RabbitQA vs Testsigma: A Practical Comparison for Enterprise QA Teams

If you’re evaluating modern AI testing tools, Testsigma makes a compelling case. It positions itself as the “#1 Unified Agentic Test Automation Platform”, and its AI brand is Atto - documented as “an AI coworker for QA teams that mobilizes a team of AI agents to autonomously plan, design, develop, execute, maintain, and optimize tests.” Its release confidence gate is a genuine idea, its combination of web, mobile, API and Salesforce coverage in one cloud-native product gives it real breadth, and unlike much of this market it offers an on-premise deployment option on the Enterprise tier.

That’s a substantive story. But the agentic era in enterprise QA raises questions that release confidence scores don’t fully answer: Where does quality governance start? And what happens after the release gate?

This comparison is designed to help you cut through the positioning and understand where each product genuinely delivers - and where the trade-offs begin to matter.

The short answer: Choose Testsigma for agentic test automation at real device scale with a quantified release confidence gate, accepting a migration of your test stack into its product. Choose RabbitQA to govern quality across the full lifecycle, from requirement validation and PBI generation before development to production availability monitoring after release, on top of the frameworks your team already uses. RabbitQA is a multi-agentic AI platform for digital product quality; Testsigma is an agentic test automation product.

RabbitQA Advantages

Requirements Governance - Quality Before the First Line of Code

Testsigma’s agents do what they set out to do. The Sprint Planner pulls Jira sprints and identifies key flows. The Generator creates test cases from user stories, PRDs, Figma designs, and session recordings. These are real capabilities that compress the time between requirement and test.

But Testsigma - like every test automation tool - starts from a story, a design, or a recording. Something that was already written. RabbitQA’s Business Agents start one step earlier: at the requirement itself, before it becomes a story, before development begins.

The Business Agents score every requirement against configurable quality metrics - clarity, completeness, acceptance and technical detail, plus your own - and surface weaknesses, missing sections and risk areas at the point where fixing them costs the least; in our delivery experience, requirement defects are among the most expensive sources of downstream rework, because the cost of fixing one rises with every stage it survives. They auto-generate acceptance criteria and produce testable backlog items, behind a configurable multi-step approval flow with approve, reject and reopen, named approvers and full history, so a requirement advances through a decision a person made and that is recorded. When a requirement changes, you regenerate the affected backlog items with a focused instruction and review the original against the revised version side by side, choosing which one carries forward. The Sprint Planner in Testsigma reads sprint context; the Business Agents in RabbitQA govern how that context got there.

This is a structural distinction, not a feature gap. It defines where the quality lifecycle begins.

One more thing about the Business Agents, because it changes who can use them. The flow is operated rather than programmed: a business analyst uploads a requirement document, reads the score and the flagged gaps, approves it, and watches backlog items and test cases come out the other side - without opening an IDE and without waiting on automation engineering capacity. The people who own the requirement are the people who can drive it. Automation engineers keep their frameworks and their repository; they are needed for the automation layer, not for the governance layer above it.

There is a second loop for change. Upload a new version of a requirement document and RabbitQA semantically diffs it against the previous version, asks what changed and why it matters, and proposes the regression coverage that change demands - each suggestion with its rationale and priority, in a set you review, reorder, approve or reject, and then materialise into real test cases.

The inputs are broader than a document, too. Backlog generation accepts an analysed document version, a direct upload, written scope, images read with vision analysis, a Confluence page, or Figma design context. Testsigma’s Generator reads Figma; so does RabbitQA - the difference is what happens next, since ours produces the backlog items and acceptance criteria development builds from rather than the tests that follow them.

A Multi-Agent Architecture Built from the Ground Up

RabbitQA’s multi-agent model was designed from inception as an agent-based operating model: AI agents do the quality work from requirements to production, and every decision stays with your team. Specialized agents, organized into three coordinated groups, own that work across the full lifecycle:

• Business Agents (SmartRequest, Analyzer, SmartPBI) - Govern demand intake. Score requirements for clarity, completeness, acceptance and technical detail. Auto-generate acceptance criteria and backlog items, behind a configurable approval flow.
• Planning Agents (CaseWriter, TestPilot, DataCrate) - Generate test cases from validated requirements (happy paths, edge cases, negative scenarios), manage test plans and test data, and provide real-time release readiness visibility.
• Technical Agents (AutoRunner, SmartAPI, HealthCheck) - Orchestrate parallel test execution, generate executable API test suites, and monitor production health.

Each agent feeds intelligence to the others. Together, they form a closed-loop quality system from requirements through production. Atto’s agents are capable within the test lifecycle - the documented ones generate tests, produce test data, analyse failures and file bugs - but they operate within the testing layer, not across the requirements and production stages that bracket it.

A note on agent counts, since the market is now competing on them. Testsigma’s homepage says 25+ agents run the loop. Its documentation has six agent pages, and as of today two of them - Optimizer and Coverage Planner - are pages whose entire content is the words “Coming Soon!”. Three agents the homepage names prominently, including the Sprint Planner, the Healer and the Executor, have no documentation page at all. The docs navigation still labels the whole thing “Atto (Beta)”. We are not saying the others do not exist; we are saying we could not find them documented, and that is the check we would apply to either vendor. Count the agents in the product rather than on the homepage.

Production Health Monitoring - Quality Doesn’t Stop at the Release Gate

Testsigma’s release confidence scoring does something real: coverage scores, pass rates, and a release gate that tells your team whether it’s safe to ship. That confidence number covers what was tested before deployment. Two things are worth reading closely before you build a process on it. First, the documented Coverage metric is not requirement coverage: it is Accepted divided by Accepted plus Pending across the AI-generated test cases, so it measures how much of the AI's output your team has approved, not how much of your product is tested. Second, the gate's effect differs between the documentation and the marketing pages. The docs describe a displayed verdict where CONDITIONAL GO “is allowed with explicit sign-off from a QA Lead”, while the marketing pages say “A BLOCKED gate stops the deploy”. The same three states are named GO, CONDITIONAL GO and NO-GO in the docs, READY, CONDITIONAL and NOT READY on the marketing pages, and SHIP, REVIEW and BLOCKED in the FAQ of one of those same pages. Ask which one your CI/CD pipeline actually enforces.

RabbitQA’s HealthCheck agent monitors live system behavior after that release gate closes. It runs scheduled availability checks against your production endpoints on an interval you set, evaluates status codes, response times and KPI criteria, opens incident records when a service stops responding - before users report it - and keeps status and response-time history with email, Slack or webhook alerting.

Testsigma - like most testing tools - treats deployment as the finish line. The confidence score tells you what you shipped; HealthCheck tells you whether it is still up. For teams where production incidents are a recurring cost, this distinction is operationally meaningful.

Framework-Agnostic - Additive, Not a Migration Project

Testsigma positions itself explicitly as a replacement for Selenium, Playwright and Cypress. Its Salesforce page is structured around migration use cases: “Replacing Selenium, Playwright, or Cypress?” That migration is part of the value proposition, and Testsigma supports part of it: Postman collections import with a documented mapping table, and manual test estates come in as CSV. It is worth knowing exactly how. Testsigma documents TestRail and Zephyr Cloud integrations, but they are linking-and-reporting connectors: you link an existing Testsigma test case to a TestRail or Zephyr case by ID, and push run results back. Bringing the estate itself across is a separate, CSV-based path — you export from the source tool and upload the CSV, and the documentation adds that “if your test cases are in other formats like .xlsx, convert the file to .csv”.

RabbitQA is additive. It works natively with Gauge, Cucumber, Selenium, Robot Framework, JUnit, TestNG, xUnit and Postman projects, alongside recorded and AI-generated suites, with Appium for mobile. RabbitQA layers governance, orchestration, and intelligence on top of what your team has already built. For QA engineers who have invested years building out a Gauge, Cucumber, or JUnit suite, their work carries forward - not into a migration project.

For teams with established open-source automation stacks and limited appetite for a tool migration, this distinction determines adoption cost.

Migration is not only an automation problem. Existing manual test estates come across too: TestRail connects with two-way case sync and result push, and XLSX imports run in chunks with auto-format detection and a mapping preview across title, section hierarchy, preconditions, steps, expected result, priority, complexity and labels. Once they are in, semantic duplicate detection scans the repository in three layers - exact match, near match, and semantic similarity - and groups what it finds for side-by-side review, resolution and undo.

Synthetic Test Data - Production-Independent by Design

The DataCrate agent generates synthetic test data on demand using a GAN-LLM approach: an LLM produces the seed data and CTGAN supersamples it, with column extraction flagging likely personal data so sensitive fields are generated rather than copied. Test environments hold no production personal data from the start - removing exposure risk under GDPR and sector-specific regulations, and eliminating the environment bottlenecks that delay release cycles.

Testsigma provides test data management capabilities, including data-driven execution with CSV, Excel and runtime variables, plus AI-based data generation through its Test Data Generator agent and built-in Data Generators. The overlap here is real; the difference is that DataCrate synthesises volume from a seed rather than driving tests from data you already have, so the environment can be built without production data in it at all.

API Test Generation - From Spec to Executable Suite in Minutes

The SmartAPI agent discovers services and endpoints from OpenAPI and Swagger specifications, Postman collections, HAR traffic captures, WSDL definitions, and raw cURL commands, then generates the full test layer: requests, assertions, captures, variables, and multi-step flows. Generated suites transfer into AutoRunner, so API coverage runs inside your CI/CD pipeline on every build.

Testsigma offers strong API testing capabilities: Postman and Swagger import, AI-based assertion generation from specs, end-to-end API plus UI test chaining, and self-healing for endpoint changes. The two products overlap here more than anywhere else in this comparison; the difference is source breadth - SmartAPI additionally ingests HAR captures, WSDL definitions, and raw cURL - and where the generated suite runs, since RabbitQA hands it to AutoRunner alongside your existing framework projects rather than keeping execution inside one product.

Audit-Ready Quality Governance - Traceable AI, Not a Black Box

In regulated industries, quality governance doesn’t end with a passing test run - it extends to demonstrating how quality decisions were made. DORA is the sharpest example: Articles 24 and 25 require in-scope EU financial entities to run a documented resilience testing programme covering end-to-end, performance and compatibility testing, to have tests performed by independent parties, and to prioritise, classify and remediate every issue those tests reveal, with internal validation that the gaps are closed. GDPR adds its own evidentiary expectations to how test data is produced and handled.

Requirement approvals, generated backlog items and test cases, review decisions and healing decisions each carry their own history - who acted, when, and on which record.

When the Business Agents approve or reject a requirement, when the Planning Agents generate test cases from it, when the Technical Agents record a failed run and the evidence behind it - that decision is traceable back to its source. Automation itself is versioned and immutable, with publish, restore and deactivate recorded as separate events. SmartRequest exports a configurable review package to PDF; backlog items, test cases and the repository export to Excel.

There is also a second human gate, on the artefact QA leads care most about. AI-generated test cases do not enter the repository on their own: they start in review, an admin or lead approves, rejects or reopens them individually or in bulk, regenerated and duplicated cases reset to pending rather than inheriting an earlier approval, and export to the main repository is blocked until they pass.

For enterprises in banking, insurance, and public sector navigating DORA’s ICT operational resilience mandates, RabbitQA’s traceability layer gives compliance teams the process record those frameworks ask for - who approved which requirement, on what evidence, and when - without reconstructing it from spreadsheets after the fact.

Open Architecture - Your AI Models, Your Context

Testsigma integrates with AI coding assistants - Claude Code, Cursor, Codex - as input sources. This means tests generated by AI coding tools can feed into Testsigma. That’s a useful integration.

Testsigma also documents bring-your-own-keys for four providers - Azure OpenAI, OpenAI, Gemini and Vertex - with a Gen AI Keys screen that maps each product feature to a specific key and model individually. Their own framing is “ensuring data sovereignty and privacy compliance” and giving you “control over data privacy, costs”. That is a real capability and more than most of this market offers.

One thing to establish before you rely on the default, though, because Testsigma’s own two pages do not agree. Its finance and SaaS industry pages describe the engine as a “Proprietary LLM (or BYOK)”. Its trust center publishes a different answer: seven subprocessors, three of them labelled AI - OpenAI, Anthropic and Google, all processing in the United States. Whatever “proprietary” describes there, the models underneath are third-party and American. That is not a criticism of using them; nearly everyone in this market does, ourselves included when you choose our capacity model. It is a reason to read the default carefully if your obligation is data residency rather than data protection, because BYOK pointed at your own Azure OpenAI region becomes the only documented route to keeping inference in the EU.

The difference is also scope and effect. RabbitQA’s providers include Anthropic and AWS Bedrock alongside OpenAI, Azure OpenAI and Google, per-agent defaults span the requirement, generation, data and accessibility stages, and on-premise deployments can run local models entirely inside your network. And bringing your own model changes the basis of the bill: licensing moves from capacity to a base licence plus per user, so your AI cost moves to your own provider contract. Testsigma’s documentation is silent on whether using your own key changes anything about what they charge - worth asking directly.

RabbitQA’s architecture works differently in one more respect. The Organization Configuration lets teams connect their own model keys and set a different default model per agent, so your own model operates inside a governed quality layer rather than feeding it from outside. A Company Knowledge Base (RAG) lets organizations upload internal documentation, which RabbitQA chunks and embeds and retrieves with hybrid keyword-plus-semantic search, so requirement intake is grounded in how your organisation actually works.

The distinction: Testsigma receives outputs from your AI tools and can run them on your keys. RabbitQA lets your AI become part of the quality process itself - and changes what you pay for when it does.

Two Pricing Models, and No Feature Gates

Enterprise AI testing has a pricing problem, and it has two halves. The first is the invisible meter: platforms in this category increasingly bill AI work in units they don’t disclose, so the first time you see what agentic testing actually costs is on an invoice, after adoption. Testsigma sits oddly on this axis and it is worth being precise. Its Test Management product is priced openly - $14 per user per month monthly, $8 annual, a free-forever tier and no seat minimum. Its automation platform publishes nothing: Pro and Enterprise are both quote-only, sold on parallel execution capacity rather than a metered AI unit. So there is no AI meter to worry about, and no automation price to plan with either. The second half of the problem is quieter and more expensive - feature gating. The capability you bought the platform for often sits one tier above the one you can afford. Testsigma’s pricing page lists accessibility testing on the Enterprise tier only.

RabbitQA is built the other way round. Every licence includes all nine agents. The three packages differ in capacity - how much agent work your team actually does - and in nothing else: requirements governance, test generation, synthetic data, API testing and production monitoring are in all of them. Three modules are priced separately in either licensing model, because each carries real infrastructure behind it: MobileHub for real devices, BrowserHub for the browser grid, and Accessibility.

Capacity is measured in work you can recognise: a requirement processed, an analysis run, a test case generated, a backlog item created, a suite executed. Not tokens, not compute units, not an “AI credit” whose exchange rate nobody will put in writing. And it is a fixed monthly capacity rather than an open meter, so you know the annual number before the year starts. Users are unlimited on this model: add your analysts, your product owners and your whole QA organisation without changing what you pay.

None of that rests on trusting us. Every AI call the platform makes records its input and output tokens, its cost, the operation behind it and how long it took, aggregated per session and across the platform. That is not what your bill is based on - your bill is fixed - it is simply visible.

The second model changes the basis of the bill. Connect your own OpenAI, Azure OpenAI, AWS Bedrock, Anthropic or Google account, in our cloud or inside your own network, and licensing moves from capacity to a base licence plus a per-user rate that falls as your team grows. Your AI cost moves to a provider contract you already negotiated and already govern.

RabbitQA Strengths at a Glance

Testsigma Advantages

Testsigma has built a capable product, and for certain team profiles and delivery contexts it is a well-designed one.

Release Confidence Scoring - A Genuinely Differentiated Idea

Testsigma’s release gate is a concrete capability: coverage score, pass rate, confidence percentage, and a release readiness indicator that tells the team whether it’s safe to ship - with the evidence behind the number. Four test plan types (Smoke, Feature, Regression, Deep Regression) feed the gate. The Coverage Analysis Agent identifies gaps in sprint coverage and flags what’s still missing.

This closes the loop on a real enterprise QA problem: the pre-release meeting where the QA lead says “I think we’re good” with no quantified evidence. Testsigma replaces that with a number and the data behind it. For teams whose primary challenge is communicating release readiness to stakeholders, this is a meaningful capability.

Real Device Scale, and an On-Premise Option

Testsigma runs tests in parallel across a large cloud grid of browser and OS combinations and real iOS and Android devices, from a single test script. Worth asking for in writing when you compare: the homepage says 2,000+ browser and OS combinations with 800+ real devices, the pricing page inverts those figures to 800+ browser/OS combinations and 2,000+ real mobile devices, and the global footer says 3,000+ device and browser environments. It supports native, hybrid and Flutter mobile applications, including single-script execution across iOS and Android, with CI/CD triggers from Jenkins, Azure DevOps and its REST API. And on the Enterprise tier Testsigma offers “Public, Private, or On-Prem Cloud Deployment”, with a full documented Docker Compose installation path - a genuine option that several vendors in this market do not have at all.

For teams with aggressive cross-browser and cross-device coverage requirements - particularly mobile-first organizations or those with Flutter applications - this infrastructure depth is a genuine advantage that requires significant investment to replicate with open-source tools.

Published Trust Posture

Testsigma publishes a subprocessor list that is public and specific, which several vendors in this market do not. If your evaluation gates on documented data handling rather than on architecture, that is a point in its favour.

Sprint-Aware Agentic Test Generation

Atto’s Sprint Planner automatically detects Jira sprints, pulls in upcoming stories, identifies key flows, and prioritizes test cases based on impact and past gaps. The Generator creates fully executable test cases from multiple input sources simultaneously: Jira tickets, Figma designs, GitHub PRs, PRDs, and session recordings. It also accepts inputs from AI coding tools including Claude Code, Cursor, and Codex.

The result is a test generation loop that is tightly coupled to the sprint cadence - not a separate testing workflow that runs alongside development, but one that reads the same sprint context developers are already working in. For Agile teams who want testing to stay in step with development rhythm, this integration model fits the way they work.

RabbitQA Gaps to Know

An honest comparison requires acknowledging where RabbitQA is still growing.

Release confidence scoring: Testsigma’s quantified release gate - coverage score, pass rate, confidence percentage, and sprint gap analysis - is a differentiated capability with no direct equivalent in RabbitQA’s current published offering. For teams whose primary stakeholder challenge is communicating release readiness with evidence, this capability matters.

Real device cloud scale: Testsigma’s device grid represents years of cloud infrastructure investment. RabbitQA’s MobileHub runs 150+ real devices on a WebRTC-native architecture with roughly half the latency of a conventional device cloud - competitive for most enterprise programmes, but not the same raw scale as a cloud-first vendor.

Published consumption detail: Testsigma publishes a per-user price for its Test Management product. We publish the unit of capacity but not a numeric rate card, and a buyer who wants to model a seat cost line by line before signing will find that specific figure on their pricing page and not on ours.

Testsigma Gaps to Know

No Requirements Governance

Testsigma does hold requirements, and more than we credited previously: the documentation describes a requirement object with Name, Description, Type, Start Date and Completion Date, created inside the test-case view and linked to test cases. The Sprint Planner reads Jira sprints and generates tests from user stories, PRDs and Figma files. What that object does not carry is any assessment of the requirement itself - no clarity or completeness scoring, no approval state, no named approvers - and nothing generates the acceptance criteria and backlog items development builds from. The upstream problem, vague or incomplete or conflicting requirements, in our experience a leading driver of project rework, remains outside Testsigma’s scope. It records what is in the sprint; it does not judge whether what is in the sprint is ready to build.

No Production Availability Monitoring

Testsigma’s confidence score tells you whether it is safe to ship. What it does not cover is what happens after you ship. Testsigma can schedule test plans and notify on pass or fail, which is scheduled test execution rather than monitoring: there is no availability or uptime service, no incident record with its own lifecycle, and no failure trend analytics against live endpoints in its published offering. The release gate closes; quality governance for what follows is outside the product.

The On-Premise Option Does Not Mention the AI

This one matters if your reason for wanting on-premise is regulatory rather than operational, and it is easy to miss because the deployment option itself is real and well documented. Testsigma publishes a full Docker Compose installation path - load balancer, ID, app, addon, audit and visual testing servers, MySQL 8, a Faktory worker, sized at sixteen cores and 64 GB with a separate database host. That is more than several vendors in this market offer at all.

What the documentation does not do is connect it to Atto. No page in the on-premise section - architecture, prerequisites, installation, post-install checklist or FAQ - mentions AI, GenAI or Atto. No Atto or BYOK page mentions on-premise. And the installation firewall whitelist is specific: \.docker.com, \.amazonaws.com, \.maven.org, plus \.sendgrid.com for email. There is no LLM provider domain on that list, and outbound access to those four is a prerequisite, so a fully disconnected installation is not described anywhere either.

We are not saying Atto cannot run on-premise. We are saying we could not find it documented, in either direction, and that a buyer whose regulator requires the AI inside the perimeter should ask Testsigma to confirm in writing which Atto agents operate there and against which model endpoint. RabbitQA’s answer to the same question is the one we can point at: on-premise deployment with a local model, where no prompt leaves the network.

Script Logic Has No Documented Converter

Testsigma positions itself as a replacement for Selenium, Playwright and Cypress, and it does import a good deal: Postman collections and manual test cases as CSV all come across, with the TestRail and Zephyr connectors linking cases and pushing results, while the estate itself comes across as a CSV export. What has no documented automated converter is script-based logic in Selenium, Playwright or Cypress - the recommended path there is recording and refining into Testsigma’s low-code format. For technical QA engineers who have built years of Playwright or Cypress competency, that is a methodology change rather than a migration, and the destination is Testsigma’s own format rather than the repository the project lives in today.

Testsigma Gaps at a Glance

The Core Architectural Difference: Sprint-to-Release vs. Requirements-to-Production

Testsigma is architected around the sprint. Its agents map the sprint, generate tests from it, execute them, heal breakages, score coverage, and call the release gate. It’s a well-closed loop - but the loop begins when the sprint is already defined and ends when the gate opens.

RabbitQA is architected around the full quality lifecycle. The loop begins before the sprint - at the requirement itself - and it ends after the release gate, in production. The Business Agents govern what goes into development. The Technical Agents monitor what comes out of it. The loop Testsigma closes, RabbitQA wraps.

RabbitQA or Testsigma: Which Is Right for Your Team?

RabbitQA is the stronger fit when:

• Your business analysts and manual testers should be able to drive quality work themselves, without waiting on automation engineering capacity
• Your team needs quality governance that starts at requirements, not at test creation
• You operate with Agile or CI/CD delivery and need quality that governs the full lifecycle from backlog to production
• Your QA engineers work in Gauge, Cucumber, Robot Framework, JUnit, TestNG, xUnit or Postman and you want to enhance - not replace - that investment
• Production availability visibility after deployment is an operational priority
• You need the AI itself inside your perimeter and you want that documented rather than confirmed in a sales call
• You operate in a regulated industry (banking, insurance, public sector) where DORA or GDPR traceability requirements extend into the quality process
• You want every capability in every tier, and the option to move the AI cost onto your own provider contract

Testsigma is the stronger fit when:

• Your primary challenge is communicating release readiness to stakeholders with quantified evidence, and a release confidence score addresses that gap directly
• You need aggressive cross-browser and cross-device coverage at scale - particularly for mobile-first teams or Flutter applications
• You are ready to migrate away from Selenium or Playwright and want one product that handles the full testing lifecycle without maintaining a separate framework
• Sprint-aware test generation tightly integrated with Jira and AI coding tools (Claude Code, Cursor) is a primary workflow requirement
• Your procurement process gates on SOC 2 or CSA Star specifically

Frequently Asked Questions

Is RabbitQA an alternative to Testsigma?

Yes, for teams that want agentic AI across the full quality lifecycle rather than the test layer alone. RabbitQA governs requirements before development, generates and runs tests, and monitors production availability after release, while keeping existing Gauge, Cucumber, Selenium, JUnit, Robot Framework or Postman assets in place.

What is the main difference between RabbitQA and Testsigma?

Scope. Testsigma’s agents operate from sprint planning to a release confidence score. RabbitQA starts earlier, validating requirements and generating PBIs before development, and ends later, monitoring production availability after deployment, with every action logged with the user who took it, when, and what changed.

Which models does each platform actually run on?

Ours are yours if you want them: OpenAI, Azure OpenAI, AWS Bedrock, Anthropic or Google on your own keys, a different default per agent, or a local model on-premise. Testsigma documents BYOK for Azure OpenAI, OpenAI, Gemini and Vertex. On its default path the picture is less clear: its industry pages describe a “Proprietary LLM”, while its trust center lists OpenAI, Anthropic and Google as AI subprocessors, all processing in the United States. If EU data residency is a requirement, that is worth resolving with Testsigma before you rely on the default.

Can Atto run in an on-premise installation?

Testsigma documents a full on-premise deployment and separately documents Atto, but no page connects the two: the on-premise section never mentions AI or Atto, the Atto section never mentions on-premise, and the install whitelist contains no LLM provider domain. We could not answer this from public documentation and would not guess on Testsigma’s behalf - ask them to confirm in writing. RabbitQA runs on-premise with a local model, which is the configuration we document and support.

What happens to our existing Selenium, Playwright or Cypress suites with each platform?

Testsigma positions itself as a replacement: its Salesforce page asks “Replacing Selenium, Playwright, or Cypress?” and targets migration. It imports Postman collections and manual test cases as CSV, and documents TestRail and Zephyr connectors that link cases and push results, but there is no documented automated converter for Selenium, Playwright or Cypress script logic. RabbitQA takes the opposite approach - it is additive and runs your Gauge, Cucumber, Selenium, Robot Framework, JUnit, TestNG, xUnit and Postman projects where they already live.

Verified against publicly available vendor documentation on 26 August 2026. Products in this category change quickly; if you believe we have described your product incorrectly, tell us and we will correct it. RabbitQA capabilities described here reflect our shipping product.

Product and company names mentioned are the trademarks of their respective owners.