
Overview

Product video
Unlock the full potential of your AWS-based applications with LaunchDarkly, the runtime control platform for the AI era, trusted by software teams to control AI-generated code and AI agents in production at any scale.
Accelerate your software development lifecycle, de-risk deployments, and move at AI speed while staying in control.
The LaunchDarkly platform delivers runtime control through two solutions: CodeControl and AgentControl.
CodeControl helps teams ship AI-generated code confidently. With CodeControl, teams can observe production behavior, make changes in real time, and limit exposure based on actual impact. Through a combination of industry-leading feature flags, progressive rollouts, real-time observability, experimentation, and automatic recovery, LaunchDarkly gives organizations the ability to move at AI speed without giving up control.
AgentControl helps teams keep AI agents in check in production, blocking bad behavior and steering responses in real time. Teams can configure prompts and models before launch, monitor and observe live performance and behavior, and automatically take action, without redeploying. When agents make curious decisions, or when small prompt or model changes cause big issues, AgentControl detects and corrects them as they happen.
With runtime control across code and agents, LaunchDarkly helps enable teams to ship AI-built software with confidence, govern agent behavior in production, optimize AI performance and cost, build self-healing systems, and experiment continuously. The result is faster release velocity, lower production risk, and the ability to continuously adapt software and AI systems without slowing down to stay safer.
For custom pricing, EULA, or a private offer, please contact aws-alliance@launchdarkly.com
Highlights
- Ship AI generated code confidently, with feature flags, progressive deliver, automatic rollback and runtime control.
- AgentControl helps keep agents on track, blocking bad behavior and steering responses in real time, enabling agents that improve continuously, and self-heal.
- Test in production with faster loops. Use AI to generate endless variations, measure what works in production, and continuously improve outcomes.
Details
Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
Features and programs
Trust Center
Buyer guide

Financing for AWS Marketplace purchases
Pricing
Free trial
Dimension | Description | Cost/12 months |
|---|---|---|
LaunchDarkly Pro Bundle | LaunchDarkly Professional Platform with 300K CMAU and 10M Exp events | $44,100.00 |
Vendor refund policy
All fees are non-cancellable and non-refundable except as required by law.
How can we make this page better?
Legal
Vendor terms and conditions
Content disclaimer
Delivery details
Software as a Service (SaaS)
SaaS delivers cloud-based software applications directly to customers over the internet. You can access these applications through a subscription model. You will pay recurring monthly usage fees through your AWS bill, while AWS handles deployment and infrastructure management, ensuring scalability, reliability, and seamless integration with other AWS services.
Resources
Vendor resources
Support
Vendor support
Support Homepage: https://support.launchdarkly.com/hc/en-us support@launchdarkly.com
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.

Standard contract
Customer reviews
Clear Customer Journey Visibility with Powerful Filters and a Polished UI
We hoped we could work around it by adding customers as users on the platform with custom access levels. However, that also isn’t possible with Launch Darkly, which seems to offer an all-or-nothing level of sharing when it comes to sessions. This is disappointing, especially because it’s something we were very used to when using Highlight.io.
Additionally while the Dashboards are fine, we would prefer this data to be exported to Grafana where we would be able to use their panels to display and transform the data as we would like. We are working on this ourselves at the moment.
Easy Setup and a Straightforward Learning Curve
Feature flags have enabled safe gradual rollouts and now reduce risk and save engineering time
What is our primary use case?
My main use case for LaunchDarkly is feature flagging and gradual rollouts. Instead of releasing a new feature to all users at once, we can first enable it for internal users, then for a small group of customers, and only later roll it out to everyone.
When we released a new feature, we first turned it on only for internal users. After that, we enabled it for a small percentage of real customers, which helped us test that feature in production without taking too much risk. If something went wrong, we could simply turn the flag off in LaunchDarkly without doing a full rollback.
We use flags for gradual deploying and testing, then rolling out. For example, we enabled a feature, tested it in a specific environment, then turned off this flag.
What is most valuable?
The best feature LaunchDarkly offers is the flag that allows rollouts.
What I appreciate about LaunchDarkly is that the setup was easy, it had a clean user experience, and the control allowed us to manage the features without deploying them to everyone. We could deploy it gradually and then roll out easily. I particularly value the ability to click to turn the feature on and off.
LaunchDarkly has positively impacted my organization by reducing the risk of releasing new features because we did not have to expose everything to all users at the same time. It eventually resulted in faster releases and more confidence. It also saved engineering time because in some cases, we did not need to do a rollback or hot fixes; we could simply disable the feature flag. Additionally, it reduced the QA time since they could only test a specific area.
What needs improvement?
LaunchDarkly can be improved by managing old flags. We have an issue with old flags; it became very messy very fast and we need to be very disciplined about managing these flags. I also heard from the manager that it was very expensive when the usage grew.
Perhaps LaunchDarkly could mark old flags somehow or add a tag to these flags when they are not in use or have not been used for a long time. We found ourselves after a short period of time having too many flags.
For how long have I used the solution?
I have been working in my current field for above ten years.
Which solution did I use previously and why did I switch?
I used LaunchDarkly in my previous company for several months.
What other advice do I have?
Overall, LaunchDarkly saved our engineering time and helped us manage features very smoothly, allowing us to gradually deploy and roll out.
My advice for others looking into using LaunchDarkly is to manage the flags carefully, as it can become messy very fast.
I believe LaunchDarkly is a very useful tool for teams wanting to release features quickly and safely; it gives a lot of control and helps reduce the risk around production releases. I would rate this product an eight out of ten.
Which deployment model are you using for this solution?
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Clean, Intuitive Dashboard with Powerful Targeting and Fast Flag Propagation
Pricing feels acceptable for what you get. I haven’t fully explored the AI features yet, so I’m holding off on judging those for now. Overall, it’s a strong choice for teams that ship continuously, but budget-conscious orgs or teams with a low release cadence should compare alternatives first.
It’s also important to point out a major issue with segment integration with the backend system: there’s a bug where it can’t retrieve the correct value while segment targeting is being processed. Although we tried to contact LaunchDarkly support, the problem seems to remain unresolved.