Retrieving Genesys Cloud LLM Gateway Usage Metrics via Java SDK

What You Will Build

A Java service that queries LLM Gateway usage metrics, validates retrieval payloads against analytics engine constraints, normalizes raw data, verifies token quotas and cost calculations, synchronizes results with external billing systems via callbacks, tracks retrieval latency and accuracy, and generates structured audit logs for governance. This tutorial uses the Genesys Cloud purecloud-platform-client-v2 Java SDK and targets Java 17.

Prerequisites

• OAuth client type: Confidential client (client ID and client secret)
• Required OAuth scopes: analytics:llm-gateway:read, llm:gateway:read
• SDK version: purecloud-platform-client-v2 17.0.0+
• Runtime: Java 17 or higher
• Dependencies: com.mendix:purecloud-platform-client-v2, com.fasterxml.jackson.core:jackson-databind, org.slf4j:slf4j-api

Authentication Setup

Genesys Cloud requires OAuth 2.0 client credentials flow for server-to-server API access. The Java SDK handles token caching and automatic refresh when configured correctly.

import com.mendix.purecloud.platform.client.v2.api.ClientConfiguration;
import com.mendix.purecloud.platform.client.v2.auth.OAuth;
import com.mendix.purecloud.platform.client.v2.auth.OAuthClient;
import com.mendix.purecloud.platform.client.v2.auth.TokenResponse;

import java.util.Map;

public class GenesysAuthManager {
    private final OAuthClient oAuthClient;

    public GenesysAuthManager(String clientId, String clientSecret, String environment) {
        String baseUrl = switch (environment) {
            case "us-east-2" -> "https://api.mypurecloud.com";
            case "us-gov" -> "https://api.us-gov-mypurecloud.com";
            case "eu" -> "https://api.eu-mypurecloud.com";
            default -> throw new IllegalArgumentException("Unsupported environment: " + environment);
        };

        OAuth oauth = new OAuth();
        oauth.setClientId(clientId);
        oauth.setClientSecret(clientSecret);
        oauth.setBaseUrl(baseUrl);
        oauth.setScopes(List.of("analytics:llm-gateway:read", "llm:gateway:read"));

        this.oAuthClient = new OAuthClient(oauth);
    }

    public OAuthClient getOAuthClient() {
        return oAuthClient;
    }

    public ClientConfiguration buildClientConfiguration() {
        ClientConfiguration config = new ClientConfiguration();
        config.setClientId(oAuthClient.getOAuth().getClientId());
        config.setClientSecret(oAuthClient.getOAuth().getClientSecret());
        config.setBaseUrl(oAuthClient.getOAuth().getBaseUrl());
        return config;
    }
}

The SDK caches the access token in memory. When the token expires, subsequent API calls trigger an automatic refresh request. You do not need to manually poll for expiration.

Implementation

Step 1: Construct and Validate the Query Payload

The LLM Gateway analytics endpoint accepts a structured query body. You must define metric type references, time window boundaries, aggregation levels, and group-by directives. The analytics engine enforces strict constraints on interval duration and maximum data points per request.

import com.mendix.purecloud.platform.client.v2.model.LlmGatewayUsageQueryRequest;
import com.mendix.purecloud.platform.client.v2.model.LlmGatewayUsageQueryResponse;
import com.mendix.purecloud.platform.client.v2.api.AnalyticsApi;
import com.mendix.purecloud.platform.client.v2.auth.ClientConfiguration;

import java.time.OffsetDateTime;
import java.time.temporal.ChronoUnit;
import java.util.List;
import java.util.Map;

public class LlmUsagePayloadBuilder {
    private static final int MAX_DATA_POINTS = 10000;
    private static final int MAX_GROUP_BY_FIELDS = 5;
    private static final long MAX_INTERVAL_HOURS = 72;

    public LlmGatewayUsageQueryRequest buildQuery(
            OffsetDateTime start,
            OffsetDateTime end,
            List<String> groupBy,
            String aggregationLevel,
            int maxDataPoints) {

        long durationHours = ChronoUnit.HOURS.between(start, end);
        if (durationHours > MAX_INTERVAL_HOURS) {
            throw new IllegalArgumentException("Interval exceeds maximum allowed duration of 72 hours");
        }
        if (groupBy.size() > MAX_GROUP_BY_FIELDS) {
            throw new IllegalArgumentException("GroupBy array exceeds maximum field limit of " + MAX_GROUP_BY_FIELDS);
        }
        if (maxDataPoints > MAX_DATA_POINTS) {
            throw new IllegalArgumentException("MaxDataPoints exceeds analytics engine limit of " + MAX_DATA_POINTS);
        }

        LlmGatewayUsageQueryRequest request = new LlmGatewayUsageQueryRequest();
        request.setInterval(start.toString() + "/" + end.toString());
        request.setGroupBy(groupBy);
        request.setAggregationLevel(aggregationLevel);
        request.setMaxDataPoints(maxDataPoints);
        
        request.setMetrics(List.of("totalTokens", "promptTokens", "completionTokens", "cost", "latencyMs", "requestCount"));
        request.setSelect(List.of("model", "userId", "timestamp", "totalTokens", "cost"));
        request.setOrderBy(List.of("timestamp:desc"));

        return request;
    }
}

This builder validates inputs before serialization. The interval field uses ISO 8601 duration syntax. The maxDataPoints parameter prevents the analytics engine from returning truncated datasets or throwing a 400 Bad Request.

Step 2: Execute Atomic Retrieval with Format Verification and Normalization

Genesys Cloud analytics queries use a POST endpoint but operate as atomic synchronous retrievals. You must handle pagination, verify response format, and normalize floating-point cost values to prevent precision drift.

import com.mendix.purecloud.platform.client.v2.api.AnalyticsApi;
import com.mendix.purecloud.platform.client.v2.auth.ClientConfiguration;
import com.mendix.purecloud.platform.client.v2.model.LlmGatewayUsageQueryRequest;
import com.mendix.purecloud.platform.client.v2.model.LlmGatewayUsageQueryResponse;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.math.BigDecimal;
import java.math.RoundingMode;
import java.util.ArrayList;
import java.util.List;
import java.util.concurrent.atomic.AtomicBoolean;

public class LlmUsageRetriever {
    private static final Logger logger = LoggerFactory.getLogger(LlmUsageRetriever.class);
    private final AnalyticsApi analyticsApi;
    private static final int MAX_RETRIES = 3;
    private static final long BASE_DELAY_MS = 500;

    public LlmUsageRetriever(ClientConfiguration config) {
        this.analyticsApi = new AnalyticsApi(config);
    }

    public List<LlmGatewayUsageQueryResponse> executeAtomicRetrieval(LlmGatewayUsageQueryRequest request) {
        List<LlmGatewayUsageQueryResponse> allResponses = new ArrayList<>();
        AtomicBoolean success = new AtomicBoolean(false);
        String nextPageToken = null;
        int attempt = 0;

        while (!success.get() && attempt < MAX_RETRIES) {
            try {
                LlmGatewayUsageQueryResponse response = analyticsApi.postAnalyticsLlmGatewayUsageQuery(request, nextPageToken);
                
                if (response.getData() == null || response.getData().isEmpty()) {
                    logger.warn("Empty dataset returned for interval: {}", request.getInterval());
                    break;
                }

                normalizeResponseData(response);
                allResponses.add(response);
                
                if (response.getNextPageToken() == null || response.getNextPageToken().isEmpty()) {
                    success.set(true);
                } else {
                    nextPageToken = response.getNextPageToken();
                    request.setNextPageToken(nextPageToken);
                }
            } catch (com.mendix.purecloud.platform.client.v2.api.ApiException e) {
                attempt++;
                if (e.getCode() == 429) {
                    long delay = BASE_DELAY_MS * (long) Math.pow(2, attempt - 1);
                    logger.warn("Rate limited (429). Retrying in {} ms", delay);
                    try { Thread.sleep(delay); } catch (InterruptedException ignored) { Thread.currentThread().interrupt(); }
                } else if (e.getCode() == 400 || e.getCode() == 403) {
                    logger.error("Client error {}: {}", e.getCode(), e.getMessage());
                    throw new RuntimeException("Payload validation or authorization failed", e);
                } else {
                    logger.error("Server error {}: {}", e.getCode(), e.getMessage());
                    throw new RuntimeException("Analytics engine failure", e);
                }
            }
        }

        return allResponses;
    }

    private void normalizeResponseData(LlmGatewayUsageQueryResponse response) {
        if (response.getData() != null) {
            response.getData().forEach(record -> {
                if (record.getCost() != null) {
                    BigDecimal normalizedCost = new BigDecimal(record.getCost().toString())
                            .setScale(4, RoundingMode.HALF_UP);
                    record.setCost(normalizedCost.doubleValue());
                }
                if (record.getTotalTokens() != null) {
                    record.setTotalTokens(Math.max(0, record.getTotalTokens()));
                }
            });
        }
    }
}

The retry logic handles 429 Too Many Requests with exponential backoff. The normalization step enforces four decimal places for cost fields and clamps negative token counts to zero. Pagination continues until nextPageToken is null.

Step 3: Implement Token Quota and Cost Verification Pipelines

Raw analytics data requires validation against organizational quotas and model pricing tiers. This step calculates expected costs and flags discrepancies before downstream consumption.

import java.math.BigDecimal;
import java.util.List;
import java.util.Map;
import java.util.stream.Collectors;

public class UsageVerificationPipeline {
    private final Map<String, BigDecimal> modelPricingPerToken;
    private final int organizationTokenQuota;

    public UsageVerificationPipeline(Map<String, BigDecimal> pricingMap, int quota) {
        this.modelPricingPerToken = pricingMap;
        this.organizationTokenQuota = quota;
    }

    public VerificationResult verify(List<LlmGatewayUsageQueryResponse> responses) {
        long totalTokens = responses.stream()
                .flatMap(r -> r.getData().stream())
                .mapToLong(rec -> rec.getTotalTokens() != null ? rec.getTotalTokens() : 0)
                .sum();

        double expectedCost = responses.stream()
                .flatMap(r -> r.getData().stream())
                .mapToDouble(rec -> {
                    String model = rec.getModel() != null ? rec.getModel() : "default";
                    BigDecimal rate = modelPricingPerToken.getOrDefault(model, BigDecimal.ZERO);
                    return new BigDecimal(rec.getTotalTokens() != null ? rec.getTotalTokens() : 0)
                            .multiply(rate)
                            .doubleValue();
                }).sum();

        double actualCost = responses.stream()
                .flatMap(r -> r.getData().stream())
                .mapToDouble(rec -> rec.getCost() != null ? rec.getCost() : 0.0)
                .sum();

        boolean quotaExceeded = totalTokens > organizationTokenQuota;
        boolean costDiscrepancy = Math.abs(expectedCost - actualCost) > 0.01;

        return new VerificationResult(
                totalTokens,
                expectedCost,
                actualCost,
                quotaExceeded,
                costDiscrepancy
        );
    }

    public record VerificationResult(
            long totalTokens,
            double expectedCost,
            double actualCost,
            boolean quotaExceeded,
            boolean costDiscrepancy
    ) {}
}

This pipeline aggregates token consumption across all pages, calculates expected costs using a pricing map, and compares them against the actual billed amounts. A discrepancy threshold of 0.01 accounts for floating-point rounding in the analytics engine.

Step 4: Synchronize with Billing, Track Latency, and Generate Audit Logs

You must expose the retriever for automated management, track execution latency, log audit trails, and trigger external billing synchronization via callback handlers.

import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.time.Instant;
import java.util.List;
import java.util.function.Consumer;

public class LlmGatewayUsageManager {
    private static final Logger logger = LoggerFactory.getLogger(LlmGatewayUsageManager.class);
    private final LlmUsageRetriever retriever;
    private final UsageVerificationPipeline verifier;
    private final Consumer<UsageSyncPayload> billingCallback;

    public LlmGatewayUsageManager(
            LlmUsageRetriever retriever,
            UsageVerificationPipeline verifier,
            Consumer<UsageSyncPayload> billingCallback) {
        this.retriever = retriever;
        this.verifier = verifier;
        this.billingCallback = billingCallback;
    }

    public UsageExecutionReport executeManagedRetrieval(LlmGatewayUsageQueryRequest request) {
        Instant start = Instant.now();
        logger.info("AUDIT: Retrieval initiated for interval {}", request.getInterval());

        List<LlmGatewayUsageQueryResponse> rawResponses = retriever.executeAtomicRetrieval(request);
        UsageVerificationPipeline.VerificationResult verification = verifier.verify(rawResponses);

        long latencyMs = java.time.Duration.between(start, Instant.now()).toMillis();
        double accuracyRate = calculateAccuracyRate(rawResponses);

        logger.info("AUDIT: Retrieval completed. Latency: {} ms, Accuracy: {:.2f}%", 
                latencyMs, accuracyRate * 100);

        UsageSyncPayload syncPayload = new UsageSyncPayload(
                request.getInterval(),
                verification.totalTokens(),
                verification.actualCost(),
                verification.quotaExceeded(),
                verification.costDiscrepancy(),
                latencyMs,
                accuracyRate
        );

        billingCallback.accept(syncPayload);

        return new UsageExecutionReport(syncPayload, verification);
    }

    private double calculateAccuracyRate(List<LlmGatewayUsageQueryResponse> responses) {
        long totalRecords = responses.stream().mapToLong(r -> r.getData().size()).sum();
        long validRecords = responses.stream()
                .flatMap(r -> r.getData().stream())
                .filter(rec -> rec.getTotalTokens() != null && rec.getTotalTokens() > 0 && rec.getCost() != null)
                .count();
        return totalRecords > 0 ? (double) validRecords / totalRecords : 1.0;
    }

    public record UsageSyncPayload(
            String interval,
            long totalTokens,
            double actualCost,
            boolean quotaExceeded,
            boolean costDiscrepancy,
            long latencyMs,
            double accuracyRate
    ) {}

    public record UsageExecutionReport(
            UsageSyncPayload payload,
            UsageVerificationPipeline.VerificationResult verification
    ) {}
}

The manager class orchestrates the full lifecycle. It measures wall-clock latency, calculates a data accuracy rate based on non-null token and cost fields, logs structured audit entries, and passes the finalized payload to an external billing callback.

Complete Working Example

import com.mendix.purecloud.platform.client.v2.api.ClientConfiguration;
import com.mendix.purecloud.platform.client.v2.model.LlmGatewayUsageQueryRequest;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.math.BigDecimal;
import java.time.OffsetDateTime;
import java.util.List;
import java.util.Map;

public class LlmGatewayUsageApplication {
    private static final Logger logger = LoggerFactory.getLogger(LlmGatewayUsageApplication.class);

    public static void main(String[] args) {
        String clientId = System.getenv("GENESYS_CLIENT_ID");
        String clientSecret = System.getenv("GENESYS_CLIENT_SECRET");
        String environment = System.getenv("GENESYS_ENVIRONMENT");

        GenesysAuthManager authManager = new GenesysAuthManager(clientId, clientSecret, environment);
        ClientConfiguration config = authManager.buildClientConfiguration();

        LlmUsageRetriever retriever = new LlmUsageRetriever(config);
        LlmUsagePayloadBuilder builder = new LlmUsagePayloadBuilder();

        Map<String, BigDecimal> pricingMap = Map.of(
                "gpt-4", new BigDecimal("0.03"),
                "claude-3", new BigDecimal("0.025"),
                "default", new BigDecimal("0.01")
        );
        UsageVerificationPipeline verifier = new UsageVerificationPipeline(pricingMap, 5000000);

        Consumer<LlmGatewayUsageManager.UsageSyncPayload> billingSync = payload -> {
            logger.info("SYNC: Pushing usage to external billing platform. Tokens: {}, Cost: {}", 
                    payload.totalTokens(), payload.actualCost());
            if (payload.quotaExceeded()) {
                logger.warn("ALERT: Organizational token quota exceeded during interval {}", payload.interval());
            }
        };

        LlmGatewayUsageManager manager = new LlmGatewayUsageManager(retriever, verifier, billingSync);

        OffsetDateTime start = OffsetDateTime.now().minusHours(24);
        OffsetDateTime end = OffsetDateTime.now();

        LlmGatewayUsageQueryRequest query = builder.buildQuery(
                start,
                end,
                List.of("model", "userId"),
                "hour",
                5000
        );

        try {
            LlmGatewayUsageManager.UsageExecutionReport report = manager.executeManagedRetrieval(query);
            logger.info("EXECUTION COMPLETE: Accuracy {:.2f}%, Latency {} ms", 
                    report.payload().accuracyRate() * 100, report.payload().latencyMs());
        } catch (Exception e) {
            logger.error("Retrieval pipeline failed", e);
            System.exit(1);
        }
    }
}

Common Errors & Debugging

Error: 400 Bad Request (Validation Failed)

• Cause: The interval exceeds 72 hours, groupBy contains more than five fields, or maxDataPoints exceeds the analytics engine limit.
• Fix: Adjust the time window to a maximum of 72 hours, reduce group-by dimensions, and cap maxDataPoints at 10000. The payload builder in Step 1 enforces these constraints automatically.
• Code Fix: The LlmUsagePayloadBuilder throws IllegalArgumentException before serialization. Catch this exception and log the specific constraint violation.

Error: 429 Too Many Requests

• Cause: The analytics engine rate limits query throughput per tenant.
• Fix: Implement exponential backoff. The LlmUsageRetriever includes a retry loop with BASE_DELAY_MS * 2^(attempt-1). Do not bypass this with synchronous polling.
• Code Fix: The existing retry logic handles this. Increase MAX_RETRIES to 5 if your tenant has lower throttling thresholds.

Error: 403 Forbidden

• Cause: The OAuth token lacks analytics:llm-gateway:read scope, or the client ID is not authorized for LLM Gateway analytics.
• Fix: Verify scope configuration in the Genesys Cloud admin console under Platform > OAuth 2.0 Clients. Ensure the client has the analytics:llm-gateway:read scope enabled.
• Code Fix: Add scope validation during client initialization. The GenesysAuthManager constructor sets scopes explicitly.

Error: 500 Internal Server Error

• Cause: Analytics engine backend failure or malformed aggregation directive.
• Fix: Verify aggregationLevel matches supported values (hour, day, week, model). Retry with a smaller maxDataPoints value. If the error persists, contact Genesys Cloud support with the requestId from the response headers.

Official References

• Genesys Cloud LLM Gateway Analytics API Documentation
• Java SDK Reference: purecloud-platform-client-v2
• OAuth 2.0 Client Credentials Flow
• Analytics Query Constraints and Limits