Sold by: Speedscale
Deployed on AWS
Understand your dependencies, autogenerate realistic mock environments, and simulate production load based on actual traffic.
3.5
Overview
Speedscale is a Production Traffic Replication platform that helps engineers build resilient cloud apps. Developers can simulate production conditions, generate load, or simulate 3rd party backends.
Highlights
- Preview kubernetes app performance and spot problems before release
- Autogenerate tests and mocks from sanitized user traffic
- Understand how your application is used by examining user traffic patterns and payloads
Details
Sold by
Delivery method
Deployed on AWS
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Pricing
Pricing is based on the duration and terms of your contract with the vendor, and additional usage. You pay upfront or in installments according to your contract terms with the vendor. This entitles you to a specified quantity of use for the contract duration. Usage-based pricing is in effect for overages or additional usage not covered in the contract. These charges are applied on top of the contract price. If you choose not to renew or replace your contract before the contract end date, access to your entitlements will expire.
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Dimension | Cost/month | Overage cost |
|---|---|---|
Price per 1 GB | $115.00 |
Dimensions summary
This listing uses a single usage-based dimension. You pay per 1 GB of traffic. Pricing scales directly with the volume of traffic you capture and replay. There are no tiers or instance sizes to choose from. Your cost rises or falls with how much traffic data you process. This structure suits teams that want billing tied to actual usage rather than a fixed commitment. The more traffic you ingest, the more units you consume.
Top-of-mind questions for buyers
You are billed per 1 GB of traffic data captured and replayed. The platform records full request and response payloads, headers, and timing, called Traffic Context. Each gigabyte of this ingested traffic counts toward your usage. The more traffic you capture and replay, the more units you consume.
Your bill scales directly with traffic volume. Capturing more gigabytes raises your cost; capturing less lowers it. There are no tiers or thresholds that trigger step changes. Each gigabyte is metered as you use it, so cost tracks your actual traffic recording and replay activity.
You can redact sensitive data and personally identifiable information on the fly using the data loss prevention feature. This anonymizes traffic while preserving its structure. Redaction keeps private data in your own environment. It affects what data is stored, not how the per-gigabyte metering works.
speedscale.com
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Delivery details
Software as a Service (SaaS)
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Updated weekly
By Speedscale
By Appvance
By ShadowTraffic
AI generated from product descriptions
Production Traffic Replication
Captures and replicates actual production traffic to simulate real-world conditions for application testing and validation.
Kubernetes Performance Preview
Enables preview of Kubernetes application performance and identification of potential issues prior to production release.
Automated Test and Mock Generation
Autogenerates tests and mock environments from sanitized user traffic data without manual test case creation.
Load Simulation
Simulates production load based on actual traffic patterns to evaluate application behavior under realistic conditions.
Traffic Pattern Analysis
Examines user traffic patterns and payloads to understand application dependencies and usage characteristics.
Generative AI-Driven Test Automation
AI Blueprint technology that autonomously navigates applications and generates thousands of test scripts within minutes, adapting to application changes without requiring manual maintenance.
Multi-Platform Application Testing
Support for testing websites, web-based applications, mobile applications, and platform-based applications including Salesforce and ServiceNow through autonomous and scripted approaches.
Self-Healing Test Automation
Machine learning-assisted test creation with fallback accessors that automatically adapt during test execution and self-healing capabilities to reduce test maintenance overhead.
Comprehensive API and Microservices Testing
Drag-and-drop test design for API-based functionality testing, with IDE support for advanced microservices, database, IoT, and multi-level dataset testing scenarios.
Parallel Test Execution with CI/CD Integration
Automatic test node scaling for massively parallel test execution with cross-browser capabilities, supporting data-driven scenarios triggered on-demand, on schedule, or via CI/CD pipeline integration.
Multi-Destination Data Generation
Supports data generation to multiple backends including Kafka, Postgres, SQL Server, local file systems, S3, Google Cloud Storage, Azure Blob Storage, and webhooks.
Composable Function Library
Includes a core library of over 50 sophisticated functions for declarative data generation using JSON API with composable function calls.
Relational Data Primitives
Generates data with relational primitives that maintain consistency across data sets by understanding relationships between datasets.
Traffic Profile Controls
Provides controls for volume, velocity, and variety to mimic statistical profiles of production traffic patterns.
Visual Debugging Interface
Includes ShadowTraffic Studio, a built-in visual debugger for monitoring and understanding data generation operations.
Standard contract
Customer reviews
ParthasarathyT
Replaying real traffic has improved deployment speed and testing but pricing still needs work
Reviewed on Jul 29, 2026
Review provided by PeerSpot
What is our primary use case?
My main use case for Speedscale is to mock the test cases, as usually the QA used to write the test cases, but when we are using Speedscale, it helps to replay the production grade traffic and incoming traffic for our service. It helps in terms of testing and validating our workloads in production grade scenarios.
Speedscale enables safer adoption for AI-generated code by allowing us to replay client traffic, which we can ideally use for our codebase where we are integrating the production in the lower environment. By integrating AI in the lower environment, we would be able to understand it and quickly use AI-driven codes and AI-driven setup without worrying about production bugs beforehand.
What is most valuable?
In my opinion, the best features Speedscale offers include faster deployment, detecting regression earlier, reproducing production bugs exactly, reducing downtime, improving customer experience, and enabling safer adoption for AI-generated code.
Speedscale has positively impacted my organization by improving deployment velocity, fostering faster releases, reducing revenue loss from outages, and enhancing customer satisfaction.
Since using Speedscale, the downtime that usually was 20 hours a week has now reduced to 8 to 10 hours, and we can anticipate quickly for those flaws.
What needs improvement?
I believe Speedscale can be improved in terms of costing, as it can be offered for only the larger and middle-scale industry, not the startup or small-scale industries.
Aside from costing, there are no improvements needed for Speedscale, as there is no technical limitation we have encountered for our environment.
For how long have I used the solution?
I have been working in my current field for beyond five years.
What do I think about the stability of the solution?
Speedscale is stable.
What do I think about the scalability of the solution?
Speedscale's scalability is quite good.
Speedscale scales well, as it adapts to our workflows. When we keep on adding the repo and our flows, it eventually adds it accordingly.
How are customer service and support?
The customer support is quite helpful and rapid and spontaneous in terms of supporting and response.
Which solution did I use previously and why did I switch?
I did not previously use a different solution, as this is our first solution.
How was the initial setup?
Speedscale is deployed in my organization on public cloud.
What was our ROI?
I have seen a return on investment, as we need fewer employees. We can have less QA in terms of writing the test cases where it does the job almost mocking the production grade traffic, so we can reduce the QA.
What's my experience with pricing, setup cost, and licensing?
My experience with pricing, setup cost, and licensing is that the pricing is quite aggressive, which is not affordable for the small scale industry and startups.
Which other solutions did I evaluate?
Before choosing Speedscale, we did not evaluate any options so far.
What other advice do I have?
Replaying production grade traffic has helped my team in deployment strategy. Usually people take 20 deployments a week per day. After using this, people are deploying it beyond 20 to 25 deployments per day. The second thing is production grade impact. Incidents may come at 20 or 25 in a week, but after using this, we can analyze them at 8 to 10, which is quite shortened.
Speedscale helps in analyzing production traffic and it helps to mock the production grade calls to our actual service. This helps for our QE team in writing the test cases by leveraging the base.
Regarding Speedscale's AI capabilities, the data has been saved securely, which helps us to track and audit with our internal people.
Regarding Speedscale's AI capabilities, the outputs were driven very accurately, and we did not find any flaws.
On a scale of one to ten, I would rate Speedscale seven out of ten because it is a risk analysis tool and not a security tool. It can mock production grade traffic, but it cannot mock production grade threats or vulnerabilities exactly. I chose seven out of ten for this reason.