For new work, use PyTorch. It has the larger ecosystem, the faster release cadence, and almost every model you will want to start from is published in it. Choose TensorFlow when you are maintaining an existing TensorFlow codebase, or when your deployment target has a mature TensorFlow path you do not want to rebuild.
That is the short version, and for most people it is the whole answer. The longer version is more interesting, because the reasons this question used to be hard have mostly stopped applying.
The state of each project
The most useful signal is not benchmarks, which depend on your model, or popularity polls, which lag reality. It is how often each project ships.
Both projects are alive. But one is on a roughly six-to-eight week cycle and the other has shipped one stable release since the start of the year. That gap tells you where new features, hardware support and performance work land first, and it compounds: every new accelerator, attention implementation and quantization format shows up in PyTorch first because that is where the people writing them work.
If you want to check this rather than take our word for it, the release histories are public on PyPI for both packages.
What actually differs
The execution model difference is the one people remember, and it is the one that matters least now. TensorFlow added eager execution years ago and PyTorch added a compiler. Both can be fast, and both can be debugged. Anyone telling you TensorFlow means static graphs and print-statement debugging is describing software that no longer exists.
Keras 3 makes this less of a binary
The framing of this question is out of date in a specific way that is worth understanding before you commit to either side.
Keras is no longer a TensorFlow front end. Keras 3 is a full rewrite that runs on JAX, TensorFlow or PyTorch, with an inference-only OpenVINO backend as well. The same model code runs on any of them, the saved file format carries no backend specialization, and a saved model can be reloaded under a different backend than the one that trained it.
That has two consequences.
If you like the Keras API, it is no longer a reason to choose TensorFlow. You can write Keras and run it on PyTorch. A Keras 3 model can be instantiated as a PyTorch Module, exported as a TensorFlow SavedModel, or used as a stateless JAX function.
If you are on tf.keras, you are on the old thing. The legacy Keras 2 code now lives in a separate tf_keras package which is in maintenance mode, with bug fixes but no new features or performance work. TensorFlow itself resolves Keras to version 3 by default. So saying you use Keras no longer tells anyone which framework you are actually running.
The deployment argument no longer holds
For years the honest case for TensorFlow was deployment: TensorFlow Lite for phones and embedded hardware, TF Serving for production endpoints, TFX for pipelines. Research in PyTorch, ship in TensorFlow. It was real advice.
The on-device half of that argument has been dissolved by Google. TensorFlow Lite is now LiteRT, and the rename came with a change of scope: it takes models authored in PyTorch and JAX as well as TensorFlow. Google has said future feature and performance work goes to LiteRT rather than the old TensorFlow Lite packages.
So the best deployment asset TensorFlow had is now framework-neutral. If your reason for choosing TensorFlow is that you eventually need to run on a phone, that reason has weakened considerably.
TF Serving and TFX are still real and still good, and if your production stack is built on them, that is a legitimate reason to stay. It is just no longer a reason to start there.
When TensorFlow is the right answer
Being clear about this matters more than being enthusiastic about PyTorch.
You have a working TensorFlow codebase. Migration costs weeks and buys you nothing your users can see. Frameworks are not a thing to switch for their own sake.
Your production infrastructure is TF Serving or TFX. Replacing serving infrastructure is a bigger project than replacing a training script, and the parts you would replace it with are not obviously better.
Your team knows it. A team fluent in TensorFlow will ship faster in TensorFlow than in a framework they are learning, and that difference is larger than any framework benchmark.
You are working from TensorFlow reference code. Some domains, particularly in established enterprise and scientific settings, still have their canonical implementations in TensorFlow. Porting introduces bugs you will not find for months.
When PyTorch is the right answer
You are starting something new. The default should be the ecosystem with more of everything, and that is PyTorch.
You work with published models. Nearly everything released now arrives as PyTorch weights first. Starting elsewhere means waiting for a conversion or doing it yourself, and conversions lose fidelity in ways that are hard to detect.
You are doing anything with large language models. The entire serving and fine-tuning stack around them assumes PyTorch. This is not close.
You need current hardware support. New accelerators and new numeric formats land in PyTorch first, because that is where the vendors send their engineers.
What this means for GPU cost
The framework choice affects your hardware bill less than most people expect, and the parts where it does matter are not the parts people argue about.
Neither framework decides your GPU. Memory requirements come from model size, batch size and precision, not from the library wrapping them. Our GPU comparison guide covers the sizing question properly, and training against inference covers why those two workloads want different cards.
The host matters more than the framework. The most common cause of a slow training run is a starved GPU, not a slow one. If your data pipeline cannot feed the card, utilization sawtooths and no framework change fixes it. Our guide to stress testing a GPU covers how to read that.
Where it does matter is multi-node. PyTorch's distributed story is the better-supported one, and the tooling around it assumes PyTorch. If you expect to scale past a single machine, that is a real consideration. Our guide to multi-GPU training with DDP and DeepSpeed covers the practical setup.
And in image size. TensorFlow images tend to be larger, which costs container disk and adds to startup time on a cold worker. It is a small effect, and only worth thinking about at high worker counts.
Running either one on Runpod
Both frameworks run in containers, so both run here. What differs is how much is done for you.
Our official images cover PyTorch, including a cluster-ready variant and an NVIDIA-based one, so a PyTorch pod is a template selection rather than a build.
TensorFlow is not one of our official images. The Hub lists community TensorFlow templates. You can also install TensorFlow into the base image, which is standard Ubuntu with CUDA and Python, or bring your own container. For anything heading to production, a container you control is the safer choice, and it is worth checking when any template you pick was last rebuilt.
Billing is per second with no minimum, so trying a framework on real hardware costs very little. An RTX 4090 is {{gpu:rtx-4090}}/hr on Secure Cloud or {{gpu:rtx-4090:community}}/hr on Community Cloud, and an H100 SXM is {{gpu:h100-sxm}}/hr. Current rates are on the pricing page.
If you are already on PyTorch and want a specific CUDA pairing, our PyTorch and CUDA setup guide covers building the image. If you are weighing a third option, JAX is worth knowing about, particularly for large-scale training.
FAQ
Is PyTorch better than TensorFlow?
For new projects, usually yes, on ecosystem and release activity rather than on any single technical advantage. For an existing TensorFlow codebase with working production infrastructure, no. The better framework is the one your code and your team are already in, unless you have a specific reason to move.
Is TensorFlow dead?
No. It still ships stable releases, still runs enormous production systems, and its serving and pipeline tooling is mature. What has changed is pace: PyTorch shipped six stable releases in the time TensorFlow shipped one. That is a real difference, and it is different from the project being abandoned.
Should I learn PyTorch or TensorFlow first?
PyTorch. More tutorials, more published models to read, and more jobs assume it. If you learn one properly, the second takes days rather than months, because the concepts transfer and only the syntax changes.
Can I use Keras with PyTorch?
Yes. Keras 3 runs on PyTorch, JAX or TensorFlow, and the same model code works on any of them. Saved models carry no backend specialization, so a model trained under one backend loads under another. Note that this applies to Keras 3, not the legacy tf.keras API, which is now a separate maintenance-mode package.
Which is faster, PyTorch or TensorFlow?
It depends on the model, the batch size, the precision and the hardware, far more than on the framework. Both compile to optimized kernels, both support mixed precision, and both are usually bounded by memory bandwidth rather than by framework overhead. Benchmark your own model rather than trusting a general answer, including ours.
Do I need a different GPU for TensorFlow than for PyTorch?
No. Both use CUDA on NVIDIA hardware, and VRAM requirements come from the model and batch size rather than the framework. Pick the card for the workload.
Can I run TensorFlow on Runpod?
Yes. It is not one of our official images, but community templates for it exist in the Hub, and it installs cleanly into the base image or into a container you build yourself.
Can I convert a model between them?
Sometimes, and it is rarely pleasant. ONNX is the usual route and it works for many standard architectures, but custom layers and unusual operations tend to break. If conversion is on your roadmap, test it early with a real model rather than assuming it will work at the end.
Get started
Whichever framework you land on, the fastest way to compare them on your own workload is to run both for an hour. Runpod bills per second with no minimum, our PyTorch templates deploy in about a minute, and TensorFlow installs into the base image. See current pricing or deploy a Pod.
