A tensor API becomes useful when another developer can send data without guessing how the model will interpret it. The difficult part is rarely transporting a list of numbers. It is agreeing on what those numbers mean, which axes they occupy, and what happens when a request does not match the model. Shape, data type, and batching belong in that agreement from the beginning.

This guide develops a small, illustrative classification interface around those decisions. The examples describe a contract you could implement in your own application; they do not represent a running TensorAPI.com service. For the broader architectural context, start with the Tensor API design overview.

Give every axis a meaning

A tensor is an array organized across zero or more dimensions. Its shape records the length of each dimension. A vector containing six features has shape [6]; four such feature vectors can form a tensor with shape [4, 6]. The first axis might represent examples and the second features, but those meanings come from the contract. The numbers alone do not establish them.

The official PyTorch tensor reference describes tensors as multidimensional collections with a single data type and documents attributes such as dtype, device, and layout. Those attributes help explain why an API needs more than a JSON array. The transport representation and the runtime tensor are related objects with different responsibilities.

Write axis descriptions beside the shape. For an image model, [batch, channels, height, width] and [batch, height, width, channels] are different contracts. An input can contain exactly the expected number of values and still be wrong because its axes were arranged differently. A schema should make that mistake detectable before inference.

Build a contract around one clear task

Suppose an internal classifier consumes six normalized measurements and returns three class scores per example. Begin with the task and feature definitions, then describe the tensor. Avoid a universal endpoint that accepts arbitrary shapes unless the underlying operation really supports arbitrary shapes. A narrow contract gives clients a smaller set of valid states to understand.

{
  "schema_version": "1",
  "input": {
    "dtype": "float32",
    "shape": [2, 6],
    "axes": ["example", "feature"],
    "values": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6,
               0.6, 0.5, 0.4, 0.3, 0.2, 0.1]
  },
  "feature_schema": "sensor-summary-v1"
}

This illustrative payload uses a flattened array in row order. That ordering must be specified explicitly. The product of the dimensions is twelve, matching the number of supplied values. The feature schema identifies the meaning and order of the six columns. A client must not silently swap two measurements just because both happen to be floating point numbers.

Keep the output similarly explicit: its shape, the ordered label vocabulary, and a model or processing revision should travel together. A score in column two is meaningless if one release calls that column “review” and another calls it “reject.” Versioning the label mapping protects downstream consumers from a structurally valid but semantically incompatible change.

Treat dtype as a deliberate conversion policy

JSON numbers do not carry a declaration such as float32. The server must validate and convert them into its chosen runtime representation. Document accepted input types, range checks, and rounding behavior. If the contract accepts integers for a floating point feature, make that an intentional conversion instead of an accidental consequence of a parser.

Do not assume that changing precision is only a storage decision. A smaller representation can alter values, and a model or operator may require particular input types. Evaluate any conversion with representative inputs and outputs. For identifiers such as token IDs, use an integer representation with validated bounds; converting an arbitrary decimal into an identifier by rounding would hide an upstream error.

Separate public dtype choices from execution details where possible. Clients often need to know the accepted numerical contract without choosing a specific accelerator or memory layout. Exposing a device name prematurely couples the interface to infrastructure that may change while the task remains the same.

Validate before allocating expensive resources

Validation should proceed from cheap structural checks to more expensive semantic checks. First require the expected fields and supported schema version. Then check that dimensions are integers, that rank and axis sizes satisfy the task, and that their product agrees with the value count. Apply explicit limits before creating a large runtime tensor.

  • Set a maximum payload size and maximum number of examples per request.
  • Reject negative dimensions and decide whether empty batches are valid.
  • Require finite numerical values when the model contract cannot handle missing or infinite values.
  • Validate categorical identifiers against the declared vocabulary.
  • Check feature ranges after applying the documented preprocessing rules.

Return errors that identify the violated rule and the relevant field. “Expected six features per example; received five” helps a client fix the request. A raw accelerator exception does not. Include a request identifier for diagnostics, while keeping internal paths and the full submitted payload out of routine error responses.

Make broadcasting an internal decision

Numerical libraries can combine certain differently shaped tensors through broadcasting. In the familiar trailing-axis rule, dimensions are compatible when their sizes match, one size is one, or the dimension is absent. That convenience is valuable inside an implementation, but it can conceal a client mistake at an API boundary.

For example, a feature vector shaped [6] may be mathematically compatible with a batch shaped [4, 6]. Whether it should apply to all four examples is a product decision. Require the batch axis unless a documented operation intentionally accepts a shared vector. Rejecting ambiguous input is easier to support than reverse engineering which broadcasting rule produced a surprising result.

Batch compatible work and preserve identity

Batching groups examples so that an implementation can process them together. It also introduces ordering, size, and latency decisions. Preserve a stable identifier for each example, particularly when requests can complete asynchronously or when internal scheduling changes their order. A response should remain joinable to its input without relying on an undocumented position.

Do not combine incompatible model revisions or feature schemas simply because their tensors have matching dimensions. Equal shapes establish numerical compatibility, not equivalent meaning. Partition work by the attributes that affect interpretation before grouping it by length or size.

For variable-length sequences, decide how padding and masks are produced and who owns truncation. Record an effective length where it helps consumers detect discarded input. The guide from prompt tokens to LLM tensors follows that issue through a language-model pipeline. The same discipline applies when padding audio windows or aligning variable-size records.

Choose batch limits using measurements from representative workloads. Larger batches can reduce per-example overhead, but they may also wait longer in a queue and consume more memory. Compare total completion time, queue delay, and failure rate before changing a default. A throughput gain does not automatically improve an interactive user's experience.

Choose transport after the contract is stable

A small JSON payload is easy to inspect and integrate. Large dense tensors may justify a binary encoding, but the same shape, dtype, ordering, and version information still has to exist. Specify byte order and exact encoding for binary values. Keep metadata and payload integrity checks connected so that a stale descriptor cannot be paired with a different buffer.

Build a few durable fixtures: one ordinary batch, one single example, one maximum permitted input, and several deliberately invalid requests. Include a feature-order mistake that passes basic shape validation. Test the response mapping as well as inference success, because a correct model result attached to the wrong example is still an incorrect API result.

Conclusion: make interpretation reproducible

A reliable tensor interface explains how values become a model input and how outputs retain their meaning. Name axes, version feature and label definitions, validate numerical conversions, and make batching behavior observable. Once these decisions are explicit, optimizing transport or execution becomes a contained engineering task. Continue with the AI tensor workflow guide to connect the contract to preprocessing, model execution, and downstream validation.