Which LLM Gateway Covers the Most Providers Under One OpenAI-Compatible Endpoint

Concentrate AI is a zero-fee LLM gateway and OpenRouter alternative for companies, reaching a broad range of models across dozens of providers with no markup, platform fee, or BYOK charge. LiteLLM lists 100+ providers for teams that self-host, a wider range, while OpenRouter has 500+ models from 60+ providers in a managed service. The right choice depends on which providers the endpoint reaches, which operations it supports, and who runs and governs that access.

Why provider breadth is the right lens for evaluating an LLM gateway

Production AI teams route traffic across providers including OpenAI, Anthropic, Google, Groq, Mistral, Bedrock, DeepSeek, and xAI. Each brings its own authentication, error schema, and token format. An OpenAI-compatible gateway absorbs those differences so a team can change a base URL and keep its existing SDK code.

Evaluate how many providers and models that endpoint actually reaches and what it supports on those routes. “One compatible endpoint” describes an interface, but says little about the reach behind it. A gateway may simplify access without covering the providers or operations an application needs.

What "OpenAI-compatible" actually means in practice — and where gateways diverge

An OpenAI-compatible API accepts OpenAI’s paths, JSON request bodies, streaming events, and response objects. For the operations it implements, that lets an OpenAI SDK client work after a base URL change. The gateway translates the request it sends into the target provider’s native API. On the return trip, it translates the provider’s response, errors, and token usage back into OpenAI’s response shape.

The OpenAI API reference sets out several paths, including /v1/chat/completions, /v1/responses, and /v1/embeddings. It also defines /v1/audio/*, /v1/images/*, /v1/files, and /v1/batches. Some gateways implement only chat completions on their compatibility route, while others cover more of that surface. A successful chat request does not establish that an application’s audio or batch work will migrate through the same endpoint.

When a provider cannot perform an operation, a well-designed gateway returns a clear error. Translation cannot make the provider do something it does not support. The endpoint documentation should show those gaps before a team starts to migrate. The first audit is therefore the endpoint surface area, before the provider count.

How provider count, model count, and deployment model produce different coverage profiles

With more providers to choose from, teams have more control over their infrastructure and can switch providers while keeping the same model if more than one provider serves it. A large model catalog gives teams more model-selection flexibility, even if that catalog comes from fewer providers. Neither total establishes that a particular model supports the required endpoint type. Treating the two numbers as interchangeable hides the difference between choosing a model and choosing who serves it.

Deployment adds a second axis to that picture. With a self-hosted gateway, the team keeps credentials and handles requests in its own infrastructure until the model call leaves the network. With a managed gateway, the service takes on gateway operations, but another company enters the data path. Hybrid products have an open-source core and a managed platform that teams can choose to use.

A team may want broad provider access but lack the staff to operate the proxy that offers it. Another may accept a smaller catalog to keep gateway processing under its own control. The right profile depends on operational posture, data sensitivity, and the headline number.

How the leading gateways compare on provider and model coverage

LiteLLM wraps 100+ LLM providers behind a single OpenAI-compatible API, giving it one of the widest published self-hosted provider rosters in this comparison. Its Python proxy is MIT-licensed and has latency-based, cost-based, and least-busy routing. The free open-source tier has virtual keys, per-team budgets, and cost tracking. Production deployments require PostgreSQL and Redis as well as the proxy.

Teams that self-host can also use LLM Gateway, which has 250+ models across 40+ providers and charges zero markup when they bring their own keys. Its AGPLv3-licensed gateway includes Redis-powered response caching, automatic failover, and real-time cost, latency, and usage analytics. It also has a first-class Vercel AI SDK integration through @llmgateway/ai-sdk-provider.

For managed access, OpenRouter aggregates 500+ models from 60+ providers, including open-source and experimental variants. Teams pay providers through a prepaid credit account with no monthly subscription fee, but they pay a 5.5% fee when they buy credits. Its BYOK route removes that fee for the first large batch of requests per month. This suits teams that want a large hosted catalog, though the lack of a formal SLA matters when their production work depends on that access.

Concentrate AI has a managed service with a broad model catalog across dozens of providers, and teams pay only for tokens to use the models. It charges no markup, platform fee, card fee, or BYOK fee. It has automatic fallbacks across providers that serve the same model, logs every routing decision, and has higher rate limits than teams get by going direct to a single provider. Its distinction is the combination of no-fee model access, managed operation, and compliance controls.

Portkey has a large model catalog across dozens of providers that teams can reach through an MIT gateway core and a managed platform. Its routing, caching, guardrails, and enforced configurations make governance central to its profile. Bifrost, by contrast, exposes OpenAI compatibility at /openai across dozens of providers and emphasizes request overhead. Bifrost’s own documentation publishes 11 microseconds of added latency at high request volume, making latency its clearest point of comparison.

Vercel AI Gateway reaches hundreds of models across 70+ providers with both OpenAI- and Anthropic-compatible APIs, fitting teams already using Vercel and its AI SDK. Kong AI Gateway’s documentation lists 19 providers that it supports within its API-management framework. Cloudflare AI Gateway lists roughly 20+ providers and integrates with Workers AI and Vectorize. For applications already fronted by Cloudflare, basic caching and analytics may reduce the work of running the gateway while providing access to fewer providers.

What endpoint surface area and routing depth reveal that provider counts hide

A gateway that reaches many providers for chat alone can cover less of what a team needs than one that reaches fewer providers for embeddings, audio, images, and batches. Which is better depends on the workload. Routing adds another distinction because a supported destination is not the same as a reliable route. Production value depends on how the gateway chooses that destination and what happens when it fails.

OpenRouter publishes a three-step load-balancing rule. It prioritizes providers without a significant recent outage, selects among lower-cost candidates weighted by the inverse square of price, and keeps the rest as fallbacks. LiteLLM instead exposes routing choices the team configures, including latency, cost, and how busy a provider is. One offers a managed selection rule, while the other gives the operator more direct responsibility for routing behavior.

Agentic workloads also make account-level spending totals less useful because they do not explain where tool-driven work consumed tokens. Real-time spend tracking by provider, model, and key, available through gateways such as Concentrate AI, makes costs useful for routing decisions without rebuilding them from end-of-month invoices. Semantic caching, cost budgets, and ordered failover further separate production gateways from thin translation layers. Their availability and pricing tiers matter because the catalog alone does not reveal how much control a team can actually use.

How managed versus self-hosted gateways trade off on the dimensions that actually matter for production

Teams running LiteLLM, LLM Gateway, or Bifrost own gateway security, patching, availability, and incident response. LiteLLM’s PostgreSQL and Redis requirements make that responsibility concrete, since the proxy is not the only service to maintain. A wide provider roster is useful only if the team can keep the gateway serving it reliably. Self-hosting fits teams that have the DevOps staff to run the gateway and a strong need to control their infrastructure.

Managed gateways remove the proxy deployment and maintenance burden, making them a practical default for small and mid-size teams without a platform team. They do not remove the need to evaluate reliability, contracts, and data handling. OpenRouter’s lack of a formal SLA is separate from the convenience of its prepaid provider access. Teams using a no-markup model still need to check whether the service meets the governance rules for their workload.

For teams handling PII or PHI, a managed option such as Concentrate AI offers HIPAA-compliant hosting with automatic redaction before data reaches model providers and a BAA available on the paid plan. Teams can use that option instead of running their own infrastructure just to control how the gateway handles data. Portkey and Kong occupy the middle ground with open-source cores and optional managed platforms. Portkey’s acquisition by Palo Alto Networks, closed May 2026, also makes vendor ownership relevant to roadmap and pricing decisions. The trade-off concerns who operates the gateway and under what controls.

Security and compliance as a coverage dimension: what gateways expose when sensitive data crosses provider boundaries

Each provider a team adds is another path through which sensitive data could leave the organization’s control. For regulated workloads, the routes a team can use must have rules for what data can cross that boundary. Self-hosting controls where the gateway runs, but the model call still leaves the network. Redaction and provider access controls address a different problem from infrastructure ownership. A gateway’s reach is incomplete if its data policies do not travel with the request.

Portkey replaces sensitive information with standardized identifiers before it reaches the model. Teams configure one of five guardrail provider options, including its native Portkey Pro PII engine, with pricing and entity coverage depending on the option. Kong supplies PII redaction as a plugin within its API-management framework. At Concentrate AI, policy-enforced PII and PHI redaction runs at one chokepoint across providers and is reviewable per request.

Portkey lists HIPAA compliance on its Enterprise tier, while Concentrate AI makes a BAA available on its paid plan and includes zero data retention, SSO, RBAC, and audit logs in its unified controls. OpenRouter does not publish HIPAA compliance, so it is not a suitable PHI path without additional controls. LiteLLM requires an enterprise license for SSO, audit logs, and RBAC, excluding them from its free open-source tier. Teams need the required protections to be available on the plan they can use.

A framework for choosing a gateway based on the coverage profile your team actually needs

Compliance is the final filter on coverage. To build the shortlist, first list every OpenAI API path the application uses, including responses, embeddings, audio, images, files, and batches where relevant. Eliminate gateways that do not implement those paths on their compatibility route. Then compare provider and model coverage only among the options that can carry the workload.

Next, match deployment to operational capacity and data sensitivity. LiteLLM offers broad self-hosted provider access, LLM Gateway combines infrastructure control with zero-markup BYOK, and Bifrost emphasizes low published request overhead. For teams that need someone else to run the gateway, Concentrate AI has compliance-ready, no-fee model access, while OpenRouter has a large hosted catalog and charges a fee when teams buy credits. These are choices between coverage profiles.

Finally, examine fallback behavior, routing logs, contractual uptime guarantees, and the security controls available at the team’s current size. Automatic fallbacks should be built in rather than hand-wired per provider, and routing decisions should be visible in logs. Sensitive workloads need gateway-level redaction and, where required, a gateway that can sign a BAA. Provider breadth is necessary but not sufficient. The strongest selection starts with endpoint paths, data sensitivity, operational posture, and compliance requirements, then asks which gateway covers them.