--- description: Compare the top AI gateways for Anthropic, OpenAI, and Gemini on SDK compatibility, cross-provider fallbacks, key management, cost tracking, and caching. title: Top 5 AI Gateways for Anthropic, OpenAI, and Gemini image: https://storage.ghost.io/c/84/03/8403f2f6-141c-411a-8f55-a32d4291533e/content/images/size/w1200/2026/10/top-5-ai-gateways-for-anthropic-openai-and-gemini-bifrost-isometric.png --- Try Bifrost Enterprise free for 14 days. [Request access](https://www.getmaxim.ai/#enterprise-trial) **TL;DR** - AI gateways let one application call Claude, GPT, and Gemini models through a single OpenAI-compatible API, with one credential instead of three vendor key sets. - Tool schemas, thinking parameters, and prompt-cache markers differ between Anthropic, OpenAI, and Gemini, so a gateway must rebuild them per provider, including on fallback. - Bifrost exposes native OpenAI, Anthropic, and Google GenAI SDK endpoints, so existing code changes only its base URL, and it adds 11 microseconds of overhead per request at 5,000 RPS. - LiteLLM is the main self-hosted alternative; OpenRouter, Vercel AI Gateway, and Cloudflare AI Gateway are hosted services that suit teams without data-residency constraints. - Anthropic calls its OpenAI-compatibility layer not production-ready and Google labels its own as beta, so a gateway that speaks each vendor's native API matters. AI gateways give engineering teams one API for calling Anthropic Claude, OpenAI GPT, and Google Gemini models, with shared keys, fallbacks, and cost tracking across all three vendors. [Bifrost](https://www.getmaxim.ai/bifrost), the [open-source AI gateway written in Go](https://github.com/maximhq/bifrost) by Maxim AI, is the best choice for enterprises running mission-critical AI workloads that require best-in-class performance, scalability, and reliability. This guide compares five AI gateways on SDK compatibility, cross-provider fallbacks, key management, per-provider cost tracking, and whether prompt caching, tool use, and reasoning survive translation. ## What Is an AI Gateway? An AI gateway is a service that sits between applications and model providers, exposing one API while handling routing, credentials, failover, and usage tracking for every provider behind it. For teams calling Claude, GPT, and Gemini, it replaces three separate SDK integrations with one governed entry point. Multi-vendor usage is the normal enterprise pattern. Menlo Ventures' [2025 Mid-Year LLM Market Update](https://menlovc.com/perspective/2025-mid-year-llm-market-update/) put Anthropic at 32% of enterprise LLM usage, OpenAI at 25%, and Google at 20%. Teams picking the best model per task end up holding keys and invoices from at least two of those vendors. The broader architecture is covered in our guide to [AI gateway architecture and why it matters](https://www.getmaxim.ai/articles/what-is-an-ai-gateway-architecture-features-and-why-it-matters/). Figure 1 shows what the application has to manage with and without a gateway in the path. ![Without a gateway the application manages three vendor SDKs and key sets; with a gateway one API format and virtual key reach OpenAI, Anthropic, and Gemini](https://articles-images-cdn.t3.tigrisfiles.io/diagrams/ai-gateways-anthropic-openai-gemini/ai-gateways-anthropic-openai-gemini-without-vs-with.png) *Figure 1: The gateway moves keys, formats, and fallback logic out of application code and into one governed layer.* Without a gateway, a Claude-to-Gemini fallback means hand-writing translation code for messages, tools, and thinking parameters. The [Bifrost AI gateway](https://www.getmaxim.ai/bifrost) covers [25+ providers and 10,000+ models](https://docs.getbifrost.ai/providers/supported-providers/overview) through one OpenAI-compatible API, which moves that work out of the application. ## Key Criteria for Choosing AI Gateways for Claude, GPT, and Gemini The right gateway for a three-vendor stack keeps each vendor's features intact while unifying keys, fallbacks, and cost data. Routing breadth matters less than whether Anthropic thinking blocks, Gemini thinking configuration, and prompt-cache markers survive translation correctly. Two vendor facts frame the criteria. Anthropic states that its OpenAI SDK compatibility layer [is not considered a long-term or production-ready solution](https://platform.claude.com/docs/en/cli-sdks-libraries/libraries/openai-sdk) for most use cases, and that prompt caching is not supported through it. Google describes its [Gemini OpenAI compatibility](https://ai.google.dev/gemini-api/docs/openai) as still in beta. Pointing the OpenAI SDK at each vendor's compatibility endpoint is a weak production foundation. | Criterion | What to check | Why it matters for Claude, GPT, and Gemini | | --- | --- | --- | | SDK compatibility | Which native SDK formats the gateway accepts (OpenAI, Anthropic Messages, Google GenAI) | Anthropic or GenAI SDK code should not need a rewrite | | Cross-provider fallbacks | Whether a failed Claude call can fall back to GPT or Gemini, and how the request is re-translated | Fallback across vendors is only useful if tools and reasoning still work on the backup | | Key management | Pooled provider keys, per-team credentials, secret-manager integration | Three vendors means three sets of keys to rotate and scope | | Cost tracking per provider | Per-request cost, filtering by provider, budgets | Each vendor prices input, output, and cached tokens differently | | Feature passthrough | Prompt caching, tool use, reasoning, beta headers | These are the features that differ most between the three APIs | | Deployment model | Self-hosted, in-VPC, or hosted only | Determines whether prompts leave your network | The [LLM Gateway Buyer's Guide](https://www.getmaxim.ai/bifrost/resources/buyers-guide) expands these into a full checklist. For a routing-focused walkthrough, see how to [route between OpenAI, Anthropic, and Gemini](https://www.getmaxim.ai/articles/best-ai-gateway-for-routing-between-openai-anthropic-and-gemini/) with fallback chains and CEL rules. ## AI Gateways Compared at a Glance The five AI gateways below all reach Claude, GPT, and Gemini models, but they differ in which SDK formats they accept, how they treat prompt caching and reasoning, and where they run. Only Bifrost and LiteLLM are self-hosted; the other three are managed services. | Gateway | Deployment | SDK formats accepted | Cross-provider fallbacks | Prompt caching handling | Reasoning handling | | --- | --- | --- | --- | --- | --- | | **Bifrost** | Self-hosted, open source; in-VPC for Enterprise | OpenAI, Anthropic, Google GenAI, Bedrock, LiteLLM, LangChain | Per-request `fallbacks` list; retries with key rotation first | Forwards `cache_control`; optional per-provider auto-injection | `reasoning` mapped to Anthropic `thinking` and Gemini `thinkingConfig` | | **LiteLLM** | Self-hosted | OpenAI format; Anthropic `/v1/messages` format | Ordered fallbacks after retries, plus context-window and content-policy fallbacks | Forwards and translates `cache_control` (Gemini context caching, Bedrock `cachePoint`) | `reasoning_effort` mapped; `reasoning_content` normalized | | **OpenRouter** | Hosted | OpenAI SDK | `models` array in priority order | `cache_control` accepted for Anthropic and Gemini | Unified `reasoning` parameter (effort or token budget) | | **Vercel AI Gateway** | Managed | OpenAI Chat Completions, OpenAI Responses, Anthropic Messages, AI SDK | Provider and model fallbacks | `caching: 'auto'` adds Anthropic cache markers | Shared effort mapped to each provider | | **Cloudflare AI Gateway** | Managed | OpenAI-compatible `/ai/v1` endpoint | Retries and model fallbacks | Exact-match response caching; provider prompt caching not published | Not published | Teams leaving a self-hosted proxy can compare [Bifrost as a LiteLLM alternative](https://www.getmaxim.ai/bifrost/resources/litellm-alternative); teams leaving a hosted router can review [OpenRouter alternatives for production AI systems](https://www.getmaxim.ai/articles/top-5-openrouter-alternatives-for-production-ai-systems/). ## 1. Bifrost: Open-Source AI Gateway with Native SDK Endpoints [Bifrost, the open-source AI gateway](https://www.getmaxim.ai/bifrost), accepts the OpenAI, Anthropic, and Google GenAI SDKs on their own endpoints, so each service keeps its SDK and changes only the base URL. It adds 11 microseconds of overhead per request at 5,000 RPS in sustained benchmarks. **Best for:** Bifrost is built for enterprises running mission-critical AI workloads that require best-in-class performance, scalability, and reliability. It serves as a centralized AI gateway to route, govern, and secure all AI traffic across models and environments with ultra low latency. Bifrost unifies LLM gateway, MCP gateway, and Agents gateway capabilities into a single platform. Designed for regulated industries and strict enterprise requirements, it supports air-gapped deployments, VPC isolation, and on-prem infrastructure. It provides full control over data, access, and execution, along with robust security, policy enforcement, and governance capabilities. In Figure 2, each SDK points at its matching Bifrost route, and every request passes the same key, budget, and routing checks before reaching a provider. ![OpenAI, Anthropic, and Google GenAI SDK clients connect to Bifrost, which applies virtual keys, budgets, routing, and fallbacks before reaching each provider and logging cost per request](https://articles-images-cdn.t3.tigrisfiles.io/diagrams/ai-gateways-anthropic-openai-gemini/ai-gateways-anthropic-openai-gemini-bifrost-architecture.png) *Figure 2: Teams keep the SDK each service already uses, while keys, budgets, fallbacks, and cost records live in one place.* ### SDK compatibility and drop-in replacement Bifrost works as a [drop-in replacement](https://docs.getbifrost.ai/features/drop-in-replacement) for the OpenAI, Anthropic, and Google GenAI SDKs. The [OpenAI SDK integration](https://docs.getbifrost.ai/integrations/openai-sdk/overview) uses the `/openai` route, the Anthropic SDK uses `/anthropic`, and the [Google GenAI SDK integration](https://docs.getbifrost.ai/integrations/genai-sdk/overview) uses `/genai`. Provider-prefixed model names let any of those clients reach any vendor: ```python from openai import OpenAI client = OpenAI(base_url="", api_key="sk-bf-...") client.chat.completions.create(model="anthropic/claude-sonnet-4-5", messages=msgs) client.chat.completions.create(model="gemini/gemini-2.5-pro", messages=msgs) client.chat.completions.create(model="openai/gpt-4o", messages=msgs) ``` An Anthropic SDK client pointed at `/anthropic` can likewise call `openai/gpt-4o-mini`. Virtual keys authenticate through whichever header the SDK already sends (`Authorization`, `x-api-key`, or `x-goog-api-key`). ### Cross-provider fallbacks and key management [Retries and fallbacks](https://docs.getbifrost.ai/features/fallbacks) work in two layers. A `429` rotates to another pooled key with backoff, a `401` or `403` rotates immediately, and a `5xx` retries the same key with exponential backoff. Only when retries are exhausted does Bifrost move to the next entry in the request's `fallbacks` list, such as `["anthropic/claude-sonnet-4-5", "gemini/gemini-2.5-pro"]`, and the response's `extra_fields.provider` reports which vendor served it. Provider keys are pooled with [weighted load balancing](https://docs.getbifrost.ai/features/keys-management), and each key can be restricted to specific models with allowlists or regex patterns. Applications never hold those keys; they hold [virtual keys](https://docs.getbifrost.ai/features/governance/virtual-keys) that carry allowed providers, allowed models, and rate limits. Bifrost Enterprise adds [secret management](https://docs.getbifrost.ai/enterprise/secret-management) with AWS Secrets Manager, GCP Secret Manager, or HashiCorp Vault, so provider keys are stored as references rather than plaintext. ### Cost tracking per provider Bifrost calculates cost per request from the [Model Catalog](https://docs.getbifrost.ai/architecture/framework/model-catalog), which syncs provider pricing every 24 hours when a config store is attached. [Request logs](https://docs.getbifrost.ai/features/observability/default) record provider, model, tokens, cost, and latency, and can be filtered by provider or cost range. [Budgets and rate limits](https://docs.getbifrost.ai/features/governance/budget-and-limits) apply hierarchically at the virtual key, team, and customer level, with optional calendar-aligned resets for monthly or quarterly spend caps. ### Enterprise deployment [Bifrost Enterprise](https://www.getmaxim.ai/bifrost/enterprise) adds clustering, RBAC, and [in-VPC deployments](https://docs.getbifrost.ai/enterprise/invpc-deployments) for teams whose prompts cannot leave their network. The [benchmark results](https://www.getmaxim.ai/bifrost/resources/benchmarks) show 11 microseconds of overhead at 5,000 RPS with a 100% success rate. ## 2. LiteLLM: Self-Hosted Proxy with Broad Provider Coverage LiteLLM is an open-source, self-hosted gateway that exposes one OpenAI-compatible endpoint for more than 100 LLM providers. It is a common self-hosted starting point for unifying Claude, GPT, and Gemini, and adds an Anthropic-format `/v1/messages` endpoint that can call OpenAI and Gemini models. **Best for:** Python-centric platform teams that want a self-hosted, OpenAI-compatible proxy they operate themselves. - **SDK compatibility:** OpenAI-format requests across all providers, plus Anthropic Messages format for every supported provider, including `openai/` and `gemini/` models. - **Fallbacks:** Ordered fallbacks after a configurable number of retries, with separate fallback lists for context-window and content-policy errors. - **Key management and cost:** Virtual keys per user, team, or project with their own models, budgets, and rate limits; spend tracked by key, team, user, and tag. - **Feature passthrough:** `cache_control` markers are forwarded to Anthropic and translated for Gemini context caching and Bedrock `cachePoint`; reasoning output is normalized into `reasoning_content`, with `thinking_blocks` for Anthropic models. LiteLLM's breadth is its main strength. A side-by-side view of [OpenRouter, LiteLLM, and Bifrost for multi-provider access](https://www.getmaxim.ai/articles/openrouter-vs-litellm-vs-bifrost-multi-provider-llm-access/) compares the three approaches directly. ## 3. OpenRouter: Hosted Router for Fast Model Access OpenRouter is a hosted service offering hundreds of models through one OpenAI-compatible endpoint. Teams use the OpenAI SDK with OpenRouter's base URL and switch between Claude, GPT, and Gemini by changing the model slug, with billing handled through OpenRouter credits. **Best for:** Small teams and prototypes that want one bill and one API key for many models, and have no requirement to keep traffic inside their own network. - **SDK compatibility:** OpenAI SDK as a drop-in client. - **Fallbacks:** A `models` array lists model IDs in priority order; rate limiting, downtime, context-length errors, and moderation flags can trigger the next model, and billing uses the model that served the request. - **Key management and cost:** Requests run on OpenRouter credits by default. Bring-your-own-key is supported, at 5% of the equivalent OpenRouter cost, with a free monthly allowance for pay-as-you-go accounts. Responses include cost, cached tokens, and reasoning tokens. - **Feature passthrough:** `cache_control` breakpoints are accepted for Anthropic and Gemini models, and a unified `reasoning` parameter accepts either an effort level or a token budget. OpenRouter offers a quick way to try many models from one key, but every prompt transits a third-party service, which rules it out for many regulated workloads. Bifrost can also use OpenRouter as an [upstream provider](https://docs.getbifrost.ai/providers/supported-providers/openrouter), keeping governance in your own gateway. ## 4. Vercel AI Gateway: Managed Gateway for Vercel-Centric Teams Vercel AI Gateway is a managed gateway that accepts OpenAI Chat Completions, OpenAI Responses, and Anthropic Messages formats, plus the Vercel AI SDK. Applications do not need to run on Vercel, and Vercel states that it adds no markup to provider token prices, including with bring-your-own-key. **Best for:** Product teams already building on Vercel and the AI SDK that want routing, budgets, and logs without operating their own gateway. - **SDK compatibility:** OpenAI SDK and Anthropic SDK clients both work against the gateway's base URL. - **Fallbacks:** Ordered provider and model fallbacks, with every routing attempt recorded in request logs. - **Key management and cost:** System credentials or bring-your-own-key; budgets per team, project, API key, or member. Budgets apply to spend on system credentials, and BYOK spend is metered separately. - **Feature passthrough:** `caching: 'auto'` adds `cache_control` breakpoints for Anthropic (direct, Vertex, and Bedrock) while OpenAI and Google cache implicitly; a shared reasoning effort is mapped to Anthropic adaptive thinking, Gemini `thinkingLevel` or `thinkingBudget`, and OpenAI `reasoningEffort`. Vercel AI Gateway handles Anthropic prompt caching and reasoning translation well, but like any hosted option it cannot be deployed inside your own VPC. A broader comparison of [multi-provider AI gateways for OpenAI, Anthropic, and Bedrock](https://www.getmaxim.ai/articles/top-multi-provider-ai-gateways-for-openai-anthropic-bedrock/) covers how hosted and self-hosted options split on that point. ## 5. Cloudflare AI Gateway: Edge Analytics and Response Caching Cloudflare AI Gateway is a Cloudflare-managed service in front of providers such as OpenAI, Anthropic, Google Gemini, and Workers AI. It offers analytics, logging, rate limiting, retries, model fallbacks, and exact-match response caching, and its REST endpoint accepts the OpenAI SDK. **Best for:** Teams already running on Cloudflare Workers that want request analytics and response caching in front of their model providers. - **SDK compatibility:** The `/ai/v1/chat/completions` endpoint accepts OpenAI SDK clients, with models named as `openai/...`, `anthropic/...`, or `google/...`. - **Fallbacks:** Request retries and model fallbacks are available for error handling. - **Key management and cost:** Third-party models on the REST endpoint are billed through Cloudflare Unified Billing; bring-your-own-key stores provider keys in Cloudflare Secrets Store. Analytics show requests, tokens, and cost. - **Feature passthrough:** Caching is gateway-side and exact-match: the full request body is hashed, and any difference produces a new cache entry. Cloudflare's caching page does not describe provider prompt caching or reasoning translation. Cloudflare's response cache differs from provider prompt caching, which reuses a prompt prefix at the vendor. Bifrost supports both: provider prompt caching passes through, and gateway-side [semantic caching](https://docs.getbifrost.ai/features/semantic-caching) adds exact-hash and similarity-based replay. ## How Prompt Caching, Tool Use, and Reasoning Pass Through a Gateway Prompt caching, tool calling, and reasoning are the features whose wire formats differ most between Anthropic, OpenAI, and Gemini. A gateway that unifies the request format must translate each one per provider, and redo it when a fallback moves the request to another vendor. The [Bifrost gateway](https://www.getmaxim.ai/bifrost) treats each fallback as a fresh request, so the payload is rebuilt for the provider that serves it. Figure 3 traces one OpenAI-format request carrying reasoning and tools. ![An OpenAI-format request with reasoning and tools reaches Bifrost, which sends an Anthropic-native payload first, then rebuilds a Gemini-native payload as a fallback and returns one normalized response](https://articles-images-cdn.t3.tigrisfiles.io/diagrams/ai-gateways-anthropic-openai-gemini/ai-gateways-anthropic-openai-gemini-fallback-translation.png) *Figure 3: Each fallback is a fresh request, so thinking, tool, and cache-marker fields are rebuilt for whichever provider serves it.* ### Prompt caching across Claude, GPT, and Gemini Anthropic caches a prompt prefix only when the request marks it with `cache_control`, as described in Anthropic's [prompt caching documentation](https://platform.claude.com/docs/en/build-with-claude/prompt-caching). OpenAI and Gemini 2.5+ cache implicitly. Bifrost forwards caller-supplied `cache_control` markers unchanged to Anthropic, and [auto prompt caching](https://docs.getbifrost.ai/features/prompt-caching) can inject a marker for clients that send none. - **Off by default:** a cache marker is a cost decision, so nothing is injected unless the operator enables it per provider. - **Caller markers win:** a request that already carries `cache_control` is forwarded untouched. - **Per attempt:** a fallback re-evaluates injection against the new provider, and Gemini's implicit caching makes injection a no-op there. - **Usage reporting:** Anthropic cache reads and writes surface as `cached_read_tokens` and `cached_write_tokens`. A separate roundup ranks [AI gateways with prompt caching support](https://www.getmaxim.ai/articles/top-5-ai-gateways-with-prompt-caching-support-in-2026/) on this feature alone. ### Tool use and reasoning Tool definitions are restructured per provider. On the [Anthropic route](https://docs.getbifrost.ai/providers/supported-providers/anthropic), `function.parameters` becomes `input_schema` and `tool_choice: "required"` becomes `any`; on Gemini they become `functionDeclarations`. Anthropic beta headers, such as interleaved thinking and the files API, are detected from request features, injected, and validated per provider, so an unsupported header never reaches Vertex or Bedrock. [Reasoning](https://docs.getbifrost.ai/providers/reasoning) uses one OpenAI-style `reasoning` object in requests and `reasoning_details` in responses. Bifrost maps `reasoning.max_tokens` to Anthropic `thinking.budget_tokens` (minimum 1,024) and to Gemini `thinkingConfig.thinkingBudget`, and reports Gemini thought tokens as `reasoning_tokens` in usage. When a feature has no unified equivalent, [passthrough endpoints](https://docs.getbifrost.ai/integrations/passthrough) forward provider-native payloads while keeping logging and observability. ## How to Choose the Best AI Gateway for Your Stack The best AI gateway for Claude, GPT, and Gemini depends first on whether prompts may leave your network, and second on which platform your team already deploys to. Self-hosted gateways answer the first constraint; hosted services trade data control for zero operations. ![Decision flow asking whether prompts must stay in your network and whether the team deploys on Vercel or Cloudflare, ending at self-hosted, platform, or hosted router options](https://articles-images-cdn.t3.tigrisfiles.io/diagrams/ai-gateways-anthropic-openai-gemini/ai-gateways-anthropic-openai-gemini-selection-flow.png) *Figure 4: Data residency decides the category first; existing platform commitments decide the rest.* As Figure 4 shows, model coverage rarely settles the choice, since all five reach the three vendors. The deciding factors are where the gateway runs and how much governance it carries: - **Choose a self-hosted gateway** when prompts contain regulated or customer data, when you need per-team budgets and audit trails, or when gateway latency matters. The open-source Bifrost gateway fits here, with [governance controls](https://www.getmaxim.ai/bifrost/resources/governance) for virtual keys, budgets, and access policies. - **Choose a platform gateway** when your application already runs on Vercel or Cloudflare and the gateway's region and data handling meet your requirements. - **Choose a hosted router** for prototypes that value one bill over control of the traffic path. Teams weighing self-hosted options can compare [open-source LLM gateways](https://www.getmaxim.ai/articles/top-5-open-source-llm-gateways-compared-2026/), and the [explainer on what an AI gateway is](https://www.getmaxim.ai/articles/what-is-an-ai-gateway-architecture-features-and-why-it-matters/) covers where one fits in the stack. ## Frequently Asked Questions ### What are the top AI gateways for Anthropic, OpenAI, and Gemini? The top AI gateways for a Claude, GPT, and Gemini stack are Bifrost, LiteLLM, OpenRouter, Vercel AI Gateway, and Cloudflare AI Gateway. Bifrost and LiteLLM are self-hosted; the other three are managed services. Bifrost ranks first because it accepts native OpenAI, Anthropic, and Google GenAI SDK requests, translates tools, reasoning, and cache markers per provider on fallback, and adds 11 microseconds of overhead at 5,000 RPS. ### Can I call Claude and Gemini models with the OpenAI SDK? Yes. Point the OpenAI SDK at a gateway's OpenAI-compatible endpoint and use provider-prefixed model names such as `anthropic/claude-sonnet-4-5` or `gemini/gemini-2.5-pro`. With Bifrost, only the base URL changes. Calling Anthropic's own compatibility layer directly is less suitable for production, because Anthropic states it does not support prompt caching and is intended mainly for testing. ### Does prompt caching work through an AI gateway? It depends on whether the gateway forwards or translates cache markers. Bifrost forwards Anthropic `cache_control` markers unchanged, can auto-inject them per provider for clients that send none, and reports cache reads and writes separately in usage. OpenAI and Gemini 2.5+ cache implicitly, so no marker is needed. Gateway-side response caching, such as exact-match or semantic caching, is a separate mechanism. ### How much does an AI gateway cost? Self-hosted open-source gateways such as Bifrost and LiteLLM have no per-request fee; you pay for infrastructure and provider tokens. Hosted gateways price differently: OpenRouter charges a fee on bring-your-own-key usage above a free allowance, Vercel states no markup on provider token prices, and Cloudflare bills third-party models through Unified Billing. ### What is the difference between an AI gateway and an LLM router? An LLM router chooses which model or provider serves a request. An AI gateway includes routing but also handles authentication, provider key pooling, budgets, rate limits, logging, and format translation between SDKs. For a three-vendor stack, keys and costs from Anthropic, OpenAI, and Google have to be governed in one place through [virtual keys and budgets](https://docs.getbifrost.ai/features/governance/budget-and-limits). ### Which LLM gateway is the best for enterprise teams? For enterprise teams calling Claude, GPT, and Gemini, Bifrost is the strongest fit: it is open source and self-hosted, supports in-VPC deployment, accepts native SDK formats from all three vendors, and enforces budgets and rate limits per virtual key, team, and customer. Hosted gateways suit teams without data-residency requirements. ## Try Bifrost Today AI gateways let teams run Claude, GPT, and Gemini side by side without three sets of SDK code, keys, and invoices. Bifrost keeps each team's existing SDK, rebuilds tools, reasoning, and cache markers for whichever provider serves a request, and governs keys, budgets, and per-provider spend in one place. Explore the [Bifrost resources hub](https://www.getmaxim.ai/bifrost/resources) for deployment guides, or [book a demo](https://getmaxim.ai/bifrost/book-a-demo) with the Bifrost team to see the gateway running against your own provider mix. ## Read next [![Top 5 AI Gateways for Controlling Shadow AI in 2026](https://storage.ghost.io/c/84/03/8403f2f6-141c-411a-8f55-a32d4291533e/content/images/size/w720/2026/10/top-5-ai-gateways-for-controlling-shadow-ai-bifrost-isometric.png) Shadow AI is the use of AI tools, models, and MCP servers that security teams have not approved and cannot see. This guide ranks five AI gateways for controlling it, including Bifrost with Bifrost Edge, Kong AI Gateway, Cloudflare AI Gateway, and Gravitee.](https://www.getmaxim.ai/articles/top-5-ai-gateways-for-controlling-shadow-ai/) [![Top 5 AI Gateways for SSO and RBAC in 2026](https://storage.ghost.io/c/84/03/8403f2f6-141c-411a-8f55-a32d4291533e/content/images/size/w720/2026/10/top-5-ai-gateways-for-sso-and-rbac-in-2026-bifrost-isometric.png) AI gateways with SSO and RBAC let enterprises tie every model request and every configuration change to a corporate identity. This guide compares Bifrost, Kong AI Gateway, Azure API Management, Gravitee, and Cloudflare AI Gateway on identity, roles, provisioning, and audit.](https://www.getmaxim.ai/articles/top-5-ai-gateways-for-sso-and-rbac-in-2026/) [![Semantic Caching: The Top 5 AI Gateways in 2026](https://storage.ghost.io/c/84/03/8403f2f6-141c-411a-8f55-a32d4291533e/content/images/size/w720/2026/10/semantic-caching-the-top-5-ai-gateways-in-2026-bifrost-isometric.png) Semantic caching serves a stored LLM response when a new prompt means the same thing as an earlier one. This guide compares Bifrost, Kong AI Gateway, Azure API Management, and Cloudflare AI Gateway on match modes, vector stores, thresholds, TTLs, and cache scoping.](https://www.getmaxim.ai/articles/semantic-caching-the-top-5-ai-gateways-in-2026/) ```json { "@context": "https://schema.org", "@type": "Article", "publisher": { "@type": "Organization", "name": "Maxim Articles", "url": "https://www.getmaxim.ai/articles/", "logo": { "@type": "ImageObject", "url": "https://storage.ghost.io/c/84/03/8403f2f6-141c-411a-8f55-a32d4291533e/content/images/size/w256h256/2025/08/thumbnail.png", "width": 60, "height": 60 } }, "author": { "@type": "Person", "name": "Kuldeep Paul", "image": { "@type": "ImageObject", "url": "https://storage.ghost.io/c/84/03/8403f2f6-141c-411a-8f55-a32d4291533e/content/images/2025/08/1727978381919.jpeg", "width": 800, "height": 800 }, "url": "https://www.getmaxim.ai/articles/author/kuldeep/", "sameAs": [] }, "headline": "Top 5 AI Gateways for Anthropic, OpenAI, and Gemini", "url": "https://www.getmaxim.ai/articles/top-5-ai-gateways-for-anthropic-openai-and-gemini/", "datePublished": "2026-10-07T06:24:00.000Z", "dateModified": "2026-10-10T06:26:40.000Z", "image": { "@type": "ImageObject", "url": "https://storage.ghost.io/c/84/03/8403f2f6-141c-411a-8f55-a32d4291533e/content/images/size/w1200/2026/10/top-5-ai-gateways-for-anthropic-openai-and-gemini-bifrost-isometric.png", "width": 1200, "height": 675 }, "keywords": "AI Gateway", "description": "AI gateways put Claude, GPT, and Gemini models behind one API with shared keys, fallbacks, and cost tracking. 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