How we compare, in your context.
Who you compare us with depends on what you are solving, so there is one table per context. Every table compares the same four things: version a policy, test a change against live traffic before it takes effect, defend a past decision afterwards, and run all of it on your own infrastructure. Embedding this under your own product is a different question, answered on sell into the enterprise.
AI Governance & Security Trust & Safety Risk & Compliance Build it yourself
AI Governance & Security
The whole field a buyer brings up — cloud guardrails, AI-security platforms, open-source frameworks, and AI-governance and compliance tools. They detect threats, and some block a request inline rather than only raising an alert. None of them is a policy-and-evidence engine: one versioned policy you backtest and shadow-test, on your own infrastructure. So we orchestrate their detectors rather than replace them.
| Capability | Cloud guardrailsAWS · Azure · Google · Cloudflare | AI-security platformsPalo Alto/Prisma AIRS · Cisco AI Defense · Lakera/Check Point · CalypsoAI/F5 · SentinelOne · HiddenLayer · Zenity | OSS frameworksNVIDIA NeMo · Microsoft AGT | AI governance / GRCCredo AI · Holistic AI · IBM watsonx · ServiceNow · OneTrust | Swiftward |
|---|---|---|---|---|---|
| What it is | Cloud filter API | Threat detection + red-team | Self-host toolkits | Governance & compliance suites | Policy + evidence engine |
| Runs on your infrastructure, nothing leaves it | partial | partial | Yes | partial | Yes |
| Bring your own detectors and models (no lock-in) | partial | partial | Yes | n/a | Yes |
| Acts inline (block, redact, route), not just detect and alert | Yes | Yes | Yes | partial | Yes |
| Versioned policy as code (diff, rollback) | partial | no | partial | partial | Yes |
| Shadow-test a change against live traffic before it enforces | partial | partial | no | no | Yes |
| Backtest a candidate ruleset on your own history | no | no | partial | partial | Yes |
| Stateful decisions (counters, rate limits, windows) | partial | no | no | no | Yes |
| Human review that survives a restart, and its decision goes back into the pipeline and the record | no | no | partial | partial | Yes |
- Credo AI, Holistic AI are AI-native governance tools. IBM watsonx, ServiceNow, OneTrust are GRC suites with AI modules.
- AWS Bedrock Guardrails ships DRAFT plus immutable numbered versions, on any model including self-hosted. The detectors inside stay AWS's.
- Google Model Armor's "inspect only" is a real shadow mode.
- Microsoft's Agent Governance Toolkit has a real Merkle-chained audit and real runtime enforcement. No backtesting against historical traffic.
- Lakera offers a sensitivity-tuning simulator, not a backtest of a candidate version against your history.
- Holistic AI's Guardian Agents and ServiceNow's AI Control Tower block a request and revoke a privilege. That is why the governance column is partial, not no.
- Cloudflare adds rate limiting at the traffic level; the counters a policy keeps are a different job.
- Azure offers an on-prem container. Palo Alto (Prisma AIRS) supports air-gapped scanning.
The conformity-assessment bodies a European buyer hires under the AI Act — DEKRA, TÜV SÜD — read the evidence rather than produce it. Not a competitor.
Per-vendor breakdown with sources: AI Control Maturity Model.
Trust & Safety
Detection: Hive, ActiveFence/Alice, Thorn's Safer for CSAM, Sightengine, and the cloud and model-vendor moderation APIs — the cheap default everyone tries first. Moderation operations and DSA: Cinder, Tremau, Checkstep. Open source: ROOST.
They detect, moderate and report at scale, including your own match-lists and CSAM hash-matching. We orchestrate that work rather than duplicate it.
None adds the layer above: one versioned policy that decides, and a record naming the rule and the frozen version behind any past decision.
| Capability | DetectionHive · ActiveFence (Alice) · Thorn (Safer) · Sightengine · OpenAI · Azure · Google · AWS | Moderation ops & DSACinder · Tremau · Checkstep | Open sourceROOST | Swiftward |
|---|---|---|---|---|
| What it is | Detector APIs | Moderation ops + DSA | Free self-host stack | Policy + evidence engine |
| Runs on your infrastructure, nothing leaves it | partial | no | Yes | Yes |
| Bring your own detectors (no lock-in) | n/a | partial | Yes | Yes |
| Versioned policy as code (diff, rollback) | no | partial | partial | Yes |
| Shadow-test a change against live traffic before it enforces | no | no | no | Yes |
| A/B two policy versions on live traffic | no | no | no | Yes |
| Backtest a proposed policy against historical content | no | no | no | Yes |
| Tamper-evident audit trail | no | no | no | Yes |
| Defend a past decision with the rule and frozen version that made it | no | no | no | Yes |
| Case management / reviewer workflow | partial | Yes | Yes | Yes |
| DSA Article 17 statement of reasons, generated from the decision | no | Yes | no | Yes |
- The cloud and model-vendor moderation APIs and Sightengine are detectors we orchestrate — we sit above them.
- ROOST is free, self-hostable open source: the Osprey rules engine, donated by Discord, and the Coop console.
- Hive runs fully on-prem, air-gapped, which is why the detection column says partial on that row rather than no.
- Thorn's Safer is the CSAM detector most large platforms already run. We read it as a signal.
- ActiveFence (now Alice) has the broadest detection coverage of the vendors listed, and now covers moderation operations too.
- Cinder, Tremau, Checkstep lead on moderation operations and DSA tooling.
Risk & Compliance
The fraud and AML specialists are years ahead on detection — ML risk scores, KYC/AML data, models built by data scientists over a decade. We do not try to beat them at detection. We orchestrate them.
The risk-decisioning and business-rules engines are the closest match to us: they write and run rules too.
Against both, the platform is where we win: versioned deterministic rules you backtest on your own history, shadow-test, and A/B. Every decision lands in a tamper-evident record, on your own infrastructure. The same rules govern AI-agent decisions, with state. Where they match us, the table says so: both groups run live champion/challenger, and the rules engines version their rules. What stays ours is the backtest: a run against your own recorded history, verdict by verdict, before anything enforces.
| Capability | Fraud & AML detectionFeedzai · SAS · NICE Actimize · Sardine · Unit21 | Risk-decisioning / rules enginesFICO · Experian PowerCurve · Provenir · Pega · IBM ODM · Drools | Swiftward |
|---|---|---|---|
| What it is | Fraud & AML detection + ML scoring | Business-rules / decisioning platform | Policy + evidence engine |
| Runs on your infrastructure, nothing leaves it | partial | partial | Yes |
| Bring your own fraud, ML, and KYC signals (orchestrate, no lock-in) | Built-in | partial | orchestrates |
| Versioned policy as code (diff, rollback) | partial | Yes | Yes |
| Champion/challenger and A/B on live traffic before a change enforces | Yes | Yes | Yes |
| Backtest a candidate ruleset on your own history, verdict by verdict | partial | partial | Yes |
| Tamper-evident audit trail | partial | partial | Yes |
| Defend a past decision to an examiner: the rule and the frozen version | partial | partial | Yes |
| Human review that survives a restart, and you declare what happens if nobody answers | Yes | partial | Yes |
| Stateful decisions (counters, limits, windows) | Yes | partial | Yes |
- Sardine publishes a rule changelog — what changed, who changed it, before and after. The closest any detection vendor gets to that row, and why the column is partial.
- Where a vendor's documentation sits behind a customer login, we say partial rather than guess.
- Your model-risk and GRC tooling — ModelOp, ValidMind, IBM OpenPages — documents and attests models. Different job. SR 26-2 leaves generative and agentic AI outside its scope, and we are the control and the record for that gap. We feed those tools.
The strongest setup buys both: their ML risk models, KYC and fraud detectors feed in as signals, and every policy and decision lives here — versioned, backtested, shadow-tested, A/B-tested.
Risk and compliance · Financial services · where the agent also calls tools, see AI Governance
If you would rather build it yourself
One comparison per open-source building block you would assemble a control plane from:
- vs OPA / Cedar — the policy evaluator
- vs LiteLLM — the gateway
- vs NVIDIA NeMo Guardrails — the guardrail framework
- vs Microsoft Agent Governance Toolkit — the agent-governance toolkit
- vs ROOST — the open-source Trust and safety stack
The model vendors' own built-ins (OpenAI's Moderation API, Anthropic's classifiers), Meta's Llama Guard and Guardrails AI are detectors you plug in, not foundations you build a control plane on. "Why not just use my model vendor's free moderation?" — Swiftward orchestrates it and sits above, with the policy and the record.
All competitor capabilities here are our reading of public documentation as of August 2026; tell us if we have misjudged yours and we will correct it.