A centralised LLM gateway comparison should start with the requests already moving through your estate, not a feature checklist. A production copilot may call one provider today, but the operating problem quickly becomes broader: which model is appropriate for each task, who can use it, what data may leave the application, how spend is constrained, and what evidence exists when security or compliance asks questions.
The useful comparison is therefore not simply gateway versus gateway. It is whether a platform creates one governed doorway between applications and model providers, or whether it adds another surface that engineering still has to operate and secure.
What a centralised LLM gateway must control
A gateway sits between your applications and providers such as OpenAI, Google Gemini, Anthropic Claude, Mistral, Cohere and DeepSeek. That position is valuable only when policy is applied to every request rather than delegated to individual application teams.
Start with the request path. A well-designed control plane applies prompt-injection and jailbreak shielding first, then DLP controls, then routes the request, and finally moderates the output. The order matters. Sensitive information should be identified before a provider receives the request, while unsafe output still needs inspection before it reaches a user or downstream system.
This is a meaningful distinction from a proxy that merely normalises provider APIs. API normalisation reduces integration work, but it does not decide whether a prompt containing personal data should be blocked, redacted as [REDACTED], or recorded as a warning. Nor does it create a consistent policy boundary for every team building internal assistants, customer-facing features and automated workflows.
The comparison should also separate guardrails from governance. A prompt filter can address a narrow safety concern. Governance includes scoped access, named routes, budget ownership, traceability, retention choices and a clear operating model for exceptions.
Compare architecture before model catalogues
Model catalogues change frequently. Architecture determines whether the gateway remains useful after your first provider change, incident review or monthly finance meeting.
Provider abstraction and routing
At a minimum, assess whether applications can call a stable, OpenAI-compatible endpoint while the platform selects from approved models behind it. This preserves application behaviour while allowing the AI platform team to define routing rules and priorities centrally.
The strongest implementations route on capability, performance requirements and cost rather than treating the most expensive model as the default answer. A concise classification task, a high-volume summarisation workflow and a complex reasoning request should not necessarily use the same model. Automatic failover also matters when provider capacity or availability changes, but it should be tested against the routes you actually intend to run.
Ask a practical question: can a team change the preferred provider or model without a code deploy? If the answer requires updating model names across multiple services, the gateway has not fully separated application logic from provider decisions.
There is a trade-off. Central routing introduces a platform dependency and requires teams to agree on route definitions. That is usually worthwhile for shared production infrastructure, but a small experimental project with a single provider and no sensitive data may not need the same level of control.
Security and data protection at request level
Security controls need to work on the live path, not only through developer guidance or retrospective log reviews. Compare whether prompt shielding is enforced before routing, whether DLP can log, warn, redact or block, and whether output moderation is applied after the model responds.
DLP action design deserves close scrutiny. Logging a finding supports investigation; warning allows a monitored exception; redaction replaces detected content with [REDACTED]; blocking prevents the request from proceeding. These actions serve different workflows and should be configurable according to data category and risk tolerance. A support assistant handling customer details may need redaction or blocking. An internal development sandbox may start with warnings while policies are tuned.
Also examine logging defaults. Full prompt and completion retention can create a second sensitive-data store, with its own access and retention obligations. Metadata-only logging by default, with payload retention as an explicit opt-in, gives teams useful operational evidence without making broad content storage the assumed setting.
For UK and EU organisations, location and contractual posture are part of this assessment. EU data residency, GDPR processor terms and audit trails that support EU AI Act record-keeping address concrete procurement and control requirements. They do not remove the need for an organisation’s own risk assessment, but they can materially reduce the effort required to evidence how AI use is governed.
Spend controls that can be enforced
Cost dashboards are useful after money has been spent. Hard spend caps and budgets change the operating model because they can alert or stop usage at the boundary. When a hard cap is reached, a request should receive a 429 rather than silently continuing under a different model or provider.
That behaviour may feel restrictive, particularly for customer-facing workloads. It is also honest. If availability is critical, set budgets with sufficient headroom, define ownership and monitor consumption before the cap is reached. Do not mistake an automatic routing feature for financial control: routing chooses an eligible model; a budget policy governs whether further spend is allowed.
A credible gateway comparison looks for cost attribution at the level that matches organisational accountability. Engineering leaders may need service-level visibility, FinOps may need route and provider trends, and product teams may need to understand the cost of a particular workflow. Per-request traces make those conversations evidence-based rather than speculative.
Deployment friction is a deciding factor
Many gateway evaluations fail at adoption rather than capability. If every application requires a new SDK, custom middleware and business-logic changes, centralisation becomes a long migration programme. The practical alternative is an OpenAI-compatible gateway where an existing client changes its API key and base URL, while application behaviour remains byte-for-byte identical.
This does not mean implementation should be treated casually. Teams should validate streaming behaviour, structured outputs where used, error handling, rate-limit responses and provider overrides. They should also decide who can manage scoped API keys, approve routes and change policy actions. The point is to focus implementation effort on governance decisions, not repeat provider integration work in every service.
routeur.ai follows this model: one API-key and base-URL change places routing, request-level policy enforcement, cost controls and traces behind a governed control plane. Its routing can use auto or a plain logical route name, allowing model decisions to remain outside application business logic.
A practical evaluation method
Run a comparison using representative traffic rather than generic prompts. Include normal requests, high-cost requests, prompt-injection attempts, prompts containing test personal data, provider unavailability and budget-limit scenarios. The platform should show what happened to each request and why.
Use the same evaluation to inspect operator experience. Can an authorised platform owner see the selected provider, model, policy actions, latency and cost in a per-request trace? Can they use a dry run to understand a routing or policy decision before applying it to live traffic? Are overrides explicit and controlled, rather than hidden in application code?
Four evaluation questions expose most gaps:
- Can every application use a stable interface while provider and model choices change centrally?
- Are prompt shielding, DLP and output moderation enforced in the correct request sequence?
- Can a budget owner set a hard cap that alerts or blocks consumption predictably?
- Is the evidence needed for an incident, audit or cost review available without retaining every payload by default?
Avoid scoring every feature equally. A broad provider list has limited value if sensitive prompts can bypass DLP. Detailed analytics are less useful if there is no hard control over spend. Conversely, a heavily governed platform may be unnecessary for a low-risk prototype. Weight criteria according to the systems you are operating and the failure modes you must prevent.
Choose for the operating model you need
The right gateway is the one that turns LLM use from scattered provider calls into an operationally manageable service. For a product team, that can mean choosing capable models without repeatedly rewriting integrations. For security, it means policies are enforced on every request. For FinOps, it means cost is visible and bounded. For compliance, it means there is an audit trail without defaulting to wholesale payload retention.
A centralised gateway should not force a choice between speed and control. It should make the controlled path the easiest path for application teams to use, then give the people accountable for risk, cost and reliability the evidence to keep production AI moving with confidence.