Sold by: LaunchDarkly
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Accelerate innovation at AI scale by using LaunchDarkly for your front-end and back-end feature releases on AWS, including AI applications using Amazon Bedrock and AgentCore!
4.5
Overview
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.
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Dimension | Description | Cost/12 months |
|---|---|---|
LaunchDarkly Pro Bundle | LaunchDarkly Professional Platform with 300K CMAU and 10M Exp events | $44,100.00 |
Dimensions summary
You buy one contract bundle: the LaunchDarkly Pro Bundle. It is a fixed package priced as a single unit, not a menu of separate options. The bundle includes a set usage allowance of 300K CMAU (client-side monthly active users) and 10M experimentation events. Pricing does not scale per seat or per connection here; you commit to the bundled capacity for the contract term. Because there is one dimension, there are no tiers or add-ons to compare on Marketplace. You pay for the package as defined by its included usage amounts.
Top-of-mind questions for buyers
A CMAU is one unique client-side user or device active in a month. Each distinct end user or device counts once, no matter how many times they connect. The bundle includes up to 300K such users per month as part of its fixed capacity.
Experimentation events tie to A/B and multivariate testing activity across features, models, and prompts. Each tracked experimentation action counts toward the 10M monthly total. The bundle includes this volume as part of the fixed package capacity.
The platform provides runtime control over code and AI agents. You get feature flags, targeting, progressive rollouts, and rollbacks. It also covers experimentation, observability such as logs and traces, and agent controls like prompt and model configuration. All run without redeploying code.
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Support Homepage: https://support.launchdarkly.com/hc/en-us support@launchdarkly.com
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Updated weekly
By LaunchDarkly
By Split Software
By Bricks Software
Top
10
In Business Intelligence & Advanced Analytics, Generative AI, Continuous Integration and Continuous Delivery
Top
50
In Agile Lifecycle Management
Top
100
In Testing
Sentiment is AI generated from actual customer reviews on AWS and G2
Functionality
Ease of use
Customer service
Cost effectiveness
Positive reviews
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Negative reviews
AI generated from product descriptions
Feature Flags and Progressive Delivery
Industry-leading feature flags with progressive rollouts enabling controlled deployment of code changes across environments
Real-time Runtime Control
Runtime control platform allowing real-time modifications to application behavior, configuration changes, and automatic recovery without redeployment
Production Observability and Experimentation
Real-time observability and experimentation capabilities for monitoring production behavior, measuring impact, and continuously testing variations
AI Agent Behavior Management
AgentControl solution for monitoring, observing, and automatically correcting AI agent behavior in production including prompt and model configuration adjustments
Automatic Rollback and Recovery
Automatic rollback mechanisms and self-healing capabilities to limit exposure and mitigate issues based on actual production impact
Feature Flagging and Deployment Control
Ability to set up feature flags and safely deploy to production, controlling which users see which features and when with zero downtime deployment capability.
Experimentation and A/B Testing
Support for A/B testing, canary releases, dark launches, and targeted rollouts to enable data-driven experimentation and feature validation.
Contextual Data Integration
Connection of feature flags to contextual customer data through Amazon S3 integration to enable seamless metric calculation and feature impact analysis.
Release Risk Mitigation
Reduction of cycle times and release risk through continuous integration/continuous delivery workflows and mean time to recovery optimization.
High-Volume Data Processing
Capability to serve feature flags to high-volume distributed systems, supporting more than 6 billion devices with reliable feature delivery at scale.
Feature Flag Management
Open-source feature flag platform enabling controlled feature releases and rollouts to manage deployment risk
Data Governance and Compliance Controls
Market-leading data governance, security, and compliance controls designed for enterprise-grade requirements including FedRamp and air-gapped deployment scenarios
Deployment Flexibility
Support for multiple deployment options including cloud-hosted private instances and self-hosted solutions
Developer Tools and Workflow Integration
Developer-focused tools for testing and deploying features to production environments with streamlined release process capabilities
Standard contract
No
No
No
Customer reviews
Giovanni Portogallo
Feature flags have simplified gradual rollouts and support safe checkout experiments in production
Reviewed on Sep 30, 2026
Review provided by PeerSpot
What is our primary use case?
My use cases for LaunchDarkly involve managing risk from new features such as AB testing and canary releases for features with gradual rollouts. This type of work is fundamentally about managing risk and testing new features.
I haven't used LaunchDarkly to manage any AI in production; the only thing I've used it for is feature flags for the most part.
What is most valuable?
What stands out to me about LaunchDarkly is that it's super easy to use. I appreciate that you can serve the feature at a percentage and that you can add users by email. For example, I used it in the past to do internal-only testing of the feature in production, which was very helpful.
From what I've seen, the management of feature flags in LaunchDarkly is pretty good. I don't have any other reference point to compare it to, but it's pretty simple to create a new flag and then edit the flag and increase or decrease traffic to that flag. I'm not an expert on feature testing, but for what I've used, it works for what I needed.
What needs improvement?
If LaunchDarkly goes down and you don't have it set up right, it could be a problem for you. I think that's a little bit on user error as well.
I would like some sort of a local instance to be available.
For how long have I used the solution?
I've been working with LaunchDarkly overall for at least a few years, probably around three years.
What do I think about the stability of the solution?
I've seen lagging, crashing, or downtime with LaunchDarkly. There was an outage, and that's what I was referring to. If there's ever an outage, I think you can define a default experience in your code, but I don't think we did that well. It might have reverted to an experience that we did not want the customers to see. For instance, my company developed a new version of our checkout page, and it was behind a LaunchDarkly flag, but it was serving 100% traffic to that flag. But when LaunchDarkly went down, it started using the prior checkout page, which is not as good of a page, which is why I'm saying that it's a problem if LaunchDarkly goes down.
What do I think about the scalability of the solution?
Regarding scalability, I think LaunchDarkly is pretty scalable. I've never had issues with it scaling.
Which solution did I use previously and why did I switch?
I've never actually used anything similar to LaunchDarkly, but I'm aware that there are competitors; however, LaunchDarkly is the only one that I've used.
How was the initial setup?
The initial deployment of LaunchDarkly was handled by a platform team that managed that initial setup, but as far as integrating it to my service, it's pretty easy to just create a LaunchDarkly client, much like any other type of client for any external software that you use, and then just go ahead and start using the flags.
What other advice do I have?
LaunchDarkly usually does not require any maintenance on my end. It's usually taken care of. The only time we have maintenance is if we're not using any flag anymore, or we make it a permanent feature instead of a feature flag; then we'd have to go into the code and clean it up to delete the flags and then delete it from LaunchDarkly. But in terms of maintenance outside of that, there isn't much required.
On a scale from 1 to 10, I would give LaunchDarkly an 8. I would definitely recommend it to another customer. It's always worked for me, except there have been outages here and there, but very rarely. I'd say it's pretty good for the most part.
Kirpalsinh R.
Emergency incident kill switches and SIEM-integrated deployment auditing
Reviewed on Sep 30, 2026
Review provided by G2
What do you like best about the product?
From a Security Operations Center (SOC) perspective, LaunchDarkly transforms how we handle active incident response and vulnerability containment. Typically, if a newly deployed feature is found to be actively exploited or leaking data, the SOC has to coordinate with engineering to initiate a full CI/CD pipeline rollback, which can take anywhere from 15 to 45 minutes. LaunchDarkly acts as an instantaneous emergency kill switch. We can immediately toggle off the vulnerable code path in milliseconds without deploying new code or restarting services. Furthermore, its native Data Export capabilities stream all feature flag evaluation and modification logs directly into our SIEM. This provides our security analysts with a definitive, real-time audit trail of exactly which developer toggled which feature in production, crucial for post-incident forensics.
What do you dislike about the product?
The platform easily facilitates the accumulation of stale feature flags, which inadvertently expands our application's attack surface. When developers forget to clean up old flags, it creates dormant code paths in production that could theoretically be exploited if an attacker manages to hijack an API key or manipulate client-side evaluations. Additionally, managing granular Role-Based Access Control (RBAC) across hundreds of microservices and thousands of flags is incredibly complex. Ensuring that junior developers don't have the permissions to toggle critical security or infrastructure flags in the production environment requires constant auditing and rigid policy enforcement.
What problems is the product solving and how is that benefiting you?
It drastically reduces our Mean Time To Contain (MTTC) during severe security incidents. Before integrating LaunchDarkly into our DevSecOps workflows, stopping a bleeding vulnerability required blocking IP ranges at the WAF or waiting for an emergency hotfix to compile and deploy. Now, our incident response playbooks include securely flipping LaunchDarkly kill switches, allowing us to instantly isolate vulnerable micro-features while keeping the rest of the application online and functional for users.
Pranjal E.
Safer releases, one customer at a time
Reviewed on Sep 29, 2026
Review provided by G2
What do you like best about the product?
The biggest value is shipping risky fixes to production turned off, then turning them on for one customer at a time across our many environments. Rolling back is a toggle, not a redeploy, so we ship faster with less risk. The REST API is excellent. We script almost all flag work and built a daily Slack digest on it to catch stale or partly rolled-out flags. Flags have become our release process, and the ROI shows up in emergency rollbacks we never had to do.
What do you dislike about the product?
Targeting is easy to get wrong. Individual targets are stored apart from rules, and new rules default to the "user" context kind, which can silently match nothing. Stale-flag cleanup is left to us, so we built our own digest. It's also hard to see what flag value a long-running worker actually evaluated. Warnings on rules that can't match and built-in stale-flag tracking would help a lot.
What problems is the product solving and how is that benefiting you?
It keeps a code fix separate from the decision to release it. We merge fixes to production turned off, turn them on for one customer to verify, then roll out everywhere. If something breaks, we turn the flag off instead of redeploying. We ship fixes faster, customer-specific issues can be tested safely in production, and bad releases have a much smaller impact.
Computer Software
Intuitive Feature Flags for Easy Testing and Key-Based Targeting
Reviewed on Sep 28, 2026
Review provided by G2
What do you like best about the product?
Easy to test out important software changes using flags. Interface is pretty intuitive and like having the option to decide based off different keys
What do you dislike about the product?
Would like more detail sometimes about exactly what was evaluated. What was sent to the LD client in order to give a particular evaluation and where it came from. The standard monitoring of number of false or true is ok, but sometimes if you are trying to debug it's not enough
What problems is the product solving and how is that benefiting you?
Reducing risk in critical software changes. Also, sometimes if we need to roll out a feature quickly and can't build the full flow for it in our system. It lets us roll it out for the required client
Mandeep Bhangu
Feature flags have enabled gradual rollouts and simple control of beta releases
Reviewed on Sep 28, 2026
Review provided by PeerSpot
What is our primary use case?
My use case for LaunchDarkly is for releasing features that we don't want to make available to customers immediately, or for beta testing. We put features behind feature flags and perform continuous deployment. Every week we deploy to production, but the whole feature is not available for clients, so we put it behind LaunchDarkly feature flags.
I have not used LaunchDarkly to manage AI in production, models, prompts, or agents.
What is most valuable?
What I like most about LaunchDarkly is the ability to create segments easily. You don't have to create rules repeatedly. You just add your specific requirements into the segment and proceed from there without reworking the entire flag or rules.
Over the period, I have seen measurable improvements in metrics such as evaluation scores when I implemented LaunchDarkly. They have made improvements, especially in enabling it globally and enabling it for a particular client, which makes a difference. Turning it off and turning it on was not a feature previously, but now it is much easier. If nothing is working, you can simply turn it off.
What needs improvement?
In the new UI where they have put production and related items next to each other, I find it is sometimes hard to determine if a feature flag is on in production or off. Looking for that specific information in the new UI was not pleasant for me.
For how long have I used the solution?
I have been working with LaunchDarkly for almost five or six years.
What do I think about the stability of the solution?
Regarding stability, it happened in four or five years of usage a couple of times that it was not syncing properly. Over the period, it got better.
What do I think about the scalability of the solution?
I would rate LaunchDarkly a seven in terms of scalability.
Which solution did I use previously and why did I switch?
I have not used anything similar to LaunchDarkly. We have been using this product for a long time.
How was the initial setup?
The initial setup was easy, and I was able to train my team. It took a couple of weeks and a couple of deployments for everyone to fully understand how to set it up for our company.
What other advice do I have?
I have been working in my current field overall for 15 or 16 years. The maintenance is taken care of by the team. I have not integrated any other tools with LaunchDarkly. I would give LaunchDarkly an overall rating of eight.