Softwr

Machine Learning · head to head

Milvus vs TensorBoard

Milvus logo

Milvus

Machine Learning

Open-source vector database for scalable similarity search

From
Free
Rated
-
Full reviewWebsite
T

TensorBoard

Machine Learning

Local visualisation for training runs, reading event files written to a directory

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Milvus vector dimensions are capped at 32,768; TensorBoard there is no authentication of any kind, so putting it on a shared host exposes every run, every metric and every logged sample image to anyone who can reach the port, and adding access control means building and maintaining a reverse proxy.
  • They diverge on capability: Milvus covers Billion-scale vectors, TensorBoard covers Scalar dashboards.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Milvus and TensorBoard actually diverge.

Attributes where Milvus and TensorBoard differ
AttributeMilvusTensorBoard
Pricing modelfreemiumopen-source
PlatformsLinux, Mac, Windows, WebWeb
Founded2017Unknown

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Machine Learning).

What each one covers

Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.

Only in Milvus

  • Billion-scale vectors
  • Multiple index types
  • GPU acceleration
  • Hybrid search
  • Data partitioning
  • PyTorch
  • TensorFlow
  • Hugging Face

Only in TensorBoard

  • Scalar dashboards
  • Run comparison
  • Graph visualisation
  • Histograms and distributions
  • Embedding projector
  • Image, audio and text panels
  • Hyperparameter view
  • Profiler

What people use each for

The jobs each tool is most often brought in to do.

Milvus

  • Self hosting a vector database for semantic searchnot TensorBoard
  • Storing and querying embeddings for retrieval augmented generationnot TensorBoard
  • Similarity search over images, audio or text at scalenot TensorBoard

TensorBoard

  • Watching a training run in progress on a workstation or a remote box, to decide whether to stop it earlynot Milvus
  • Diagnosing why a model is not learning, by looking at gradient and weight histograms rather than only the loss curvenot Milvus
  • Profiling a slow training loop to find out whether the bottleneck is the data pipeline or the acceleratornot Milvus
  • Working in an environment with no outbound network access, where a hosted tracking service is not an optionnot Milvus

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Milvus

  • Vector dimensions are capped at 32,768
  • A collection is limited to 64 fields, 1,024 partitions and 16 shards
  • Only 1 index is allowed per field
  • Search returns at most 16,384 vectors as top-k, and nq is capped at 16,384
  • Input and output per RPC is capped at 64 MB for insert, search and query
  • VARCHAR values are limited to 65,535 characters
  • Data loaded into query nodes cannot exceed 90% of available memory
  • An instance supports at most 65,536 collections

TensorBoard

  • There is no authentication of any kind, so putting it on a shared host exposes every run, every metric and every logged sample image to anyone who can reach the port, and adding access control means building and maintaining a reverse proxy.
  • Event files grow without limit and the interface loads runs into memory, so a directory holding hundreds of runs or a script logging scalars every step becomes slow to start and unpleasant to navigate well before the disk fills.
  • It shows only what the training loop chose to write, so nothing connects a curve back to the code commit, the data set version or the environment unless the engineer logged those explicitly, which means the reproducibility problem is left entirely to you.
  • TensorBoard.dev, the hosted service for sharing a run by link, was shut down at the end of 2023, so results now travel between colleagues as screenshots or through a deployment somebody on the team has to operate.
  • Comparing runs is done by ticking boxes in a run list, which is fine for ten runs and useless for a thousand, and that threshold is precisely where teams start paying for MLflow, Weights and Biases or Neptune instead.

Pricing, plan by plan

Milvus

Free
  • Open SourceFree
    • Full features
    • Self-hosted
    • Community support
  • Zilliz CloudFree
    • Managed service
    • Free tier available

TensorBoard

Free

No published plan breakdown. See the TensorBoard review.

Which should you pick?

Choose Milvus if

  • You need billion-scale vectors.
  • You want to start without paying.
  • You work on Linux, Mac, Windows, Web.
  • You also want multiple index types.
Milvus review

Choose TensorBoard if

  • You need scalar dashboards.
  • You want to start without paying.
  • You also want run comparison.
TensorBoard review

Questions people ask

Is Milvus or TensorBoard better?
Neither clearly leads. Milvus starts at Free and TensorBoard at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Milvus or TensorBoard?
Milvus starts at Free and TensorBoard at Free.
Does Milvus or TensorBoard run on more platforms?
Milvus runs on Linux, Mac, Windows, Web. TensorBoard runs on Web.
Can I use Milvus for free?
Both have a free tier, so you can try either at no cost before committing.
What is Milvus best used for?
Milvus is most often used for self hosting a vector database for semantic search, storing and querying embeddings for retrieval augmented generation, similarity search over images, audio or text at scale. Of those, self hosting a vector database for semantic search and storing and querying embeddings for retrieval augmented generation are not what TensorBoard is typically brought in for.
What can Milvus do that TensorBoard cannot?
Milvus covers Billion-scale vectors, Multiple index types, GPU acceleration, Hybrid search. TensorBoard covers Scalar dashboards, Run comparison, Graph visualisation, Histograms and distributions.

Answered from the vendors’ own pages

Milvus: How much does Milvus cost?

Milvus is open-source and free to use and modify. The self-hosted version has no licensing cost. Zilliz Cloud (the managed SaaS version) does not publish pricing on the website.

Source
TensorBoard: Does it work with PyTorch?

Yes. PyTorch includes a SummaryWriter that emits the same event file format, and Lightning wires it up by default. Nothing about the tool requires TensorFlow at run time.

Milvus: Is there a free or open-source version of Milvus?

Yes, Milvus is fully open-source and available for free. Milvus Lite is a lightweight option for learning and prototyping that can be installed via pip.

Source
TensorBoard: Do I have to install TensorFlow to use it?

No. The tensorboard package installs on its own. Some plugins expect TensorFlow to be present, but the scalar, histogram and image dashboards do not.

Milvus: Does Milvus offer a managed cloud service?

Yes, Zilliz Cloud is a fully managed Milvus cloud offering with serverless and dedicated cluster options. Pricing must be requested from the company as it is not listed on the public website.

Source
TensorBoard: Is it an experiment tracker?

No, and treating it as one is the common mistake. It visualises whatever a run wrote to disk. It does not store hyperparameters, code versions, artefacts or results in a way that survives someone deleting the log directory.

TensorBoard: How do I share a dashboard with a colleague?

Host it yourself behind your own authentication, or send screenshots. The hosted sharing service was retired at the end of 2023.

TensorBoard: What does it cost?

Nothing. It is Apache 2.0 licensed and runs on your own machine.

Share

Related pages

More on Milvus

More on TensorBoard

Keep looking

Other head to heads