---
title: Crest Data Quick Start Services
description: Quick Start Professional Services for Full-Stack and LLM Observability
breadcrumbs: Docs > Integrations > Crest Data Quick Start Services
---

> For the complete documentation index, see [llms.txt](https://docs.datadoghq.com/llms.txt).

# Crest Data Quick Start Services
marketplace
{% callout %}
# Important note for users on the following Datadog sites: us2.ddog-gov.com

{% alert level="info" %}
To find out if this integration is available in your organization, see your [Datadog Integrations](https://app.datadoghq.com/integrations) page or ask your organization administrator.

To initiate an exception request to enable this integration for your organization, email [support@ddog-gov.com](mailto:support@ddog-gov.com).
{% /alert %}

{% /callout %}
  Full-Stack Observability Quick Start ServiceLLM Observability Quick Start Service
## Overview{% #overview %}

Crest Data's Full-Stack and LLM Observability Quick Start Services accelerate your Datadog implementation with high-velocity, expert-led engagements designed to get your team to production quickly.

Our Full-Stack Quick Start covers Infrastructure Monitoring, Log Management, and APM, delivering customized dashboards, intelligent alerting, and integrations for immediate impact.

Our LLM Quick Start delivers comprehensive AI observability using Datadog, to give teams visibility into model performance, quality, and cost.

### Benefits{% #benefits %}

- **Full-Stack Observability**:

  - **Complete visibility**: Infrastructure, logs, and application monitoring in one place
  - **Rapid implementation**: Preconfigured dashboards, pipelines, and monitors from day one
  - **Cloud support**: AWS, GCP, and Azure integration
  - **End-to-end tracing**: Service mapping, transaction tracing, and application insights

- **LLM Observability**:

  - **LLM Cost Management**: Track token costs by workflow and model for data-driven decisions
  - **AI Cost Optimization**: Realize cost savings through caching, routing, model selection, and prompt tuning
  - **AI Workflow Reliability**: Pinpoint latency bottlenecks, failing tools, and degraded workflows
  - **AI-Specific Tracing**: Comprehensive pipeline traces with metadata
  - **Model Quality and Performance**: Compare models and build repeatable quality frameworks

- **Expert knowledge transfer**: Hands-on training and documentation

- **Proven ROI**: Value assessment templates to measure impact

- **Enterprise-grade Security**: RBAC and SSO for access control and compliance

### Project Scope{% #project-scope %}

#### Full-Stack Observability: Three week engagement:{% #full-stack-observability-three-week-engagement %}

**Week 1: Discovery and Infrastructure Monitoring**

- Conduct platform discovery, architecture review, and Datadog setup with RBAC and SSO
- Deploy Datadog agents across 10-15 hosts (servers, VMs, containers)
- Deliver up to four infrastructure dashboards and monitors for CPU, disk, memory, and host availability

**Week 2: Log Management**

- Onboard up to two log sources
- Integrate with two hyperscaler services and two log pipelines for parsing and filtering
- Build one log analytics dashboard and up to three monitors for anomaly and pattern detection

**Week 3: APM, Knowledge Transfer, and Documentation**

- Instrument up to two services
- Enable service maps
- Implement end-to-end tracing for three transactions, one APM performance dashboard and up to two latency or error anomaly detection monitors
- Deliver a two-hour knowledge transfer workshop
- Provide value assessment templates and Day-2 roadmap

#### LLM Observability: Four-week engagement:{% #llm-observability-four-week-engagement %}

**Week 1: Discovery, Architecture Review, Setup**

- Conduct AI use case discovery and architecture review; validate RBAC and SSO
- Build an inventory of AI components
- Define baseline KPIs and establish a KPI framework

**Week 2: Instrumentation and Trace Collection**

- Instrument up to two AI workflows
- Capture traces across the full chain
- Integrate up to two model providers and instrument up to five MCP and toolintegrations
- Capture metadata (prompts, model version, token counts, session context, errors, tool outcomes)
- Build up to three dashboards around throughput, latency, failures and token consumption

**Week 3: Quality, Cost, and Reliability**

- Build dashboards: Summary, Token Usage, Cost by workflow and model, Latency, Failure Modes
- Build up to five monitors. (for example, latency spikes, error rates, token and cost anomalies)
- Define an initial quality framework
- Validate the service map

**Week 4: Operationalization and Handover**

- Develop an AI observability runbook for common incident scenarios
- Deliver governance guidance, and prompt, model, and workflow tracking recommendations
- Identify cost optimization opportunities
- Deliver a two-hour knowledge transfer workshop and Day-2 roadmap for evals, guardrails, and production SRE practices

## Support{% #support %}

For support or feature requests, contact Crest Data through the following channels:

- Sales email: [datadog-sales@crestdata.ai](mailto:datadog-sales@crestdata.ai)
- Support email: [datadog.integrations@crestdata.ai](mailto:datadog.integrations@crestdata.ai)
- Website: [Crest data website](https://www.crestdata.ai/)

This application is made available through the Marketplace and is supported by a Datadog Technology Partner. Click Here to purchase this application.