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    LaunchDarkly

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    Deployed on AWS
    Free Trial
    AWS Free Tier
    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.

    Details

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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

    Vendor refund policy

    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
    3 reviews
    Insufficient data
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    0 reviews
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    Mixed reviews
    Negative reviews

    Overview

     Info
    AI generated from product descriptions
    Feature Flag Management
    Industry-leading feature flags enabling runtime control for code and application behavior management
    Progressive Deployment and Rollout
    Progressive delivery capabilities with automatic rollback functionality for controlled software releases
    Real-time Observability and Monitoring
    Real-time observability and monitoring of production behavior for AI-generated code and agent performance
    AI Agent Control and Governance
    Runtime control for AI agents including prompt and model configuration, behavior monitoring, and automatic corrective actions without redeployment
    Production Experimentation and Testing
    Production testing capabilities with continuous experimentation and measurement of variations to optimize outcomes
    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 new features to production environments with streamlined release process capabilities

    Contract

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    Standard contract
    No
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    No

    Customer reviews

    Ratings and reviews

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    4.5
    753 ratings
    5 star
    4 star
    3 star
    2 star
    1 star
    70%
    27%
    2%
    1%
    0%
    4 AWS reviews
    |
    749 external reviews
    External reviews are from G2  and PeerSpot .
    Information Technology and Services

    Took the guesswork out of our releases

    Reviewed on Jun 17, 2026
    Review provided by G2
    What do you like best about the product?
    The biggest win for us has been how much control it gives the product and engineering teams without needing a deploy every time we want to change something. We use feature flags constantly to roll things out gradually, test with specific user segments, and kill a feature instantly if something looks off in the metrics. As someone who lives in the data, I appreciate that I can tie flag rollouts directly to experiment results instead of relying on engineering to ship a new build every time we want to iterate. The integrations with our existing stack (Slack, Datadog, Jira) make it easy to get visibility into flag changes without having to log into the platform constantly, alerts show up where the team already works. We've also started using some of the AI-assisted features for flag cleanup and identifying stale flags, which has saved us a decent chunk of manual auditing time as our flag count has grown across teams.
    What do you dislike about the product?
    Not much that frustrates me day-to-day. The native dashboards are decent, but we usually export data to view alongside our other product metrics for deeper analysis. Onboarding new team members on flag naming and project structure also takes a little hand-holding early on, just due to the flexibility of the tool. Minor friction points, nothing that's affected our decision to keep using it.
    What problems is the product solving and how is that benefiting you?
    Before LaunchDarkly, shipping a feature meant a full deploy and finger crossing, rolling back fast if something broke wasn't really an option. Now we ship code dark and turn it on for specific user groups whenever we're ready. For example, when we rolled out a new pricing flow recently, we turned it on for 5% of users first, watched conversion and drop-off for a few days, then ramped it up once we were confident it wasn't hurting anything. That kind of staged rollout would've been a nightmare to coordinate manually before. As an analyst, it's made my life easier too, I can trust that a rollout was clean instead of trying to untangle a messy, all-or-nothing launch.
    Information Technology and Services

    Effortless Targeted Rollouts, Experiments, and Audit Logs That Save Time

    Reviewed on Jun 11, 2026
    Review provided by G2
    What do you like best about the product?
    Honestly, the thing that's made the biggest difference for me is just being able to control who sees what without having to rope in an engineer every single time. We're juggling a lot of moving parts —HR, IT, payroll—and before LD, getting a feature in front of a specific set of customers meant coordinating deploys, syncing across teams, the whole thing. Now I can target a specific customer segment, set a rollout percentage, and go. The experimentation piece has also been really useful, I use it to gut-check whether something is actually working before we fully commit. And the audit log sounds boring but I reference it more than I expected to. Stakeholders ask "why did this change" and I can just show them. That alone has saved a few awkward conversations.
    What do you dislike about the product?
    Nothing major honestly. If I had to nitpick, the initial setup and the whole projects/environments structure takes a little getting used to, new people on the team sometimes need a bit of hand-holding at first. But once it clicks it's fine. More of a onboarding thing than an actual issue with the product. Overall it's been pretty smooth for us.
    What problems is the product solving and how is that benefiting you?
    The big one for me is that it's closed the gap between when something is built and when we actually feel confident putting it in front of customers. That used to be a stressful handoff, lots of back and forth, timing pressure, hoping nothing breaks. Now the flag is already in prod, we control the switch, and we can move at our own pace. I'll typically start with a small beta, maybe 10 or 15 customers, get some signal, and then expand from there. It also makes cross-team coordination easier. I can tell customer success exactly what's going out and when, without waiting on release notes or a deploy window. It's made the whole release process feel a lot less chaotic honestly. Less firefighting, more intentional shipping.
    James L.

    Clear Customer Journey Visibility with Powerful Filters and a Polished UI

    Reviewed on Jun 08, 2026
    Review provided by G2
    What do you like best about the product?
    I can view all aspects of the events in a customer’s journey. This is especially helpful when I need to find specific moments around an issue by using filters, such as the name of a button the user clicked. From there, it’s easy to extend the search to other customer journeys and see whether they encountered the same issue as well. Overall, this is straightforward to do because the UI is very well presented.
    What do you dislike about the product?
    It’s unfortunate that we’ve lost the ability to share individual session captures with external users. There are times when we want to provide customers with evidence of an issue or of a user’s actions, but in Launch Darkly this isn’t possible.

    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.
    What problems is the product solving and how is that benefiting you?
    Speaking from the position of the support team, Launch Darkly allows a singular platform that we can use to troubleshoot the customer and end user journey issues. It also provides the tools for our developers to enhance the level that we can do this via custom fields. It's integration with Linear makes it easy for Issues to be raised linked to the journey and the Dashboards allow a holistic view across the front end so we can be proactive with bug investigation and fixes.
    Computer Software

    Easy Setup and a Straightforward Learning Curve

    Reviewed on Jun 03, 2026
    Review provided by G2
    What do you like best about the product?
    LaunchDarkly is relatively easy to set up and has a straightforward learning curve compared to the other platforms we explored.
    What do you dislike about the product?
    So far, I haven't come across any major drawbacks, at least with the features I've worked with and explored.
    What problems is the product solving and how is that benefiting you?
    Most of my exploration has been around feature flags and user lifecycle–based experiment setup.
    Avi Cherny

    Feature flags have enabled safe gradual rollouts and now reduce risk and save engineering time

    Reviewed on Jun 03, 2026
    Review from a verified AWS customer

    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?

    Public Cloud

    If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?

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