers publish versioned model packages (called “Cogs”), and Replicate handles running them on demand. You pay per second of GPU time, rounded to the request. It gained popularity through its image generation catalog — Stable Diffusion variants, Flux, ControlNet — and its strength is breadth: thousands of community-published models you can call with one API key.

Modal is closer to serverless compute than to a managed API. You write Python, decorate functions with @app.function(gpu="A100"), and Modal schedules them on GPU workers. The billing is the same per-second model as Replicate, but you have full control over the execution environment. Custom CUDA kernels, custom tokenizers, streaming with your own logic layered in — all straightforward. The tradeoff is that you write more code.

Feature comparison

Pricing comparison

These are the rates we verified in January 2026. All assume on-demand usage; reserved capacity changes the calculus significantly.

Together AI’s token-based pricing makes it predictable for text workloads: if your average Llama 3.3 70B request is 1,000 input tokens and 500 output tokens, you pay roughly $0.81 per 1,000 requests. Replicate’s per-second billing on that same model would depend entirely on how fast the server responds — if average latency is 4 seconds, an A100 at $0.0014/sec costs $0.0056 per request, which scales to $5.60 per 1,000 requests. For text, Together AI is usually cheaper. For image generation, the opposite is often true because Together AI has limited image model selection.

Where each platform wins

Together AI: high-volume text inference

Together AI is the right call when your primary workload is text or code inference at scale and you want the simplest possible operational model. No container images, no custom server code, no GPU scheduling. You authenticate, pick a model from their catalog (Llama, Mistral, Qwen, DeepSeek, Gemma, and others), and send requests. Rate limits are generous at the paid tier, and their throughput on popular models is fast — we measured around 65 tokens/second on Llama 3.3 70B under moderate load.

The constraint is control. If you need to modify how the model is called, add a guardrail layer, or use a model not in their catalog, you are stuck. Together AI does not let you bring arbitrary weights or modify the inference server.

Best for: Product teams running LLM features at scale who want managed infrastructure, token-based billing, and the latest open-source text models without DevOps.

Replicate: image and video workloads

Replicate’s catalog of image and video models is unmatched. You can call Flux 1.1 Pro, SDXL, Stable Video Diffusion, and dozens of ControlNet variants through the same API key. Per-image pricing is predictable, and many of these community models are actively maintained by their original authors.

The downside is that cold starts on less-popular models can be brutal — 20 to 30 seconds on a model that has not run recently. If you are calling a community model during off-hours, you will wait. For high-traffic production use, Replicate offers dedicated deployments at higher per-second rates, which eliminates cold starts but removes the pay-per-prediction advantage.

Best for: Teams building image or video generation features who need wide model selection and do not want to self-host Stable Diffusion infrastructure.

Modal: custom logic and research workloads

Modal is where you go when you need the model to be part of a larger compute pipeline. Batch embedding jobs, custom retrieval-augmented generation pipelines with preprocessing, multi-step inference chains, or experiments with custom kernels — all of these are natural Modal use cases. You write Python, import modal, and the platform handles container building, GPU allocation, and scaling.

The cost of this flexibility is real: you will spend time writing and maintaining deployment code that Together AI and Replicate abstract away. Modal also has no managed model catalog, so you are responsible for pulling weights, loading them efficiently, and handling model state.

Best for: ML engineers who need compute infrastructure rather than a managed API, or teams with non-standard inference requirements that do not fit a managed platform.

Honest verdict

If you are running a production LLM feature on text models, start with Together AI. The token-based pricing is transparent, the operational burden is minimal, and their model catalog covers most open-source text needs. The main risk is vendor lock-in to their model selection and the absence of customization options.

If your workload involves image or video generation, Replicate is the pragmatic choice. The cold start problem is real and matters more as your traffic grows, but for most teams the pay-per-prediction model and the breadth of available models outweigh the latency variance.

If you have an ML engineer on the team and your use case does not fit the other two, Modal gives you the most flexibility. The per-second GPU pricing is competitive with Replicate, and you get full Python control. Expect to invest more in deployment code upfront.

One thing all three have in common: none of them will hold the same prices a year from now. The open-source model hosting market is compressing margins fast. The architectural patterns you build on top of these platforms will outlast the specific pricing structures, so keep your calling code behind an abstraction layer and be ready to switch.