AI Governance Platform Comparison

AI gateways like Portkey, LiteLLM, and OpenRouter solve a real problem: unified access to multiple LLM providers with routing, fallback, and cost controls. They are excellent at what they do — routing. But routing is not governance. A gateway that sends your employee's message to GPT-4 or Claude does not know whether that message contained a social security number. It does not know whether the use case it is serving is high-risk under the EU AI Act. It does not produce the audit evidence a regulator will demand.

Difinity starts where gateways stop. Every request that flows through Difinity Flow is scanned for PII before it reaches any LLM provider. Compliance policies are enforced — not logged after the fact. Every interaction is recorded with full policy context: what was checked, what was flagged, what action was taken, and which model produced the response. This is what governance looks like at runtime.

The practical consequence: you can use Portkey to route traffic and still have zero compliance posture. Adding a gateway does not reduce your regulatory exposure. Adding Difinity does — because governance is enforced at the point of execution, not reviewed after the fact in a dashboard.

Observability platforms like Helicone and Langfuse give you visibility into your AI systems: request volumes, latency distributions, cost breakdowns, prompt versions, and response quality scores. This is genuinely valuable for engineering teams optimising LLM performance. The problem is that observability is retrospective. It tells you what happened. It cannot change what happens.

When a customer service agent sends a prompt containing a patient's date of birth to an external LLM, Helicone logs it. Difinity prevents it. The distinction matters enormously in regulated industries. The EU AI Act, GDPR, HIPAA, and most financial services regulations do not care how good your logs are — they care whether non-compliant interactions were blocked at the point of execution.

Difinity includes its own observability layer: every interaction is logged with full context, searchable, filterable, and exportable for audit preparation. So you get observation and enforcement from a single platform rather than layering an observability tool on top of ungoverned AI infrastructure.

AI governance platforms like Credo AI and Holistic AI address the policy and documentation layer of AI compliance: risk assessments, model cards, bias testing reports, policy registries, and compliance dashboards. They are designed to help governance, risk, and compliance teams build and document AI governance programmes. This is important work. But documentation is not enforcement.

A governance platform can tell you that your loan decisioning model has a documented bias policy. It cannot tell you whether that policy is being applied to every API call the model makes in production. The gap between a policy existing in a document and a policy being enforced at runtime is the gap where regulatory exposure lives — and it is the gap that most enterprise AI deployments currently have.

Difinity connects the policy layer to the enforcement layer. Policies configured in Difinity Hub are applied by Difinity Flow to every live request. When a compliance team sets a rule that all high-risk use cases must have human oversight enabled, that rule is enforced — not noted. The compliance dashboard reflects actual enforcement state, not intended policy state.

Cloud providers have built guardrail capabilities into their own AI services: AWS Bedrock Guardrails, Azure AI Content Safety, Google Vertex AI safety filters. These are useful controls for workloads running exclusively within a single cloud provider. The problem is that enterprise AI is not single-provider. Most organisations are running OpenAI, Anthropic, Google, and custom models simultaneously — often across teams that chose their providers independently.

Cloud guardrails create a compliance patchwork. Your AWS-hosted AI has Bedrock Guardrails. Your team using the OpenAI API directly has nothing. Your Azure-deployed model has content safety filters but no audit trail that maps to EU AI Act requirements. Each provider's governance is configured differently, produces different evidence formats, and cannot see what the others are doing.

Difinity provides a single governance layer across every AI provider — regardless of which cloud they run on. One policy engine. One audit trail. One compliance dashboard. When a regulator asks for evidence of PII protection across all your AI systems, Difinity produces it. A patchwork of vendor-specific guardrails does not.

GRC platforms like Vanta, Drata, and OneTrust have transformed how organisations manage SOC 2, ISO 27001, and GDPR compliance. They automate evidence collection, track control status, and generate audit-ready reports. For IT security compliance, they are excellent. The issue is that AI governance is a different discipline with different requirements — and they do not overlap as much as their marketing materials suggest.

SOC 2 asks whether your infrastructure is secure. The EU AI Act asks whether your AI models are making discriminatory decisions, whether personal data is being sent to external LLMs without redaction, whether high-risk AI systems have conformity assessments, and whether there are documented human oversight mechanisms. Vanta can tell you whether your S3 buckets are public. It cannot tell you whether your customer-facing AI model is compliant with Article 14.

Difinity and GRC platforms are complementary, not competitive. A mature enterprise needs both: Difinity to govern the AI runtime layer, Vanta or Drata to manage the broader IT compliance programme. Customers frequently use both. The distinction is that replacing a GRC tool with Difinity is the wrong framing — they solve adjacent problems for adjacent teams.

The internal build option is always on the table. Engineering teams are capable of building PII detection, policy engines, audit logging, and LLM routing middleware. The question is not whether they can — it is whether they should, and at what cost. A realistic enterprise AI governance build includes: a multi-provider gateway, PII detection for 15+ entity types across multiple languages, a policy engine with no-code configuration, a compliance dashboard, audit trail infrastructure, bias detection, human escalation workflows, conformity assessment tooling, and a regulation monitoring function to update policies when laws change.

That is 12–18 months of engineering time for an initial build, assuming a team with expertise in AI governance, compliance law, and security engineering. It does not include the ongoing maintenance burden: regulation updates, new provider integrations, model changes, security patches, and the compliance team's never-ending requirement for new reports and evidence formats.

Total cost of ownership for an internal build typically exceeds £500,000 in year one across engineering, legal review, and compliance consulting. Difinity early access starts at $49 per use case per month. More importantly, Difinity's regulatory expertise — the knowledge of what Article 9 actually requires and how to map it to a software control — is not something an engineering team can acquire quickly. It is built into the product.