promptfoo vs DeepTeam: when each one fits
Choose between these two when you need open-source red teaming that maps findings to OWASP and NIST AI RMF, and the real split is a declarative Node CLI now owned by OpenAI against a Python API built on DeepEval. promptfoo fits a team that wants tests written as YAML configs, run from a terminal or a pipeline job, and judged by a tool that is MIT licensed and, per its documentation, runs completely locally. DeepTeam fits a team whose red teaming already lives in Python, where attacks and judgments are written as code and the tests sit next to the application's own test suite. promptfoo is the stronger default when the first goal is a release gate that a non-Python team can read and change. DeepTeam is the stronger default when security engineers want to script custom vulnerabilities and multi-turn attacks in the same language as the application.
Confirm the language decision first. promptfoo's CLI requires Node.js 22.22.0 or newer, per its npm release, and DeepTeam runs on Python versions from 3.9 up to, but not including, 3.14, per PyPI. Both need a model API key for attack generation and judging, so the recurring model cost is similar in kind for each and should be checked against your own provider terms. Ask each vendor which judge model it uses by default and whether you can pin it, because a changed judge changes your pass and fail results.
Then decide how deep agent testing must go and what sits above the open-source layer. promptfoo's documented setup covers CI/CD pipelines, and its paid Enterprise tier adds team sharing, SSO, continuous monitoring and a centralized guardrail dashboard, with On-Premise available for your own infrastructure. DeepTeam lists agentic vulnerabilities and the OWASP Top 10 for Agents 2026 mapping, and it connects to Confident AI's commercial platform for risk assessment tracking and report sharing. Confirm with each vendor which agent scenarios your application needs, and whether a commercial layer is required for your governance, not only for convenience.
Which platform fits which scenario in promptfoo vs DeepTeam
This table matches common team situations to the tool that fits them better, with the specific reason and the condition that would flip the choice. Read each row against your own language stack, CI system and deployment constraints, then confirm the open points with each vendor before you commit.
| Scenario | Better fit | Reason and condition |
|---|---|---|
| Node team gating releases in GitHub Actions | promptfoo | promptfoo is a Node CLI driven by declarative configs that can run in CI/CD pipelines. DeepTeam is a Python API, so a Node team would add a Python runtime. DeepTeam becomes the better choice if the gate must be written as Python tests. |
| Python team with existing pytest suites | DeepTeam | DeepTeam is a Python framework built on DeepEval, so red team checks can sit alongside existing Python test code. promptfoo is driven by config files and a Node CLI, which adds a second toolchain to maintain. |
| Team needing agent vulnerability coverage | DeepTeam | DeepTeam's README lists agentic vulnerabilities among its categories and covers single-turn and multi-turn attacks, and it maps tests to the OWASP Top 10 for Agents 2026. Confirm the agent-specific plugins you need in promptfoo's red teaming documentation before choosing promptfoo for this scenario. |
| Team needing one shared dashboard across teams | promptfoo | promptfoo Enterprise adds team sharing, RBAC, SSO and a centralized guardrail dashboard under custom pricing. The free self-hosted server is not recommended for production and has no RBAC or SSO, so this need points to a commercial tier. DeepTeam pairs with Confident AI's commercial platform for report sharing, so compare that offering with promptfoo's before deciding. |
| Air-gapped or data-isolated deployment | promptfoo | promptfoo On-Premise runs Enterprise functionality on your own infrastructure with complete data isolation and a dedicated deployment engineer. DeepTeam also runs locally, but it still needs a model API key to generate attacks and judge responses, so confirm which model endpoints your network can reach. |
| Team that needs a documented OWASP and NIST mapping for reports | Either, verify in practice | Both tools map tests to the OWASP LLM Top 10 and the NIST AI RMF. DeepTeam also lists MITRE ATLAS, BeaverTails and Aegis out of the box. Ask each vendor to show the mapping for your specific control set before you rely on it. |
| Team that expects to run the tool long term without a vendor platform | promptfoo Community or DeepTeam | promptfoo Community is free and includes red teaming with 10k probes per month, with community support. DeepTeam is Apache-2.0 and runs locally. Confirm probe volume and model costs in your own environment, since model API usage is billed to you in both cases. |
| Team that needs runtime guardrails in production | DeepTeam | DeepTeam includes 7 guardrails for fast binary classification of inputs and outputs, including PromptInjectionGuard and PrivacyGuard. promptfoo's documented focus is evaluation and red teaming, with guardrail management in its Enterprise dashboard, so a separate runtime library may be needed if you rely on promptfoo alone. |
How promptfoo and DeepTeam are built and what that means for operations
The two tools differ at the foundation: promptfoo is a Node.js CLI and library with declarative configs, while DeepTeam is a Python API built on DeepEval. Each one runs on your machine, but each depends on a model API key and has a different path to a shared, governed deployment. The cards below cover what a platform team must install, maintain and check before either tool joins a pipeline.
Runtime and language
promptfoo runs as a Node.js CLI and library, so the platform team must provide Node.js 22.22.0 or newer on build agents and developer machines. DeepTeam is a Python package supporting Python 3.9 up to, but not including, 3.14, so the team must own a supported interpreter and a pinned virtual environment. Check which runtime your pipeline images already ship before choosing, because a second runtime adds a second patching and upgrade path.
Local execution and model keys
Both tools run locally, and promptfoo states that evals run on your machine and talk directly with the LLM. DeepTeam needs a model API key such as OPENAI_API_KEY, or a custom model that DeepEval supports, to generate attacks and judge responses. Each run therefore sends test prompts and responses to a judge model, so the team should decide which judge model is approved, where its key lives in the secrets store, and whether the judge endpoint meets its data handling rules.
Self-hosted promptfoo storage
The self-hosted promptfoo app is an Express server with a web UI and API, deployable with Docker, Docker Compose or Kubernetes with Helm. Its data sits in a local SQLite database that cannot be shared across replicas, and it has no RBAC, horizontal scaling, built-in authentication or SSO. promptfoo's own documentation says it is not recommended for production use, so a platform team running it for shared results should plan for a single instance, backups of the database file and an access layer in front of it.
Result and report handling
Scan output decides who can see vulnerabilities, so the team should define where results land before the first run. A promptfoo CLI run writes results to the machine or pipeline that executed it, while self-hosted sharing is limited by the SQLite design described above. DeepTeam produces pass or fail results per vulnerability through its Python API, so the team must choose a retention location, an access rule and a process for reviewing failed attacks that contain sensitive prompts.
Commercial layer attachment
promptfoo's commercial path is Enterprise, which adds team sharing, SSO, custom roles, continuous monitoring and a centralized guardrail dashboard, and On-Premise, which runs those features on your own infrastructure. DeepTeam connects to Confident AI's commercial platform for risk assessment tracking, production vulnerability monitoring and report sharing. Ask each vendor which features are included at each tier, where data is processed and what the pricing basis is, since neither price list is a substitute for a written quote.
Upgrades and dependency ownership
Both projects release frequently, so the platform team must own version pinning for each. promptfoo is installed from npm and released on GitHub, which means lockfiles and Node.js version pins matter. DeepTeam is installed from PyPI and currently at version 1.0.10, which means the team must track DeepEval compatibility as well. Confirm the upgrade cadence, changelog quality and rollback steps with each maintainer before the tool becomes a release gate.
Side-by-side capability check for promptfoo and DeepTeam
This table compares promptfoo and DeepTeam on attack coverage, agent testing, guardrails, CI fit, licensing, identity and the commercial layer around each. Read each row as a question to answer for your own environment, since several cells mark items you should confirm with the vendor before you decide.
| Capability | promptfoo | DeepTeam |
|---|---|---|
| Framework alignment | Red team plugins and strategies aligned with the OWASP LLM Top 10 and the NIST AI Risk Management Framework, and documented as supporting EU AI Act testing | Maps tests to the OWASP Top 10 for LLMs 2025, the OWASP Top 10 for Agents 2026, the NIST AI RMF, MITRE ATLAS, BeaverTails and Aegis out of the box |
| Attack and vulnerability coverage | Plugins and strategies generate adversarial inputs; confirm the current plugin list for your target categories in the red teaming documentation | More than 50 ready-to-use vulnerabilities and more than 20 research-backed attack methods, covering single-turn and multi-turn attacks |
| Agent testing | Red teaming covers LLM applications; confirm agent-specific plugins and how tool-use flows are exercised in your configuration | Lists an agentic vulnerability category and a Top 10 for Agents mapping; confirm depth of agent tool and multi-step testing with Confident AI |
| Runtime guardrails | Enterprise adds a centralized guardrail dashboard; the open-source CLI focuses on pre-release evaluation and red teaming | Includes 7 guardrails for fast binary input and output classification in production, such as PromptInjectionGuard, PrivacyGuard, ToxicityGuard and CybersecurityGuard |
| Judging method | Evaluations run locally and call the model directly; confirm the grader model and how pass or fail is decided for each plugin | Each vulnerability is scored pass or fail by an LLM-as-a-judge metric, which needs a model API key such as OPENAI_API_KEY or a custom DeepEval model |
| CI and pipeline use | Runs checks in CI/CD pipelines from declarative configs; confirm the pipeline setup and exit behavior you need | Python API built on DeepEval that runs locally; confirm how you would wire red team runs into your pipeline and where results are stored |
| License | MIT license | Apache-2.0 license |
| Language and runtime | Node.js CLI and library; version 0.124.1 requires Node.js 22.22.0 or newer | Python library; version 1.0.10 supports Python 3.9 up to but not including 3.14 |
| Identity and access in self-hosting | Self-hosted app has no built-in authentication, SSO or RBAC and is not recommended for production; SSO and roles sit in Enterprise tiers | Confirm with Confident AI and the project whether any identity, SSO or role controls exist outside the local library and commercial platform |
| Commercial platform | Promptfoo Enterprise and On-Premise add team sharing, SSO, custom roles, continuous monitoring and priority support at custom pricing | Confident AI's commercial platform integrates for risk assessment tracking, production vulnerability monitoring and report sharing; confirm pricing and hosting options |
What is free and what is paid in each platform
promptfoo is an MIT-licensed CLI and library for evaluating and red teaming LLM applications. Its README states that it is now part of OpenAI and remains open source and MIT licensed, and the founders have said it will continue to support a diverse range of providers and models. The software runs locally: evals execute on your machine and talk directly to the model you configure, so the license covers the tool itself while model API charges come from your provider.
- MIT license: You can use, modify and redistribute the core tool under MIT terms, with no paid tier required to run evals or red team scans.
- Local execution: Configs, prompts and results stay on the machine or runner where the CLI runs, which matters for teams with data residency limits.
- Model costs stay separate: Attack generation and grading call an LLM, so provider token charges recur with every scan.
- Ownership change: The OpenAI acquisition is announced, and the license is stated as unchanged, so confirm the current terms in the repository before you standardize on it.
Confirm the license text and any future change to the open-source commitment directly with the vendor before you write it into procurement documents.
The promptfoo Community edition is free. It includes all LLM evaluation features, all model providers and integrations, and red teaming with 10k probes per month, with support through the community. For most engineering teams testing a single application, this is the tier to evaluate first, because the attack coverage and CI integration are not gated behind Enterprise.
- Free evaluation features: Evals, all supported providers and integrations are included at no license charge.
- 10k red team probes per month: This is the monthly allowance on the Community tier, so a large scan schedule may exceed it.
- Community support: Help comes from public channels and documentation, with no contracted response times.
- Single-user operation: The Community tier does not include team sharing, RBAC or SSO, so shared dashboards are a paid concern.
- Probe planning: Count how many red team runs per month your pipelines, model variants and regression cycles will produce before you rely on this allowance.
Ask the vendor to confirm how probes are counted per run, per plugin and per strategy before you size a pipeline against the 10k limit.
promptfoo Enterprise is the paid, vendor-operated tier aimed at organizations that need shared work across teams. The pricing page lists team sharing, teams-based access control and SSO, custom roles and permissions, continuous monitoring, a centralized guardrail dashboard, remediation recommendations and priority support with SLAs. Pricing is custom, so the cost depends on the scope agreed with the vendor.
- Team sharing and access control: Teams-based access control, SSO and custom roles address who can see and change evaluation results.
- Continuous monitoring: Ongoing monitoring is listed as an Enterprise feature, separate from one-off CI runs.
- Centralized guardrail dashboard: A single view of guardrail status across applications is included in this tier.
- Priority support with SLAs: Response commitments are part of the contract, so ask for the exact SLA terms in writing.
- Custom pricing: No public price is published, so the quote reflects seats, usage and support level.
Ask for the contracted SLA text, the usage limits that apply to scans, and whether SSO and RBAC are available in the deployment model you will use.
promptfoo On-Premise runs the Enterprise functionality on the customer's own infrastructure with complete data isolation and a dedicated deployment engineer. It is the commercial path for teams that need Enterprise features but cannot send scan data to a vendor-hosted service. It differs from the free self-hosted server, which the documentation says is not recommended for production.
- Customer infrastructure: Enterprise functions run in your environment, which supports data isolation requirements.
- Dedicated deployment engineer: A named engineer is included in this tier, so ask what hours, scope and handover are covered.
- Not the free self-hosted server: The self-hosted app uses local SQLite, has no RBAC or built-in SSO, and cannot scale horizontally, so it does not replace this tier for production.
- Custom pricing: Costs cover the license, deployment engineering and any infrastructure you provision, and none of these figures are published.
- Recurring and one-time costs: Deployment effort is typically one-time, while license and support recur. Confirm the split in the quote.
Ask the vendor which components are delivered, how upgrades are handled and what the deployment engineer's role ends with.
DeepTeam is an Apache-2.0 open-source framework from Confident AI for red teaming LLM systems and AI agents. It runs locally, is built on DeepEval, and needs a model API key such as OPENAI_API_KEY or any custom model DeepEval supports to generate attacks and judge responses. Confident AI's commercial platform integrates with DeepTeam for risk assessment tracking, production vulnerability monitoring and report sharing.
- Apache-2.0 license: The framework is open source under a permissive license, so you can run it without a license fee.
- Model costs recur: Attack generation and LLM-as-a-judge scoring consume your own model API budget on every run.
- Confident AI platform: Risk assessment tracking, production monitoring and report sharing are offered through the commercial platform, which the README describes as an integration.
- Commercial terms not supplied: Tiers, prices, seat limits, SLAs, hosting options and support levels for the platform are not established here.
- Scope of the platform: Confirm whether the platform can run on your own infrastructure and where stored results live.
Before you plan a DeepTeam rollout, ask Confident AI for its current plans, data hosting options, access controls and support terms in writing.
What drives the running cost of each tool
The largest recurring cost in both tools is usually model API usage, not the software itself. promptfoo and DeepTeam both call a model to generate attacks and, in DeepTeam's case, to judge every response against a vulnerability metric. DeepTeam's README states that it needs a model API key such as OPENAI_API_KEY, or any custom model DeepEval supports, to generate attacks and judge responses, so every red team run bills tokens to whichever provider you choose. Ask any vendor or partner quote to separate the attack generation model, the judge model and the target model, because each is paid for on its own meter. Scan size is the main lever: the number of plugins, the number of attack methods per plugin and the number of multi-turn conversations all multiply the calls. The promptfoo Community edition includes red teaming with 10k probes per month, which caps probes on that tier but does not cap the model calls your own provider bills for the target.
Engineer time is the second component and mostly one-time. In promptfoo, the work is writing and reviewing declarative YAML configs, wiring the CLI into CI/CD, and deciding which OWASP LLM Top 10 and NIST AI RMF plugins apply. In DeepTeam, the work is Python test suites built on DeepEval, which usually cost more to set up and more to maintain as code grows. Both tools have recurring upgrade work. promptfoo's version 0.124.1 was published on October 8, 2026 and requires Node.js 22.22.0 or newer, so Node runtime changes can land on your build agents. DeepTeam 1.0.10 supports Python 3.9 up to but not including 3.14, so a Python upgrade may force a dependency review. Confirm in the quote who owns these upgrades and how often they are scheduled.
Infrastructure and storage matter only if you self-host the promptfoo app. The self-hosted server stores data in a local SQLite database that cannot be shared across replicas and has no RBAC, horizontal scaling, built-in authentication or SSO, and the documentation does not recommend it for production. Moving to a production-grade design means adding a shared datastore, identity integration and a supported deployment on Docker, Docker Compose or Kubernetes with Helm, and each of those is an extra cost. promptfoo On-Premise and Enterprise add SSO, RBAC and continuous monitoring at custom pricing, so ask for those line items separately. DeepTeam has no server to run, but Confident AI's commercial platform for risk tracking and report sharing is optional and priced separately. For either tool, ask for a written scope that names the model providers, scan volume, environments, storage location and the upgrade and support window.
Moving a red team suite from promptfoo to DeepTeam or back
This sequence shows a team which parts of an existing red team suite carry over between promptfoo and DeepTeam, which parts must be rebuilt, and how to confirm the new suite reproduces the old findings before CI relies on it.
Inventory configs, plugins and strategies
The engineer who owns the promptfoo configuration lists every target, red team plugin, strategy, custom policy and test threshold in the config files, and records which CI job runs each one. Keep the inventory in version control next to the configs. The step is done when every plugin and strategy in use appears once in the list, with the target it runs against and the pipeline that triggers it. Anything left out here will be missed in the rebuild.
Map each check to a DeepTeam equivalent
For each inventoried plugin and strategy, the security engineer checks the DeepTeam README and documentation for a matching vulnerability or attack method. DeepTeam lists more than 50 vulnerabilities and more than 20 attack methods, and maps tests to the OWASP Top 10 for LLMs 2025, the OWASP Top 10 for Agents 2026 and the NIST AI RMF out of the box. Mark each item as a direct match, a partial match or no match. Items without a match need a custom vulnerability or a documented gap, and the team should record a named owner for each one.
Rebuild judge and model settings
Promptfoo and DeepTeam both use a model to generate attacks and judge responses, so the team must choose the attack generator, the judge model and the API keys again in DeepTeam, which runs on DeepEval and needs a key such as OPENAI_API_KEY or a custom model that DeepEval supports. Confirm which model the promptfoo config used for grading and whether the new judge is comparable. The step is done when a written note records each model, its provider and where the key is stored, and the team agrees the judge is acceptable for the release gate.
Rewrite the suite as a Python harness
A developer translates the declarative YAML targets and test settings into a Python script that calls DeepTeam's red teaming API against the same application endpoint. Targets that promptfoo reached through a provider definition need a matching callback in Python. The step is done when the script runs locally, completes against a non-production target and writes results that a reviewer can read. Keep the promptfoo config in the repository until the comparison in the next step is finished.
Rerun baselines and compare findings
The team runs the old promptfoo suite and the new DeepTeam suite against the same frozen build of the application, then compares pass and fail results category by category. Differences are expected because the attack generation and judge models differ, so each mismatch should be reviewed by a person and logged with a decision. The step is done when the team signs off on the mapping and has a baseline that future releases will be measured against. If promptfoo findings were exported, keep them alongside the new results for audit.
Update CI jobs and gating rules
The DevOps engineer replaces the promptfoo job in the pipeline with a job that installs DeepTeam, sets the model key from the secrets store and fails the build on the agreed conditions. Existing promptfoo CI settings such as its failure flags and JUnit output do not carry over, so the new job needs its own exit code handling and test report format. The step is done when a test branch triggers the job, the build fails on a deliberately vulnerable target and passes on the baseline, and the old promptfoo job is disabled only after both pass.
Rebuild dashboards, access and reporting workflows
The team lead identifies who uses promptfoo's web interface, shared results, roles or single sign-on, and decides how those users will see DeepTeam results instead. Promptfoo's team sharing, access control and SSO are part of its paid Enterprise tiers, while DeepTeam's commercial integration from Confident AI covers risk assessment tracking, production monitoring and report sharing. Confirm in writing what each platform's paid tier includes before moving, because access rules, report history and approval workflows are often the hardest parts to recreate.
Where red team implementations run long
For DeepTeam, the longest work sits in the judge and the API keys, not the attack list. Each vulnerability category is scored pass or fail by an LLM-as-a-judge metric, so the judge model you choose decides what counts as a failure. Assign a named owner to select the judge, hand-check a sample of verdicts against your acceptance criteria, and record that calibration before any CI gate depends on it. Keys are the second decision. DeepTeam needs a model API key, such as OPENAI_API_KEY or a custom model that DeepEval supports, to generate attacks and judge responses, so attack prompts and target responses pass through that provider. Confirm with the provider and your data protection team which endpoints are approved, where they process data, and who controls the keys in your secrets store.
promptfoo's cost shifts toward hosting and access control once results must be shared. Its documentation says evals run on your machine and talk directly with the LLM, so a developer workstation or a CI runner can execute a scan with no server at all. The friction starts when a team wants a shared dashboard. The self-hosted app is an Express server backed by a local SQLite database that cannot be shared across replicas, has no RBAC, horizontal scaling, built-in authentication or SSO, and is not recommended for production use. Teams that need those controls either build them around the server or evaluate promptfoo Enterprise or promptfoo On-Premise, which add SSO, teams-based access control and a dedicated deployment engineer. Both are custom-priced, so write the identity provider, hosting target and support expectations into the scope before requesting a quote.
On both sides, the client has to supply the same inputs before work starts. Name one owner for test scope and the judge, one owner for model access and spend, and one owner for each test target, meaning a staging endpoint with the tools and data an agent actually uses. Then get sign-off criteria in writing: the pass threshold for the CI gate, which OWASP LLM Top 10 or NIST AI RMF categories block a release, and who approves exceptions. Ticketing and SIEM integration is where schedules most often slip, because each tool's result format must be mapped to your queue and log schema. Confirm what each product can export and in what format, then push one finding through to your ticketing system before committing to a plan. Write agent scenarios, including tool permissions, memory and multi-step paths, as test cases with owners so they can be reviewed like any other change.
Common questions before choosing promptfoo or DeepTeam
How much effort and time does a promptfoo or DeepTeam rollout take?
Effort depends on how many applications you test, how many attack categories you run, and whether results must feed a release gate or a reporting process. promptfoo runs locally from declarative configs, so a first evaluation can start quickly, while wiring checks into CI/CD and tuning the plugins and strategies to your application takes longer. DeepTeam is a Python API built on DeepEval, so the work centers on writing test code, choosing vulnerabilities and attack methods, and reviewing judge results. Neither tool publishes a duration in the sources we used, so ask any implementation partner for a written scope with a timeline, and ask your own engineers to time a pilot against one application first.
Can our team run promptfoo or DeepTeam without outside help?
Yes, both tools run locally and are open source, so a capable engineering team can install them and run a first scan without a vendor contract. The harder part is the ongoing work: maintaining test configs or Python test suites, keeping model API keys and judge models configured, interpreting failures, and deciding what counts as a fix. Outside help usually adds value in the scoping, the CI integration, and the evidence you keep for an assessment. DeployOpen plans, implements and documents self-hosted platforms in your own environment and runs security testing, but it is not a vendor of either tool.
What skills does an AI red teaming rollout require?
The work needs three skill sets. promptfoo requires comfort with Node.js (the current npm release requires Node.js 22.22.0 or newer) and YAML-style configs, and it is the more natural fit for teams that already gate releases with CI. DeepTeam requires Python (it supports Python 3.9 up to but not including 3.14, per PyPI) and enough familiarity with DeepEval to read LLM-as-a-judge results. Either way, someone must understand your application's threat model and the OWASP and NIST AI RMF mappings well enough to decide which findings matter.
Do we need promptfoo Enterprise for SSO or shared reporting?
Only if you need those features in a supported form. The promptfoo pricing page lists team sharing, teams-based access control, SSO, custom roles, continuous monitoring and a centralized guardrail dashboard under Enterprise, at custom pricing. The self-hosted promptfoo app, according to the promptfoo self-hosting documentation, stores data in a local SQLite database that cannot be shared across replicas, has no RBAC, horizontal scaling, built-in authentication or SSO, and is not recommended for production use. Ask promptfoo for a written quote that names the features and deployment model you need, and confirm whether the On-Premise option fits your network rules.
How does DeepTeam's Confident AI integration differ from running it locally?
DeepTeam runs locally and needs a model API key, such as OPENAI_API_KEY or another model that DeepEval supports, to generate attacks and judge responses. Confident AI's commercial platform, per the DeepTeam README, adds risk assessment tracking, production vulnerability monitoring and report sharing. Local runs keep test results on your own systems, while the platform adds a hosted record and sharing workflow. Confirm with Confident AI where data is stored and processed, what the platform costs, and whether it meets your data residency and access requirements before sending results to it.
How hard is it to switch between promptfoo and DeepTeam later?
Switching costs come from the tests, not the tool. promptfoo uses declarative configs, while DeepTeam uses Python code, so attack definitions, judge criteria and CI wiring will need to be rewritten rather than copied. Results also differ in format, so historical findings may not map one to one. Keep a record of your mapped OWASP and NIST AI RMF categories, your target definitions and your pass or fail criteria in a tool-neutral document. That makes a later move a re-implementation project with a known scope.
Is DeployOpen an official partner or certified provider of promptfoo or DeepTeam?
No. DeployOpen is not an official partner, reseller or certified provider of promptfoo, OpenAI, Confident AI or DeepEval, and no supplied fact states otherwise. DeployOpen is a Chainscore Labs company that plans, implements, hardens and supports self-hosted open-source platforms in your own infrastructure, runs security testing, and prepares evidence for compliance assessments. It does not issue audit reports or certifications. Ask any provider to show its vendor relationship in writing and to name the people and deliverables covered by the scope.
How DeployOpen helps teams choose and implement red teaming tools
DeployOpen plans, implements and supports self-hosted open-source platforms inside the client's own infrastructure, including security testing work. For promptfoo vs DeepTeam, we start from the environment the tests must run in, then agree in writing what the red teaming setup will cover, build it and hand it over with documentation.
How we scope
- We confirm which OWASP LLM Top 10 and NIST AI RMF test categories your team needs before choosing either tool.
- We record which model providers and API keys the tests will use, since DeepTeam needs a model API key to generate attacks and judge responses.
- We agree in writing whether the work covers promptfoo CI gating, DeepTeam Python test suites, or both, with deliverables and timelines fixed upfront.
How we build
- We install and configure the chosen tool inside your infrastructure, including the CI/CD pipeline stages that run red team checks.
- We wire attack categories, agent test cases and guardrail checks to the systems your LLM application actually exposes.
- We test the red teaming setup itself, confirming that failing results are recorded and surfaced to the people who must act on them.
How we hand over
- We document configuration files, test definitions, pipeline steps and how results map back to the chosen framework controls.
- We walk your engineers through running scans, reading reports and adding new test cases as your application changes.
- We record which decisions depend on the commercial platform, such as Enterprise access control or Confident AI report sharing, so you can confirm them with each vendor.
How we support
- Ongoing support, upgrades and continuous testing are available as retainers, scoped in writing with your team.
- We can update configurations when promptfoo or DeepTeam releases change, after checking the release notes in your environment.
- We prepare test evidence for compliance assessments, while independent auditors and certification bodies issue any audit reports or certifications.
