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    LaunchDarkly

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    Deployed on AWS
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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

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    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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    Deployed on AWS
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    Pricing

    Free trial

    Try this product free according to the free trial terms set by the vendor.
    Pricing is based on the duration and terms of your contract with the vendor. This entitles you to a specified quantity of use for the contract duration. If you choose not to renew or replace your contract before it ends, access to these entitlements will expire.
    Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator  to estimate your infrastructure costs.

    12-month contract (1)

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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

    AI Insights

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    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.
    launchdarkly.com+2
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    All fees are non-cancellable and non-refundable except as required by law.

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    Usage information

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    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.

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    Product comparison

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    Accolades

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    Top
    10
    In Business Intelligence & Advanced Analytics, Generative AI, Continuous Integration and Continuous Delivery
    Top
    50
    In Agile Lifecycle Management
    Top
    100
    In Testing

    Customer reviews

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    Sentiment is AI generated from actual customer reviews on AWS and G2
    Reviews
    Functionality
    Ease of use
    Customer service
    Cost effectiveness
    4 reviews
    Insufficient data
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    Positive reviews
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    Negative reviews

    Overview

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    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

    Contract

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    Standard contract
    No
    No
    No

    Customer reviews

    Ratings and reviews

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    4.5
    903 ratings
    5 star
    4 star
    3 star
    2 star
    1 star
    69%
    28%
    1%
    1%
    1%
    7 AWS reviews
    |
    896 external reviews
    External reviews are from G2  and PeerSpot .
    Luis Bravo

    Feature flags and AI prompts have enabled rapid config changes and faster experimentation

    Reviewed on Oct 20, 2026
    Review provided by PeerSpot

    What is our primary use case?

    I use LaunchDarkly for feature flags to turn on and off features that we deploy into production. I have also used LaunchDarkly to manage AI in production. We're using it for AI config, which are basically the prompts. So we are using it for flags and we also use it for the prompts, to store the prompts.

    How has it helped my organization?

    LaunchDarkly has helped me solve the challenge of iterating quickly on prompts and instructions. Having the instructions and prompts in LaunchDarkly makes it fast to iterate and just like flags, I can go into a UI and make the change, rather than having to deploy code, because the CI workflows tend to be a lot slower. We ended up wanting to have the instructions as code anyway, so we had to work on a specific pipeline to be able to still store that from our side as code and also publish it to LaunchDarkly. The main advantage is the same as the flag's flexibility—basically being able to change it quickly without having to actually wait for a PR to be merged and something to be deployed.

    What is most valuable?

    LaunchDarkly works well and it's nice to have the control of a feature flag as a whole. It's very handy. The only thing that I would like is a more structured way to visualize the flags, especially when you end up having too many flags and there is some hierarchy. The UI and the way that it is presented is very flat. When you end up having structures and dependencies on flags, there is no dashboard that would give you exactly what you want, especially when you're working on a particular project. Because the way that we have in our organization is that we have multiple products, multiple features and multiple teams that are working on features. You end up having a lot of different flags that are presented in a flat manner, and you may have a main one that when it is turned on, the child ones are evaluated based on it, and you may have many depths on that relationship. If there were a dashboard or a certain view that would make that structure apparent and very obvious, it would be useful.

    Also, because you have different values depending on whichever environment that you're interested in, you have to actually dig into what are the relevant flags and then go to a particular environment that you are focused on. If there would be some sort of dashboard that would give you all of that as a whole, not individually by flag, but as a whole structure is constructed and your interest checked on, it would be handy from the flag side.

    What needs improvement?

    I would like to see all of the flags and their relationships and I would like to see a tree type view. As a developer, I may be working on a particular piece from a particular team. If I'm able to just group them the right way based on the hierarchy of the flag and just select the particular feature that I'm working on and only see that and see all of the hierarchy, and also see all of the values based on the environment that I am interested in at a glance, would be very handy and that would save a lot of time.

    For how long have I used the solution?

    I have been using LaunchDarkly for maybe six months overall. I have been working in my current field for seventeen years.

    What do I think about the stability of the solution?

    There was one time that there was an outage, and we had to act on it because the default that we were getting actually ended up breaking some other feature.

    What do I think about the scalability of the solution?

    I am not aware of any performance issues, but I am aware of one time that there was an outage and we had to act on it to fix it.

    How are customer service and support?

    We have a thread in Slack where we are raising issues directly to LaunchDarkly support. I'm not really involved in that, but I do know that this thread exists.

    Which solution did I use previously and why did I switch?

    I have used an in-house solution previously. Comparing to that, LaunchDarkly is very feature rich, but it's basically because we were doing it ourselves. We are also using LaunchDarkly for AI configuration and that also seems intuitive, but it also ends up suffering from the same symptom of having everything be very flat. We have been having issues with certain variations of AI config being too large. There is a certain limit that LaunchDarkly imposes on how much you're able to post as a variation for an AI config. We have been having a lot of issues with that recently. We did a migration from the in-house solution to LaunchDarkly.

    How was the initial setup?

    I didn't actually do the platform part, but from my point of view, I was just moving existing flags to be in LaunchDarkly flags. I didn't do any work regarding that, but because it was already taken care of, I was just using the flags.

    What about the implementation team?

    There's a platform team that is taking care of the maintenance side, so it doesn't require maintenance on my end.

    What was our ROI?

    I wouldn't be in the position to actually compare metrics, but LaunchDarkly seems to be pretty performant. The client in the browser gets the events fairly quickly.

    What other advice do I have?

    I give LaunchDarkly a rating of eight out of ten overall for everything.

    Jonathan Barson

    Feature flags have enabled cautious rollouts and have supported fast, coordinated launches

    Reviewed on Oct 16, 2026
    Review provided by PeerSpot

    What is our primary use case?

    Regarding my use cases, I am cautious about breaking customers in production, so I generally put LaunchDarkly flags on pretty much any feature that is going to be rolled out to production to slowly roll things out and ensure nothing is broken. Additionally, it is valuable for developing a new feature and then launching it all at once when it is ready to go, so I do both of those.

    I am not using LaunchDarkly to manage any AI in production, as other teams may be doing that. At the moment, my use of AI is limited to coding assistance, while other teams are integrating AI into the product.

    What is most valuable?

    What I appreciate most about LaunchDarkly is that it is very easy to use. I estimate I have around 30 flags, though I have not counted them recently. Finding the flags is straightforward; I search for the flag I need and then proceed with the setup for whatever I need to do. One useful aspect for my workflow is that whenever I set a flag or change something, I ask my coding LLM to confirm that everything is working as it should, whether it is to catch a typo or ensure I did not add the wrong merchant ID.

    What needs improvement?

    What I dislike about LaunchDarkly is the inability to quickly find all the flags I have currently running and their statuses. I want to know if they are rolled out partially, but I have to check externally by asking my coding agent about all my LaunchDarkly flags and their statuses. Recently, I forgot about a flag I rolled out to a couple of merchants, and I noticed I was not alone; several people had flags running for months with only one or two merchants enabled. A feature to alert me if a flag is underutilized or needs attention after a week of being rolled out would be beneficial.

    For how long have I used the solution?

    Overall, I have been working with LaunchDarkly for a couple of years, having used it at Instacart and currently at Thanks, but it was not utilized at Thomson Reuters. I have a couple of years of experience with it.

    What do I think about the scalability of the solution?

    I have not encountered any scalability issues with LaunchDarkly. I work for a mid-sized company with hundreds or thousands of users, and I have not run into scalability issues here. While my experience at Instacart might have been different, I was not using it as in-depth as I do now. I currently use LaunchDarkly on a daily basis.

    What other advice do I have?

    I have not integrated any other tools with LaunchDarkly; it stands by itself in my usage.

    Other than the server that checks the status whenever I request it, which saves me from going directly to LaunchDarkly, I do not have additional tools integrated.

    I would give LaunchDarkly a rating of nine out of ten because I never give out tens, as that implies no room for improvement, and everybody can improve. Overall, LaunchDarkly is good; it is easy to use, and I can see what is happening relatively quickly.

    Computer Software

    Vega Unreliable: Slow Trace Loading, Missing Data, and Hard-to-Find Sessions

    Reviewed on Oct 16, 2026
    Review provided by G2
    What do you like best about the product?
    In some cases, I’m able to view logged API responses. Searching for sessions is also pretty straightforward, especially if you already know the time and the user you’re trying to find.
    What do you dislike about the product?
    Vega hasn’t been working for me at all. If you don’t know exactly who you’re looking for and when, sessions are very hard to find. LaunchDarkly flags can also be difficult to track down, including the rules and where they’re being served from. Often I can find a session, but then there’s no information in the trace data, so it’s practically useless. On top of that, loading traces is very slow and hit-or-miss.
    What problems is the product solving and how is that benefiting you?
    I usually review session replays to see what the API calls contain and check whether anything looks off.
    Figo c.

    Effortless Client-Specific Feature Flags with LaunchDarkly

    Reviewed on Oct 15, 2026
    Review provided by G2
    What do you like best about the product?
    The best thing that Launch Darkly lets us do is implement client specific requests and hide it behind a flag for other clients effortlessly.
    What do you dislike about the product?
    The manual part of setting flags by logging in the website.
    What problems is the product solving and how is that benefiting you?
    Gating client specific features from other users, it is helping in shipping code faster!
    Computer Software

    Fast, Flexible Feature Rollouts—with One Flagging Quirk

    Reviewed on Oct 15, 2026
    Review provided by G2
    What do you like best about the product?
    I got onboarded quickly and started using LaunchDarkly within a few days of discussing it. The UI is fairly simple, handles complex use cases well beyond what I need right now, and lets me enable a flag for a new user group in just a few clicks. The SDK is well thought out and easy to initialize, and it works across every coding language I’ve encountered in my professional career. Setup was rapid too; one pull request into our repository got the job done.
    What do you dislike about the product?
    I have to enable flags for individual SDKs, and I’d rather have every flag I create enabled for all the SDKs I use by default. This has bitten me several times and is probably my biggest pain point with LaunchDarkly. During setup, my biggest headache was getting SRE to add an environment variable for the LaunchDarkly token.
    What problems is the product solving and how is that benefiting you?
    I use LaunchDarkly to roll out features incrementally and control who gets them, avoiding the risk of production outages, bugs, or “big bang” rollouts that shock users with sudden massive changes.
    View all reviews