Deployment
Designed for self-hosted, scoped per environment
Two deployment shapes — single-tenant on your infrastructure or multi-tenant SaaS. Either way: your CI/CD plugins, your identity provider, your LLM. Implementation timeline is sized to your environment, not a marketing number.
Two deployment shapes
Self-hosted (single tenant)
- For who
- Regulated industries: BFSI, healthcare, government. Anyone whose security policy bars sending API specifications to third-party services.
- Runs on
- Linux or Windows server you control, on-prem or in your cloud account. Connected to your internal network, IdP, and observability stack.
- Data posture
- API specs, credentials, prompts, generated tests, and audit logs all stay within your infrastructure boundary.
Multi-tenant SaaS
- For who
- Teams that don't need data residency or air-gapped operation. Faster onboarding, no infrastructure overhead.
- Runs on
- Hosted by us with subdomain-based tenant isolation and per-tenant database segregation.
- Data posture
- API specs and audit logs stored in tenant-isolated MongoDB; credentials AES-256-CBC encrypted at rest.
Stack at a glance
Backend
Node.js 20 + Express, MongoDB 7, Redis (ioredis). License + edition system gates features per tier.
Frontend
React 18 SPA (CRA + CRACO), MUI v7 + Tailwind CSS. Served by the same Node process or a separate Nginx front-end.
Local Runner
Bun-compiled native binary plus an Electron desktop wrapper (NSIS installer for Windows). Runs tests on the user's laptop without bouncing through the platform host.
LLM runtime (Enterprise)
Any OpenAI-compatible endpoint — Ollama (default localhost:11434), vLLM, LM Studio, or your internally-hosted inference service. Configured per-user or system-wide via AI Settings.
CI/CD plugins
Six first-party plugins, all calling the same /api/v1 contract: Jenkins (Maven), GitHub Actions, Azure DevOps (VSTS task), GitLab CI, CircleCI, Bitbucket Pipelines.
MCP server
Native Model Context Protocol server exposing six tools (generateTests, enrichTest, explainCoverage, generateForGaps, analyzeEndpoint, validateTests) over stdio. Drop-in for Claude, Cursor, and any MCP-compatible client.
Bring your own LLM
Self-hosted LLM inference is included on the Enterprise tier. Any of the runtimes below — or any OpenAI-compatible endpoint — works as a drop-in. Cloud providers (OpenAI, Anthropic, Azure OpenAI, Gemini, and 9 more) are available too, always bring-your-own-key.
Ollama
Default localhost:11434 endpoint. Most common pick for self-hosted local LLM serving.
vLLM
High-throughput inference server. Recommended when serving a centralized LLM to many users.
LM Studio
GUI-based local model server with OpenAI-compatible API.
Any OpenAI-compatible endpoint
Bring your own internally-hosted inference service. Configurable base URL, optional API key, and model namespace prefix (ollama/, local/).
Six first-party CI/CD plugins
Real plugins, not generic webhooks. Each integrates the test run lifecycle (trigger, poll, JUnit/JSON artifact), supports quality gates, and is published as a vendor-native artifact. A public REST API covers any CI system not on this list.
Jenkins
Java/Maven plugin with quality gates and JUnit/JSON artifact output.
GitHub Actions
Action with quality gates, artifact output, and signed-token authentication.
Azure DevOps
VSTS task (TypeScript) for Azure Pipelines.
GitLab CI
Node CLI plus container image for GitLab runners.
CircleCI
Node CLI orb-compatible pattern.
Bitbucket Pipelines
Node CLI for Bitbucket pipeline steps.
How an enterprise rollout works
Implementation is scoped per environment. The shape below is what we run for regulated-enterprise deployments. Timelines depend on your IdP, network constraints, and how aggressively your team wants to roll out CI/CD plugins.
- 01
Architect call (30 min)
A working call with the engineer who will run your deployment. Topology, identity provider, network egress posture, runtime constraints. We leave with your requirements and a draft architecture diagram.
- 02
Sandbox install
Stand up a non-production deployment matching your target topology. Validate AI provider configuration (cloud or self-hosted LLM), import a representative spec, generate and run tests end-to-end.
- 03
Security questionnaire + integration review
Your security team reviews the questionnaire response, deployment diagram, and reference architecture in parallel. We address gaps and confirm CI/CD integration points.
- 04
Production cutover
Production install, IdP integration where applicable, CI/CD plugin rollout, runner provisioning. Post-cutover, a dedicated success engineer (Enterprise) supports onboarding.
For data-flow specifics and identity controls, see the platform security page.
Plan your deployment with the engineer who'll run it
A 30-minute working call. Topology, IdP, network egress, runtime constraints — discussed, not pitched. We leave with your requirements; you leave with a draft architecture.