The main alternatives for serving an AI model in production come in three groups: hosted APIs for open models someone else already runs, serverless GPU platforms where you ship your own code or weights, and open-source serving frameworks you run yourself. Work out first whether your model is off-the-shelf or your own, because that rules options in or out.
Is the model you're putting into production one somebody else built, or one you've trained or changed yourself? If you're looking past Baseten, that single question will cut your shortlist in half before you've read a feature page.
Small teams usually move for one of three reasons. They want a simple API for a popular open model, they want to run their own code without managing servers, or they want the serving layer on their own infrastructure. The nine options below are in no particular order, with a quick table first and the detail after.
Nine model serving options at a glance
| Tool | Kind of platform | Good for |
|---|---|---|
| Beam | Serverless functions and endpoints | Functions, queues and endpoints billed only while code runs |
| BentoML | Inference platform and open-source framework | Deploying any model, hosted or with an open-source framework |
| Cerebrium | Serverless GPUs for your own model | Real-time voice, video and LLM apps without Kubernetes |
| DeepInfra | Hosted open-model API | Production-ready hosted models with zero data retention |
| fal | Generative media models and serverless GPUs | Image, video and audio models, or deploying your own |
| Fireworks AI | Hosted open-model API | Calling popular open models and shaping them to your needs |
| Groq | Fast hosted inference | Speed on the open models it already hosts |
| KServe | Kubernetes model serving, open source | Self-hosted serving for teams already on Kubernetes |
| Ray Serve | Open-source serving library | Programmable serving on your own cloud or cluster |
Where each serving platform fits
Alphabetical, in no particular order. Claims about speed and scale are each vendor's own.
Beam
Best for: Developers who want VMs, sandboxes, task queues, functions and endpoints on bare-metal servers, billed only while code runs, with an open-source project behind it.
BentoML
Best for: Teams that want a hosted inference platform with an open model catalogue, and an open-source framework for custom inference when they outgrow it.
Cerebrium
Best for: Real-time apps such as voice agents and video models that need serverless GPUs billed by the second, with fast cold starts claimed on the page.
DeepInfra
Best for: Teams who want hosted machine-learning models that are easy to deploy, with zero retention, secure inference and the vendor's own data centres (vendor claim).
fal
Best for: Builders of image, video and audio features who want to call ready-made media models, or deploy and train their own on serverless GPUs.
Fireworks AI
Best for: Product teams that want to call the latest open models through an API and, by the vendor's account, turn them into specialised models of their own.
Groq
Best for: Developers who care most about fast responses from the models on its own platform, with infrastructure, inference and control in one place (vendor wording).
KServe
Best for: Kubernetes teams serving predictive and generative models who want scale to zero, GPU acceleration and support for several frameworks, all open source.
Ray Serve
Best for: Engineering teams that want framework-agnostic serving with model composition, streaming and request batching, run on their own infrastructure or cloud.
Off-the-shelf model or one you built?
This is the part teams skip. A platform that serves open-source models and a platform that serves your custom model sound alike on a home page. They're not the same job.
Fireworks AI, Groq and DeepInfra are hosts for models they already run. You pick a model from their range and call their API. That's quick and tidy if a popular open model does what you need. The first of the three also talks about turning open models into your own specialised versions, so ask how far that goes before you assume it'll take something you trained elsewhere.
If you've fine-tuned a model, changed its architecture or built something from scratch, a catalogue host may not take it at all. You'll need a platform that runs your own code or weights, and that's where the rest of this list comes in.
At the far end sit Ray Serve and KServe. Both are open source, and both put the operations work back on you: the first runs on your own infrastructure or cloud, and the second assumes you already run Kubernetes. They're tools for teams with an engineer who's happy to own scaling, updates and being on call, not for a founder who wants an endpoint by Friday.
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So write one line before you compare anything else: is my model off-the-shelf, or is it mine? Every other choice follows from that.
Hosted catalogue, serverless code or a framework you run
Hosted model APIs. The three catalogue hosts above. Choose one of these when a well-known open model does the job and you'd rather not run anything at all.
Serverless platforms for your own code. Cerebrium deploys voice agents, video models and LLMs on serverless GPUs, with no Kubernetes to manage. Beam gives you functions, endpoints, task queues and sandboxes on bare-metal servers, billed only while your code runs. fal is built for generative media, with image, video and audio models and serverless deployment, so it's strongest for pictures and sound rather than text models.
Frameworks you can run yourself or have hosted. BentoML sits across two groups: an open-source framework for custom inference, and a hosted inference platform with an open model catalogue. The two open-source frameworks from the previous section belong here as well, for teams that want the whole serving layer in their own hands.
My advice for a small team shipping a model
Match the platform to the model, then to the team. If an open model works and you've no infrastructure person, start with a hosted API and keep your prompts and evaluation set portable. If the model is yours and nobody wants to run servers, try the serverless platforms. If you've an engineer who already runs clusters, a self-run framework gives you the most control, in exchange for their time.
Before you move real traffic, send the same set of real requests through your top two and compare speed, errors and output side by side. My list of model deployment platforms covers the wider MLOps side, and my guide to AI inference platforms looks at hosted model APIs in more depth.
Five questions for the sales call
- Can you run my own weights or code, or only models from your range?
- What happens to response times when traffic spikes, and what's a cold start like?
- Where does my data go, and is any of it kept after a request?
- Can I move my model and its settings to another platform without rewriting them?
- Who do I talk to when an endpoint fails in the middle of the night?
A vendor who answers the first question vaguely is telling you something useful.
Do you make a model serving or inference platform? Tool makers can ask to be considered for a featured place on this list.
Want your tool on this list? Featured spots and reviews here are paid partnerships. Here's how to partner with me.
Frequently asked questions
What is model inference?
Inference is the moment a trained model takes an input, such as a prompt or an image, and returns an answer. Serving platforms run that step for you at scale.
Can I host a fine-tuned model on a hosted model API?
Sometimes. Some hosts accept custom or fine-tuned versions of models they support, others only run their own range. Ask before you plan a migration around it.
What is serverless GPU hosting?
It means you deploy your code and the platform starts GPUs when requests arrive and stops them when they end, so you don't manage machines yourself.
Do I need Kubernetes to serve a model?
No. Hosted APIs and serverless platforms hide it from you. Some open-source serving tools are built on Kubernetes, so they're a better fit for teams that already run it.
What's a cold start?
It's the delay when a platform has to load your model onto a GPU before answering the first request. It matters most for voice and other real-time apps.
Should a small team self-host model serving?
Only if someone can own it. Self-hosting gives control over data and setup, but scaling, updates and fixes at night all land on your team.
