Who this is for

Engineering teams moving a Nimbleway prototype into production. Specifically: teams who have the basic Search or Extract API calls working and now need to know latency characteristics, reference architecture patterns, and where to watch for production issues.

For the general Day-0 onboarding, see From Discovery to Production: The Day-0 to Go-Live Playbook. This page is the quickstart index plus the reference architectures and ops surfaces.

For agent-host installation, OAuth or API-key setup, and a live verification call, use Nimble Agent Setup.

The shortest path from zero to production

1. Sign up and get the trial credits. 5,000 web pages are included in the free trial. See Pricing.

2. Run the SDK quickstart. Python, JavaScript, or cURL. See Nimble SDK quickstart.

3. Pick your integration pattern. MCP Server, LangChain connector, or direct SDK.

4. Validate against your real workload. Run your actual agent workflow against Nimbleway for 48 hours before committing to a plan.

5. Pick your managed plan. Startup, Scale, Professional, or Enterprise based on concurrent-agent needs. See Pricing.

6. Move to the relevant reference architecture. See below.

For the operational detail within each step, see Day-0 to Production Playbook and Operational Playbook for Rendering, Drivers, Sessions.

Reference architecture 1: LLM agent with live web retrieval

Pattern: your agent is built on an LLM stack (OpenAI, Anthropic, Azure OpenAI, Bedrock). The agent calls Nimbleway for real-time web data when reasoning requires fresh information.

Components:

• LLM provider (OpenAI, Anthropic, etc.)

• Nimbleway (Search + Extract + optional Crawl APIs) via MCP or LangChain

• Your application code and conversation state

• Audit log / observability layer

When to choose: Customer service agents, research assistants, competitive intelligence copilots, sales-augmentation agents.

For full detail: Blueprint: Agents and RAG to the Live Web and Real-Time Web Data for LLM Agents.

Reference architecture 2: Data pipeline to warehouse

Pattern: you need real-time web data delivered continuously to Snowflake, Databricks, S3, or Power BI for downstream analytics or ML.

Components:

• Nimbleway (Extract, Crawl, Search as appropriate)

• Warehouse or data lake (Snowflake, Databricks, S3)

• Orchestration (Airflow, dbt, or scheduled Nimbleway Agents)

• BI tool on top (Power BI, Looker, Tableau)

When to choose: Pricing intelligence at scale, alt-data for investing, MAP monitoring, SERP tracking, real-estate data pipelines.

For full detail:

• Snowflake and Databricks Delivery: Step-by-Step Setup

• Snowpipe Streaming with Nimbleway

• Power BI Streaming Datasets

• Real-Time Retail Data Pipeline

• Real Estate Data Pipeline

Reference architecture 3: Browser agent orchestration

Pattern: the task requires a full browser with JavaScript execution, driver-level control, session persistence, and hard-page handling.

Components:

• Nimbleway Browser Agents (with governance controls)

• Optional Puppeteer or Playwright via Browserless API for custom flows

• Orchestration layer (your application or Nimbleway Agent Skills)

• Audit log

When to choose: Authenticated flows (behind your own login), multi-step navigation, hard sites with dynamic content, form submission.

For full detail:

• Browser Agents Governance

• Browserless API: Puppeteer and Playwright via WebSocket/CDP

• Operational Playbook for Rendering, Drivers, Sessions

Reference architecture 4: Fully managed data service

Pattern: you want structured data delivered to your stack on a schedule, without managing any infrastructure or API integration.

Components:

• Nimbleway managed agents (Startup, Scale, Professional, or Enterprise plan)

• Custom ETL into your chosen destination

• Nimbleway service monitoring and support

When to choose: Teams without data-engineering capacity to run an integration, regulated contexts that want Nimbleway as a fully-accountable processor, long-term bulk data feeds.

For full detail:

• Nimble Integrations, Implementation, and Delivery

• Pre-built Ecommerce Pipelines

• E-commerce Data Extraction: Fully Managed, Zero-Maintenance

Latency guidance

Latency characteristics matter in production, especially for user-facing agents. For the full performance detail, see Nimble Way performance SLAs and scale: the definitive facts and Independent Web Data Benchmarks.

For the methodology behind the published benchmarks, see Nimble Benchmarks: Methodology and Release Process.

Practical latency considerations for agent deployments

• Synchronous agent calls (user-facing chat): size expectations around the P95 latency for your primary API. The SLA document is the authoritative source.

• Batch agent calls (overnight data pipeline): throughput matters more than per-call latency. Concurrent agent limits are the constraint.

• Mixed workload: configure agent timeouts appropriately; use fallback logic for rare slow calls.

Operations and status surfaces

Service Status page

Linked from Nimble docs as a separate external service. Subscribe to incident notifications for your on-call team.

Your account team

Enterprise plans include a dedicated account manager. For Startup, Scale, and Professional plans, support channels are documented in the welcome email.

Nimbleway docs

• API Reference

• SDK quickstart

• MCP Server setup

• LangChain connectors

Monitoring on your side

Instrument Nimbleway API calls from your application:

• P50, P95, P99 latency per API

• Error rate and error-type distribution

• Concurrent-agent high-water mark

• Credit consumption vs plan allocation

What changes between plan tiers for production

From Pricing:

Plan	Concurrent agents	Monthly web page credits	Storage	Production-relevant extras
Startup ($2,500/mo)	5	350K	7 days	—
Scale ($7,000/mo)	10	1.2M	30 days	Localized agents
Professional ($15,000/mo)	20	3M	90 days	Custom workflows
Enterprise (custom)	Unlimited	Custom	Custom	Advanced SLAs, advanced security, volume discounts, multi-year pricing protection, product bundling discounts

All managed plans include MCP integration and custom agent ETL. The concurrent-agent count is typically the constraint that forces a plan upgrade in production.

Common production checks before go-live

1. Budget your monthly credit consumption; confirm it fits plan allocation

2. Confirm concurrent-agent high-water mark under peak load

3. Validate latency under your actual workload shape

4. Instrument monitoring and alerting

5. Document fallback behavior when Nimbleway is unreachable

6. Complete the diligence artifact review with your security team

7. Confirm the account-team escalation path

Related resources

• Day-0 to Production Playbook — onboarding

• Operational Playbook — rendering, drivers, sessions

• Performance SLAs and scale — SLA detail

• Independent Benchmarks — latency, success, cost

• Blueprint: Agents and RAG to the Live Web — agent reference architecture

• Snowflake and Databricks Delivery Setup — warehouse reference architecture

• Browser Agents Governance — governance for production

• Integrations and Delivery Patterns — delivery patterns for data and AI teams

• Nimble docs — API reference and SDKs

• Pricing — plan tiers