AI model deployment: Best practices for production environments

What is AI model deployment?

The process of making machine learning models available in production environments. It includes packaging trained models, managing their configurations, and guaranteeing they can reliably serve predictions to real users.

Building AI applications comes with a unique set of challenges. Unlike traditional software, AI models are non-deterministic—meaning they can produce different outputs even with the same inputs. They also depend heavily on configuration, from model parameters to the carefully crafted prompts that shape their behavior.

Change any of these elements, and you could majorly impact the user experience.

The traditional approach—of packaging model changes with application code — creates unnecessary friction. Every prompt tweak requires a new code deployment, every model update means rebuilding containers, and if something goes wrong, rolling back means another software deployment cycle.

This is where LaunchDarkly AI Configs can help. Rather than coupling model configurations with your application code, AI Configs let you manage and update them at runtime. You can test new models, adjust prompts, and roll back changes instantly (all without redeployment).

Below, we’ll show you how to transform your AI model deployment with AI Configs.

AI model deployment fundamentals

Deploying AI models has fundamentally changed since the early days of AI app development. The rise of cloud service providers offering large language models (LLMs) as API services relieved many teams from having to manage model artifacts directly. Instead, the focus has shifted to managing configurations, prompts, and runtime parameters.

Key components

A production AI service needs three core elements:

Model configuration: Which model to use and its parameters (like temperature and token limits).
Prompts and messages: The instructions and context that guide model behavior.
Runtime controls: How to route traffic, monitor performance, and handle failures.

Deployment patterns

Teams typically choose between two approaches:

Embedded configuration: Model settings and prompts live in application code. Changes require redeployment, but configurations are version-controlled and tested with code.

Runtime configuration: Model settings and prompts are managed separately from application code and can be updated without redeployment. This provides more flexibility but needs proper governance.

Resource planning

When planning AI deployments, focus on:

Cost management: Track token usage and optimize prompt length.
Performance monitoring: Watch latency, particularly during high traffic.
Error handling: Have fallback options when models or providers fail.

AI-as-a-service has simplified infrastructure needs, but it brings new challenges in configuration management and reliability.

Building production AI applications introduces obstacles that traditional deployment practices weren't designed to handle. Typically, the same input reliably produces the same output, but AI models can generate varying responses. This non-deterministic behavior makes testing and validation more complex. You need ways to measure and monitor output quality (not just correctness).

Small changes in prompts or model parameters can dramatically impact output quality. A temperature setting that works perfectly for one use case might produce unusable results for another. You need flexibility to tune these configurations without rebuilding and redeploying your entire application.

And then there are the model providers. Most teams rely on third-party model providers like OpenAI or Anthropic. When these services experience issues or release updates, you may need ways to switch to backup providers, roll back to stable versions, test new model versions safely, and monitor provider-specific metrics.

AI applications often require frequent adjustments. Teams need to:

refine prompts based on user feedback
test new model versions as they're released
optimize for cost and performance
adapt to changes in model behavior

Traditional deployment cycles (where changes are packaged with application code) can create problems for each iteration. You end up choosing between shipping quickly or maintaining stability.

These challenges call for a new approach—one that separates model configuration from application deployment and provides tools for safe runtime updates. That’s where AI Configs can help solve these problems.

What are AI Configs?

AI Configs is the LaunchDarkly solution for managing AI models and their configurations at runtime. Think of it as a control plane for your AI features. Instead of embedding model settings, prompts, and parameters in your application code, you manage them through the LaunchDarkly interface and SDKs.

Each AI Config contains one or more variations. A variation defines a complete set of settings for your AI feature:

Which model to use
What prompts or messages to send
Any model-specific parameters like temperature or token limits

The real power comes from being able to update these settings without touching your application code. Want to test a new model? Create a variation. Need to adjust a prompt? Update it through the UI. Having issues with a provider? Roll back instantly to a previous variation.

Here's an example:

# Your application code stays clean and simple
def generate_response(user_input):
    context = Context.builder('user-123').build()
    
    # AI config handles all the model details
    config, tracker = ld_ai_client.config(
        'my-ai-config',
        context,
        default_config
    )
    
    response = tracker.track_openai_metrics(
        lambda: openai_client.chat.completions.create(
            model=config.model.name,
            messages=config.messages
        )
    )
    
    return response.choices[0].message.content

AI Configs are specifically designed for managing AI workloads. They include built-in support for:

Model configuration management
Prompt templating and variables
Token usage tracking
Performance monitoring
User satisfaction metrics

This ultimately means you can focus on building great AI features while LaunchDarkly handles the complexity of managing them in production.

Runtime configuration strategies

Managing AI models in production means being able to update configurations without disrupting your service. Here are a few strategies to help you use AI Configs:

Structure your configurations: Use templates for consistent prompt structures. Separate business logic from prompt content. Keep configuration metadata organized and searchable. This makes it easier to update prompts systematically and reuse successful patterns across different features.
Build in observability: Each configuration should include clear success metrics. Define what "good" looks like in terms of response quality, latency, and cost. Set up monitoring that tracks these metrics across configuration changes.
Implement access controls: Not everyone needs to edit AI Configurations. Use roles and permissions to control who can make changes, especially in production environments. Consider requiring approvals for major configuration updates.
Maintain configuration history: Keep previous configurations available for quick rollbacks. Document why changes were made and their impact. This history helps debug issues and informs future optimization efforts.
Create testing protocols: Establish standard test cases for validating configuration changes. Include edge cases and potential failure modes. Test both the happy path and error scenarios before rolling out updates.

Best practices for deploying AI models in production

Deploying AI features requires a different approach to software delivery. It won’t necessarily be a major overhaul, but you’ll need to tweak some things to manage AI systems across their full lifecycle. Here’s how to do it:

1. Progressive delivery for AI

AI features need careful rollout strategies to minimize risk. Start by defining clear phases for your rollout:

Start with shadow deployments. Run your new configuration alongside the existing one without serving results to users. This lets you compare model behavior, evaluate performance, and catch potential issues before they impact users. Shadow deployments are great for testing new models or significantly different prompt strategies.

When you're confident in your changes, use targeting rules to roll them out gradually:

Deploy to internal users who can provide detailed feedback. Then expand to beta testers who represent your target audience. Next, roll out to a small percentage of production traffic (typically starting at 5-10%). Finally, monitor metrics carefully and increase rollout percentage if everything remains stable.
Keep your configurations versioned and documented. Include metadata like version numbers, update dates, and change descriptions in your configurations. This creates an audit trail and makes it easier to track which changes led to which outcomes.
Implement fallback configurations. If your primary model provider has issues or if a new configuration isn't performing well, you need a proven, stable configuration to fall back on. These fallbacks should be simple and reliable. Prioritize consistency over cutting-edge features.

2. Monitoring AI products

Quality monitoring combines traditional metrics with AI-specific indicators. Here's how to set up comprehensive monitoring for your AI features.

Core metrics to track:

Token usage and costs across different models and features
Response times across the full request lifecycle
Quality indicators like user satisfaction and completion rates
Error patterns, including rate limits, timeouts, and provider errors

AI Configs automatically tracks these metrics to give you visibility into generation counts, token consumption, user satisfaction signals, response latency, and error rates. This data helps you learn how your AI features perform in production.

Build dashboards that compare metrics across different model variations, user segments, and time periods. These visualizations help you track the impact of configuration changes and identify optimization opportunities. Regular reporting on usage patterns and costs helps justify configuration decisions and track improvements over time.

For long-term success, monitor for model drift and track seasonal patterns in usage and performance. Keep historical data to identify trends and validate that your AI features continue to meet user needs.

3. Optimizing AI experiences

Optimizing AI features goes beyond just picking the latest model or tweaking prompts. Success demands balancing performance, cost, and user satisfaction across different segments of your user base. Start with baseline measurements. Track how your current configurations perform in terms of response quality, latency, and cost.

Establish clear success metrics: what makes an AI interaction "good" for your specific use case? For a customer service bot, this might mean successful query resolution. For a content generator, it could be how often users accept the generated content without edits.

Test different configurations systematically. Try variations in:

Model selection (newer isn't always better)
Prompt structure and content
Parameter settings like temperature and token limits
Response formatting and presentation

Create targeted experiences for different user segments. Enterprise customers might need more advanced models, while freemium users could use lighter alternatives.

Model behavior can drift over time, user needs evolve, and new models become available. Review your configurations regularly to maintain quality and control costs. Keep testing and measuring—what works today might not be efficient tomorrow.

Success means finding the right balance for your specific needs, not just chasing the latest model or lowest-cost option.

Get started with LaunchDarkly AI Configs

LaunchDarkly AI Configs give you the tools to ship quickly without sacrificing stability. You can manage your AI features at runtime, monitor their performance, and optimize them based on real user data.

Start by identifying one AI feature in your application that could benefit from runtime configuration. Maybe it's a chatbot that needs frequent prompt updates or a content generator where you want to test new models. Create an AI Config for this feature, and see firsthand how much easier it is to iterate without constant redeployments.

Remember:

Use progressive rollouts to safely test changes
Monitor key metrics to catch issues early
Create targeted experiences for different user segments
Keep fallback configurations ready for reliability

Ready to streamline your AI deployments? Start a free trial of LaunchDarkly, or check out our AI Configs documentation to learn more about implementing AI Configs in your application.

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Jesse Sumrak

LaunchDarkly