We keep production TensorFlow models running securely, ship them to phones with LiteRT, and help you move to Keras 3 or PyTorch when the time is right, with no surprises.
Tensors flow through a graph of operations: that is where the name comes from.
From Google's TensorFlow 2.21 announcement (March 2026): core TensorFlow is maintained, while LiteRT and Keras 3 are where new development happens.
Security and critical bug fixes; minor releases for new Python and dependencies.
Maintained for existing production pipelines.
Maintained; no major new features expected.
Actively developed: faster GPU, new NPU acceleration, PyTorch and JAX conversion.
Actively developed; runs on TensorFlow, JAX or PyTorch.
Google recommends Keras 3, JAX or PyTorch.
Answer two or three questions to see what we would usually recommend for your model.
Quantisation stores weights in fewer bits, so a model shrinks dramatically. LiteRT then runs it on the phone's GPU or NPU: Google reports about 1.4x faster GPU performance than TFLite.
Model size by number format (relative to FP32)
Keras 3 runs on TensorFlow, JAX or PyTorch. Switch the backend and the same model trains on a different engine: a gentle path off TensorFlow-only code.
Many companies run TensorFlow Extended (TFX) pipelines that retrain and redeploy models automatically. We maintain, monitor and modernise them.
We handle each model on its own merits, so production never breaks.
List models, TensorFlow and Python versions, custom ops and where each model runs.
Upgrade to TensorFlow 2.21, pin dependencies and add accuracy monitoring.
Per model: keep, move to Keras 3, port to PyTorch, or convert to LiteRT.
Rebuild one model at a time and compare accuracy and latency against the original.
Retraining pipelines, monitoring and MLOps for the long term.
Models that solve a business problem and keep running.
We are framework-neutral and recommend what fits each model.
| Option | Strengths | When we choose it |
|---|---|---|
| TensorFlow | Mature production tooling: TF Serving, TFX, LiteRT | Existing TF systems, on-device deployment |
| PyTorch | Default for new research and LLMs, huge model ecosystem | Most new deep learning projects |
| Keras 3 | One high-level API on TensorFlow, JAX or PyTorch | Teams wanting backend freedom |
| Hosted model APIs | No training needed | When a general model is good enough |
| Databricks | Data and ML platform at scale | Large data teams |
Straight answers to what clients ask us most.
Yes. TensorFlow 2.21 was released in March 2026, and Google continues to ship security fixes, critical bug fixes and minor releases for new Python versions. Active feature development has shifted to LiteRT and Keras 3, and Google recommends Keras 3, JAX or PyTorch for new generative AI work.
Not necessarily. Models that work well in production can stay on TensorFlow with proper maintenance. We recommend migration when you need major new features, large generative models, or are starting a new project.
LiteRT is the new name for TensorFlow Lite, Google's runtime for running models on phones and edge devices. With TensorFlow 2.21 it gained faster GPU performance (about 1.4x versus TFLite), new NPU acceleration, and first-class conversion from PyTorch and JAX.
Keras 3 is a high-level deep learning API that can run on TensorFlow, JAX or PyTorch. Code written in Keras can switch backend with a configuration change, which makes it a smooth bridge away from TensorFlow-only code.
Yes. TF 1.x code with sessions and graphs usually needs a rewrite; we rebuild it in Keras 3 or PyTorch and verify that accuracy matches before switching over.
Through quantisation (for example FP32 to INT8 or INT4), pruning and distillation, then conversion to LiteRT. Model size can drop to a quarter or less, usually with small accuracy loss that we measure.
Yes. We keep existing pipelines running, patched and monitored, and modernise them gradually where it makes sense.
It depends on the number of models and their complexity. We start with a short audit and give a fixed price per milestone.
Tell us what you want to build, your data and integrations, and your timeline. We reply within 4 business hours with honest advice.