Check usage per request

Requests that consume read units or embedding tokens return a usage field alongside their results. Reading it tells you what a single request cost, so you can compare approaches before running them at scale.

Read units

Query, fetch, and list by ID requests return a usage field with the read unit consumption of that request.

While Pinecone tracks read unit usage with decimal precision, the Pinecone API and SDKs round these values up to the nearest whole number in query, fetch, list, and full-text search responses. For example, if a query uses 0.45 read units, the API and SDKs will report it as 1 read unit.For precise read unit reporting, see index-level metrics or the organization-wide Usage dashboard.

Example query request:

from pinecone.grpc import PineconeGRPC as Pinecone

pc = Pinecone(api_key="YOUR_API_KEY")
index = pc.Index("example-index")

response = index.query(
    vector=[0.22,0.43,0.16,1,...], 
    namespace='example-namespace', 
    top_k=3,
    include_values=False,
    include_metadata=False
)

print(response)

import { Pinecone } from '@pinecone-database/pinecone'

const pc = new Pinecone({ apiKey: "YOUR_API_KEY" })
const index = pc.index("example-index")

const queryResponse = await index.namespace('example-namespace').query({
    vector: [0.22,0.43,0.16,1,...],
    topK: 3,
    includeValues: false,
    includeMetadata: false,
});

console.log(queryResponse);

import io.pinecone.clients.Index;
import io.pinecone.configs.PineconeConfig;
import io.pinecone.configs.PineconeConnection;
import io.pinecone.unsigned_indices_model.QueryResponseWithUnsignedIndices;

import java.util.Arrays;
import java.util.List;

public class QueryByVector {
    public static void main(String[] args) {
        PineconeConfig config = new PineconeConfig("YOUR_API_KEY");
        // To get the unique host for an index,
        // see https://docs.pinecone.io/guides/manage-data/target-an-index
        config.setHost("INDEX_HOST");
        PineconeConnection connection = new PineconeConnection(config);
        Index index = new Index(config, connection, "example-index");

        List<Float> query = Arrays.asList(0.22f,0.43f,0.16f,1f,...);
        QueryResponseWithUnsignedIndices queryResponse = index.query(3, query, null, null, null, "example-namespace", null, false, false);
        System.out.println(queryResponse);
    }
}

package main

import (
	"context"
	"encoding/json"
	"fmt"
	"log"

	"github.com/pinecone-io/go-pinecone/v4/pinecone"
)

func prettifyStruct(obj interface{}) string {
	bytes, _ := json.MarshalIndent(obj, "", "  ")
	return string(bytes)
}

func main() {
    ctx := context.Background()

    pc, err := pinecone.NewClient(pinecone.NewClientParams{
        ApiKey: "YOUR_API_KEY",
    })
    if err != nil {
        log.Fatalf("Failed to create Client: %v", err)
    }

	// To get the unique host for an index,
	// see https://docs.pinecone.io/guides/manage-data/target-an-index
	idxConnection, err := pc.Index(pinecone.NewIndexConnParams{Host: "INDEX_HOST", Namespace: "example-namespace"})
	if err != nil {
		log.Fatalf("Failed to create IndexConnection for Host: %v", err)
	}

	queryVector := []float32{0.22, 0.43, 0.16, 1, ...}

	res, err := idxConnection.QueryByVectorValues(ctx, &pinecone.QueryByVectorValuesRequest{
		Vector:        queryVector,
		TopK:          3,
		IncludeValues: false,
	})
	if err != nil {
		log.Fatalf("Error encountered when querying by vector: %v", err)
	} else {
		fmt.Printf(prettifyStruct(res))
	}

The response looks like this:

{'matches': [{'id': 'record_193027', 'score': 0.00405937387, 'values': []},
             {'id': 'record_137452', 'score': 0.00405937387, 'values': []},
             {'id': 'record_132264', 'score': 0.00405937387, 'values': []}],
 'namespace': 'example-namespace',
 'usage': {'read_units': 1}}

{
  matches: [
    {
      id: 'record_186225',
      score: 0.00405937387,
      values: [],
      sparseValues: undefined,
      metadata: undefined
    },
    {
      id: 'record_164994',
      score: 0.00405937387,
      values: [],
      sparseValues: undefined,
      metadata: undefined
    },
    {
      id: 'record_186333',
      score: 0.00405937387,
      values: [],
      sparseValues: undefined,
      metadata: undefined
    }
  ],
  namespace: 'example-namespace',
  usage: { readUnits: 1 }
}

class QueryResponseWithUnsignedIndices {
    matches: [ScoredVectorWithUnsignedIndices {
        score: 0.004059374
        id: record_170370
        values: []
        metadata: 
        sparseValuesWithUnsignedIndices: SparseValuesWithUnsignedIndices {
            indicesWithUnsigned32Int: []
            values: []
        }
    }, ScoredVectorWithUnsignedIndices {
        score: 0.004059374
        id: record_107423
        values: []
        metadata: 
        sparseValuesWithUnsignedIndices: SparseValuesWithUnsignedIndices {
            indicesWithUnsigned32Int: []
            values: []
        }
    }, ScoredVectorWithUnsignedIndices {
        score: 0.004059374
        id: record_171426
        values: []
        metadata: 
        sparseValuesWithUnsignedIndices: SparseValuesWithUnsignedIndices {
            indicesWithUnsigned32Int: []
            values: []
        }
    }]
    namespace: example-index
    usage: read_units: 1
}

{
  "matches": [
    {
      "vector": {
        "id": "record_193027"
      },
      "score": 0.004059374
    },
    {
      "vector": {
        "id": "record_137452"
      },
      "score": 0.004059374
    },
    {
      "vector": {
        "id": "record_132264"
      },
      "score": 0.004059374
    }
  ],
  "usage": {
    "read_units": 1
  },
  "namespace": "example-index"
}

For a worked example of inspecting read costs, see the read units notebook.

Embedding tokens

Requests to one of Pinecone’s hosted embedding models, either directly via the Generate vectors operation or automatically when upserting or querying an index with integrated embedding, return a usage field with the total tokens generated. For example, this request uses the multilingual-e5-large model to embed sentences related to the word “apple”:

# Import the Pinecone library
from pinecone.grpc import PineconeGRPC as Pinecone
from pinecone import ServerlessSpec
import time

# Initialize a Pinecone client with your API key
pc = Pinecone(api_key="YOUR_API_KEY")

# Define a sample dataset where each item has a unique ID and piece of text
data = [
    {"id": "vec1", "text": "Apple is a popular fruit known for its sweetness and crisp texture."},
    {"id": "vec2", "text": "The tech company Apple is known for its innovative products like the iPhone."},
    {"id": "vec3", "text": "Many people enjoy eating apples as a healthy snack."},
    {"id": "vec4", "text": "Apple Inc. has revolutionized the tech industry with its sleek designs and user-friendly interfaces."},
    {"id": "vec5", "text": "An apple a day keeps the doctor away, as the saying goes."},
    {"id": "vec6", "text": "Apple Computer Company was founded on April 1, 1976, by Steve Jobs, Steve Wozniak, and Ronald Wayne as a partnership."}
]

# Convert the text into numerical vectors that Pinecone can index
embeddings = pc.inference.embed(
    model="llama-text-embed-v2",
    inputs=[d['text'] for d in data],
    parameters={"input_type": "passage", "truncate": "END"}
)

print(embeddings)

// Import the Pinecone library
import { Pinecone } from '@pinecone-database/pinecone';

// Initialize a Pinecone client with your API key
const pc = new Pinecone({ apiKey: 'YOUR_API_KEY' });

// Define a sample dataset where each item has a unique ID and piece of text
const data = [
  { id: 'vec1', text: 'Apple is a popular fruit known for its sweetness and crisp texture.' },
  { id: 'vec2', text: 'The tech company Apple is known for its innovative products like the iPhone.' },
  { id: 'vec3', text: 'Many people enjoy eating apples as a healthy snack.' },
  { id: 'vec4', text: 'Apple Inc. has revolutionized the tech industry with its sleek designs and user-friendly interfaces.' },
  { id: 'vec5', text: 'An apple a day keeps the doctor away, as the saying goes.' },
  { id: 'vec6', text: 'Apple Computer Company was founded on April 1, 1976, by Steve Jobs, Steve Wozniak, and Ronald Wayne as a partnership.' }
];

// Convert the text into numerical vectors that Pinecone can index
const model = 'llama-text-embed-v2';

const embeddings = await pc.inference.embed(
  model,
  data.map(d => d.text),
  { inputType: 'passage', truncate: 'END' }
);

console.log(embeddings);

// Import the required classes
import io.pinecone.clients.Index;
import io.pinecone.clients.Inference;
import io.pinecone.clients.Pinecone;
import org.openapitools.inference.client.ApiException;
import org.openapitools.inference.client.model.Embedding;
import org.openapitools.inference.client.model.EmbeddingsList;

import java.math.BigDecimal;
import java.util.*;
import java.util.stream.Collectors;

public class GenerateEmbeddings {
    public static void main(String[] args) throws ApiException {
        // Initialize a Pinecone client with your API key
        Pinecone pc = new Pinecone.Builder("YOUR_API_KEY").build();
        Inference inference = pc.getInferenceClient();

        // Prepare input sentences to be embedded
        List<DataObject> data = Arrays.asList(
            new DataObject("vec1", "Apple is a popular fruit known for its sweetness and crisp texture."),
            new DataObject("vec2", "The tech company Apple is known for its innovative products like the iPhone."),
            new DataObject("vec3", "Many people enjoy eating apples as a healthy snack."),
            new DataObject("vec4", "Apple Inc. has revolutionized the tech industry with its sleek designs and user-friendly interfaces."),
            new DataObject("vec5", "An apple a day keeps the doctor away, as the saying goes."),
            new DataObject("vec6", "Apple Computer Company was founded on April 1, 1976, by Steve Jobs, Steve Wozniak, and Ronald Wayne as a partnership.")
        );

        List<String> inputs = data.stream()
            .map(DataObject::getText)
            .collect(Collectors.toList());

        // Specify the embedding model and parameters
        String embeddingModel = "llama-text-embed-v2";

        Map<String, Object> parameters = new HashMap<>();
        parameters.put("input_type", "passage");
        parameters.put("truncate", "END");

        // Generate embeddings for the input data
        EmbeddingsList embeddings = inference.embed(embeddingModel, parameters, inputs);

        // Get embedded data
        List<Embedding> embeddedData = embeddings.getData();
    }

    private static List<Float> convertBigDecimalToFloat(List<BigDecimal> bigDecimalValues) {
        return bigDecimalValues.stream()
            .map(BigDecimal::floatValue)
            .collect(Collectors.toList());
    }
}

class DataObject {
    private String id;
    private String text;

    public DataObject(String id, String text) {
        this.id = id;
        this.text = text;
    }

    public String getId() {
        return id;
    }
    public String getText() {
        return text;
    }
}

package main

// Import the required packages
import (
    "context"
   	"encoding/json"
    "fmt"
    "log"

    "github.com/pinecone-io/go-pinecone/v4/pinecone"
)

type Data struct {
    ID   string
    Text string
}

type Query struct {
	Text string
}

func prettifyStruct(obj interface{}) string {
    bytes, _ := json.MarshalIndent(obj, "", "  ")
    return string(bytes)
}

func main() {
    ctx := context.Background()

    // Initialize a Pinecone client with your API key
    pc, err := pinecone.NewClient(pinecone.NewClientParams{
        ApiKey: "YOUR_API_KEY",
    })
    if err != nil {
        log.Fatalf("Failed to create Client: %v", err)
    }

    // Define a sample dataset where each item has a unique ID and piece of text
    data := []Data{
        {ID: "vec1", Text: "Apple is a popular fruit known for its sweetness and crisp texture."},
        {ID: "vec2", Text: "The tech company Apple is known for its innovative products like the iPhone."},
        {ID: "vec3", Text: "Many people enjoy eating apples as a healthy snack."},
        {ID: "vec4", Text: "Apple Inc. has revolutionized the tech industry with its sleek designs and user-friendly interfaces."},
        {ID: "vec5", Text: "An apple a day keeps the doctor away, as the saying goes."},
        {ID: "vec6", Text: "Apple Computer Company was founded on April 1, 1976, by Steve Jobs, Steve Wozniak, and Ronald Wayne as a partnership."},
    }

    // Specify the embedding model and parameters
    embeddingModel := "llama-text-embed-v2"

    docParameters := pinecone.EmbedParameters{
        InputType: "passage",
        Truncate:  "END",
    }

    // Convert the text into numerical vectors that Pinecone can index
    var documents []string
    for _, d := range data {
        documents = append(documents, d.Text)
    }

    docEmbeddingsResponse, err := pc.Inference.Embed(ctx, &pinecone.EmbedRequest{
        Model:      embeddingModel,
        TextInputs: documents,
        Parameters: docParameters,
    }) 
    if err != nil {
        log.Fatalf("Failed to embed documents: %v", err)
    } else {
        fmt.Printf(prettifyStruct(docEmbeddingsResponse))
    }
}

PINECONE_API_KEY="YOUR_API_KEY"

curl https://api.pinecone.io/embed \
  -H "Api-Key: $PINECONE_API_KEY" \
  -H "Content-Type: application/json" \
  -H "X-Pinecone-Api-Version: 2026-07" \
  -d '{
      "model": "llama-text-embed-v2",
      "parameters": {
        "input_type": "passage",
        "truncate": "END"
      },
      "inputs": [
        {"text": "Apple is a popular fruit known for its sweetness and crisp texture."},
        {"text": "The tech company Apple is known for its innovative products like the iPhone."},
        {"text": "Many people enjoy eating apples as a healthy snack."},
        {"text": "Apple Inc. has revolutionized the tech industry with its sleek designs and user-friendly interfaces."},
        {"text": "An apple a day keeps the doctor away, as the saying goes."},
        {"text": "Apple Computer Company was founded on April 1, 1976, by Steve Jobs, Steve Wozniak, and Ronald Wayne as a partnership."}
      ]
  }'

The response looks like this:

EmbeddingsList(
    model='llama-text-embed-v2',
    data=[
        {'values': [0.04925537109375, -0.01313018798828125, -0.0112762451171875, ...]},
        ...
    ],
    usage={'total_tokens': 130}
)

EmbeddingsList(1) [
  {
    values: [
      0.04925537109375, 
      -0.01313018798828125, 
      -0.0112762451171875,
      ...
    ]
  },
  ...
  model: 'llama-text-embed-v2',
  data: [ { values: [Array] } ],
  usage: { totalTokens: 130 }
]

class EmbeddingsList {
    model: llama-text-embed-v2
    data: [class Embedding {
        values: [0.04925537109375, -0.01313018798828125, -0.0112762451171875, ...]
        additionalProperties: null
    }, ...]
    usage: class EmbeddingsListUsage {
        totalTokens: 130
        additionalProperties: null
    }
    additionalProperties: null
}

{
  "data": [
    {
      "values": [
        0.03942871,
        -0.010177612,
        -0.046051025,
        ...
      ]
    },
    ...
  ], 
  "model": "llama-text-embed-v2",
  "usage": {
    "total_tokens": 130
  }
}

{
  "data": [
    {
      "values": [
        0.04925537109375,
        -0.01313018798828125,
        -0.0112762451171875,
        ...
      ]
    }, 
    ...
  ],
  "model": "llama-text-embed-v2",
  "usage": {
    "total_tokens": 130
  }
}

Get started

Index data

Search

Optimize

Manage data

Manage cost

Move to production

Agents

Pod-based indexes (legacy)

Read units

Embedding tokens

See also

​Read units

​Embedding tokens

​See also

Read units

Embedding tokens

See also