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The Best AI Gateway Providers
The following list presents providers that convincingly address the requirements outlined here—ranging from specialized European platforms and open-source projects to the AI gateway services offered by global cloud providers. The list of features and providers is a selection and is not intended to be exhaustive; all information has been compiled to the best of our knowledge based on official manufacturer information, documentation, and wikis, without any guarantee as to its timeliness, completeness, or accuracy.
Best AI Gateway for Businesses
Seven Criteria for Your Shortlist
An AI gateway consolidates access to AI models and enforces central rules for their use. To ensure that the solutions are comparable, you should apply the same criteria to all providers:
Operational Location and Jurisdiction: Who operates the gateway, where are requests processed, and which other service providers are involved? Distinguish between the location of the gateway, model processing, and log data.
Model Coverage and Fault Tolerance: Which model providers can be integrated? Are there self-hosted models, configurable fallbacks, and routing rules for outages?
Personal data protection: Are sensitive details detected, blocked, or replaced with placeholders? Can the gateway restore the original values in the response?
Guardrails: What checks are in place for inputs and responses—for example, against prompt injection, unwanted content, or the disclosure of confidential information?
Cost and Budget Control: Can expenses be assigned to and limited for individual teams, projects, or API keys? Distinguish between spending alerts and blocking further requests.
Logging and Traceability: What metadata and content are stored? Who has access, what retention periods apply, and can changes to the configuration be traced?
Price and Operating Costs: In addition to model and gateway costs, also consider additional security services, log storage, and your own operations. The following profiles do not specify specific prices.
Amazon Bedrock
Amazon Bedrock provides models from various vendors via common interfaces and enhances them with security and governance features. For organizations with AWS workloads, model access can be integrated into existing identity management, monitoring, and logging frameworks. Typical use cases include cloud applications, AI agents, and applications with connected knowledge sources. ²
Bedrock Guardrails checks inputs and responses for, among other things, unwanted content, restricted topics, and sensitive information. Personal data can be blocked or masked. For cost allocation, Bedrock supports, among other things, IAM identities, application profiles, and metadata from individual requests. AWS Budgets supplements spending monitoring with thresholds and notifications.
When it comes to logging, one distinction is important: Model Invocation Logging is disabled by default and must be configured for the desired region. If enabled, original inputs may still appear in the log even if Guardrails masks them for the model invocation. Log security must therefore be handled separately. ²
Bedrock is particularly well-suited for companies that operate AI within their AWS architecture and want to implement governance using AWS services.
AI Gateway
Cloudflare AI Gateway is a fully managed service on the global Cloudflare network. It aggregates access to various model providers and supplements it with caching, rate limiting, routing, and fallbacks. For teams already using Cloudflare Workers, this allows the AI integration to be incorporated into their existing application architecture. ³
Security features include content checks and Data Loss Prevention (DLP). DLP scans prompts and responses for sensitive information and can flag matches or block the relevant traffic. The documented “Flag” and “Block” actions differ from reversible masking, which is followed by restoration of the original values. When streaming response checking is enabled, Cloudflare also buffers the entire response before delivery.
Spend limits cap cumulative costs by model, provider, or custom metadata such as team and user ID. Logging is enabled by default but can be configured. A dedicated parameter can be used to suppress the storage of request and response content, while metadata such as token consumption, costs, and runtime are retained. ³
Cloudflare AI Gateway is particularly well-suited for SaaS products, APIs, and web applications already running on Cloudflare infrastructure.
Apigee und Model Armor
Google Cloud integrates the Apigee API management platform with Model Armor, a security service for AI applications. Companies already using Apigee can use it to manage model and agent traffic through the same administrative structures as traditional APIs. ⁴
Model Armor checks prompts and responses for prompt injection, jailbreak attempts, and sensitive data, among other things. Integration with Sensitive Data Protection enables detailed detection rules and configurable de-identification. In Apigee, these checks are integrated into the API proxies via policies. The documented integration supports both detection-only and detection-with-blocking modes.
Token quotas and prompt token limits are available for usage control. Model Armor leaves logging to the customer: When Cloud Logging is enabled, metadata and excerpts of the inspected content can be stored, depending on the configuration. The availability of the involved services must match the selected region. ⁴
This combination is well-suited for API-centric organizations on Google Cloud that want to extend their existing API governance to AI applications.
AI Gateway
Kong is expanding its API management capabilities to include control over model, tool, and agent traffic. The AI Gateway consolidates various model providers behind a unified interface and supports routing, load balancing, and fallbacks. It can be operated either with Konnect as the management layer or self-hosted. ⁵
Security features include AI Sanitizer for removing personally identifiable information, AI Prompt Guard for restricted topics and terms, and semantic checks of prompts and responses. Additionally, security services from AWS, Microsoft, and Google can be integrated. For the Model Context Protocol (MCP), Kong provides controls for authentication, access rights, and the use of connected tools.
Token budgets, cost calculation, and spending limits support consumption management. Semantic caching and prompt compression can reduce the number or scope of paid model calls. Logs, metrics, and OpenTelemetry, among other tools, are available for analysis. ⁵
Kong is particularly relevant for companies with an existing Kong platform and for platform teams that want to design their own gateway configuration and operating model.
LiteLLM
LiteLLM is an open-source gateway for self-hosting. It translates the APIs of more than 100 model providers into an OpenAI-compatible format. This provides applications with a common endpoint, while the platform team determines the gateway’s location and infrastructure. ⁶
Virtual keys control access for individuals, teams, and projects. They can be linked to model permissions, budgets, and rate limits. LiteLLM tracks the costs of individual requests and supports load balancing, automatic retries, and fallbacks. Logs and metrics can be forwarded to tools such as Langfuse, Datadog, Prometheus, and OpenTelemetry.
Microsoft Presidio can be integrated to protect personal data. Detected values can be masked, or the request can be blocked. With the appropriate configuration, LiteLLM restores placeholders in the model response to their original values. The Presidio services required for this must be provisioned separately. Advanced features such as SSO and administrative audit logs are included in the Enterprise offering. ⁶
LiteLLM is well-suited for internal platform teams that want to build their own gateway layer and integrate it deeply into their systems.
Azure API Management
Microsoft provides AI Gateway capabilities through Azure API Management. The solution is designed for organizations that want to integrate model access into their existing Azure architecture and manage it centrally. This includes integration with existing identity services and monitoring via Azure Monitor or Application Insights. ⁷
Token-based limits and quotas restrict usage per API consumer and time period. Semantic caching allows responses to similar queries to be reused. Policies can be used to integrate Azure AI Content Safety for prompt verification. These features are not limited to Azure OpenAI but can also be used with other supported model backends.
Azure Language offers an additional component for detecting and redacting personal data. This is distinct from content moderation via Content Safety and must be integrated into the respective processing workflow. Logging of prompts, responses, and token consumption in API Management is also configured and enabled. ⁷
Azure API Management is particularly well-suited for applications and agents in organizations with a strong Microsoft stack.
Myra AI Workspace
Myra AI Workspace combines the governance features of an AI gateway with a user interface for employees. The Workspace solution is designed for organizations that want to integrate their own applications while simultaneously introducing AI into their business units. More than 20 provider integrations are available via an OpenAI-compatible endpoint. Myra’s own models run on EU infrastructure; external model providers can also be integrated. In addition to operating as a service, an on-premises offering is documented. ⁸
A two-stage guardrail pipeline combines, among other things, pattern and term recognition with additional checks for personally identifiable information (PII) and prompt injection. The PII Protector replaces detected personally identifiable information with tokens before it is passed on to external models and can restore the original values in the response. For this to work, the check must be configured accordingly for both the request and the response. For a request processed entirely using the user’s own Myra models, masking is not required because no external model provider is involved.
For privacy-critical applications, the error behavior is also relevant: Outside of a mandatory PII policy, the PII Protector may, depending on the settings, allow unmasked requests to pass through in the event of a detection service failure. Mandatory masking or a “fail-closed” configuration prevents this behavior. Projects can also be restricted so that only local Myra models are used.
Budgets apply at the gateway, tenant, and API token levels. In addition, there are rate limits, routing rules, fallbacks, and circuit breakers. Structured request logs record, among other things, the provider, model, token consumption, costs, cache status, and guardrail results. Cost tracking requires stored pricing information for the respective model. The scope and retention of content data must be considered separately: The documentation also describes standard, unmasked turn-replay data sets with limited retention periods, as well as exceptions and options for deactivation. ⁸
Myra is particularly well-suited for companies that wish to jointly procure and operate a gateway and chat workspace. Shared projects, file uploads, and document exports support use by business units.
OpenRouter
According to OpenRouter, it aggregates more than 500 models from over 80 providers behind an OpenAI-compatible API. Models are switched using the model ID. Consolidated billing and automatic fallbacks simplify access for development teams that want to compare different models and deploy them in production. ⁹
Guardrails combine budget limits, model and provider approvals, as well as data retention and regional requirements. The Sensitive Info Guardrail detects sensitive information in inputs and replaces it with placeholders or blocks the request. It does not check model responses. NLP-based detection of names and addresses is marked as beta; according to the documentation, if this check times out, the request continues to process.
Business and Enterprise customers can use regional routing. Here, “Europe” does not refer exclusively to the EU, but to the EU/EEA, the United Kingdom, and Switzerland. Regional compliance for prompts and responses does not apply across the board to account, billing, and operational data. For custom deployments connected via Bring Your Own Key (BYOK), the region remains the customer’s responsibility. ⁹
OpenRouter is particularly well-suited for product and development teams seeking hosted access to many models via a shared API.
Portkey: Prisma AIRS AI Gateway
Portkey is now known as Prisma AIRS AI Gateway. The platform combines model access with observability, prompt management, guardrails, and governance. According to the product page, more than 1,600 models can be accessed via a common API. ¹⁰
SaaS and hybrid deployment options are documented. In the hybrid model, the gateway and data layers run in the customer’s own environment, while Portkey operates the management layer. According to current documentation, fully isolated new installations are no longer provided; older descriptions of such operational options should therefore not be interpreted as current offerings.
PII redaction replaces detected sensitive information with standard placeholders. It can be configured for both input and output and, according to the documentation, is intentionally non-reversible. Cost and token limits are available for Enterprise and select Pro customers. Administrative audit logs are included in the Enterprise offering; these should be distinguished from request logs and traces. ¹⁰
Portkey is suitable for development teams with high demands for evaluating production AI applications, as well as for organizations that wish to run their data layer on their own network.
Traefik Hub AI Gateway
Traefik Labs combines the API Gateway, AI Gateway, and MCP Gateway into a single control plane. The product is designed for cloud-native and hybrid environments where model and agent traffic needs to be controlled alongside existing APIs. ¹¹
The AI Gateway supports cross-vendor routing and failover. Security checks can be configured using multiple components. These include checks for personally identifiable information, undesirable topics, and jailbreak attempts. Depending on the protection component, sensitive content can be masked or blocked. Checks configured as critical halt processing even if the check itself fails.
Token-based rate limits and quotas can be assigned to individual users, teams, or API keys based on identity attributes. Metrics, traces, and access logs are available for analysis. Offline operation without a connection to the Traefik Hub dashboard is possible, but requires appropriate activation and must be configured when the gateway is created. ¹¹
Traefik is well-suited for platform teams that want to control API, model, and MCP traffic within their own infrastructure using common rules.
AI Gateway Providers Side by Side
“Origin” refers to the provider’s or project sponsor’s corporate headquarters—not the location where data processing takes place, i.e., where prompts, responses, and log data are processed.
For self-operated gateways, the operating organization determines the gateway location; connected external models must be considered separately. Regional routing services also have their own scopes of application: OpenRouter’s “Europe” region, for example, includes not only the EU/EEA but also the United Kingdom and Switzerland. ⁹
Amazon Bedrock
🇺🇸
Managed AWS Service
Models from various manufacturers via common interfaces. ²
Cloudflare AI Gateway
🇺🇸
Hosted Service
Integration with various model providers. ³
Google Apigee und Model Armor
🇺🇸
Combination of Google Cloud Services
Cross-model integration via API proxies. ⁴
Kong AI Gateway
🇺🇸
Managed by Konnect or self-hosted
Multiple model providers via a single interface. ⁵
LiteLLM
🇺🇸
Self-Operation
More than 100 model providers via an OpenAI-compatible endpoint. ⁶
Microsoft Azure API Management
🇺🇸
Azure service with configurable gateway features
Azure OpenAI and other supported model backends. ⁷
Myra AI Workspace
🇩🇪
Hosted service; on-premises option available
More than 20 provider integrations and proprietary models on EU infrastructure. ⁸
OpenRouter
🇺🇸
Hosted Service
More than 500 models from over 80 manufacturers. ⁹
Portkey / Prisma AIRS AI Gateway
🇺🇸
SaaS or Hybrid Operation
More than 1,600 models, according to the product page. ¹⁰
Traefik Hub AI Gateway
🇫🇷 / 🇺🇸
Proprietary infrastructure; offline option available
Cross-provider model integration. ¹¹
Amazon Bedrock
🇺🇸
Cloudflare AI Gateway
🇺🇸
Google Apigee und Model Armor
🇺🇸
Kong AI Gateway
🇺🇸
LiteLLM
🇺🇸
Microsoft Azure API Management
🇺🇸
Myra AI Workspace
🇩🇪
OpenRouter
🇺🇸
Portkey / Prisma AIRS AI Gateway
🇺🇸
Traefik Hub AI Gateway
🇫🇷 / 🇺🇸
Protection of Sensitive Data and Logging
The following table distinguishes between the handling of sensitive content and its logging. Masking data before it enters the model does not automatically mean that stored logs will contain only masked data. ², ⁸
Query logs document model calls and their processing. Administrative audit logs, on the other hand, record changes to settings and permissions, for example. These functions should be evaluated separately. ¹⁰
Amazon Bedrock
Guardrails can block or mask sensitive information in inputs and responses.
Model invocation logging is optional. Enabled logs may contain original inputs despite guardrail masking. ²
Cloudflare AI Gateway
DLP checks prompts and responses and can flag matches or block traffic.
Logging is enabled by default. Content logging can be disabled, while usage metadata is retained. ³
Google Apigee und Model Armor
Verification and blocking; configurable de-identification via Sensitive Data Protection.
Customer-configured cloud logging; depending on the settings, excerpts of reviewed content may also be stored. ⁴
Kong AI Gateway
AI Sanitizer and other guardrails check or clean up inputs and responses.
Logs, metrics, and OpenTelemetry support the analysis of AI traffic. ⁵
LiteLLM
Masking or blocking with Presidio; restoring the original values in responses is configurable.
Query logs and monitoring integrations; administrative audit logs are included in the Enterprise offering. ⁶
Microsoft Azure API Management
Content Safety Guidelines; for personal data, Azure Language can be integrated as an additional component.
Logging of prompts, responses, and token usage is configurable; analysis is performed using Azure monitoring services. ⁷
Myra AI Workspace
Reversible tokenization for external models. With fully local Myra processing, masking is not required.
Query metadata and Guardrail results; content and replay storage must be configured separately. ⁸
OpenRouter
Placeholders or blocked input. The Sensitive Info Guardrail does not check model responses.
Usage metadata is collected; separate settings and terms apply to content logging and regional processing. ⁹
Portkey / Prisma AIRS AI Gateway
Non-reversible redaction of sensitive information; configurable for both input and output.
Query logs and traces; administrative audit logs are an enterprise feature. ¹⁰
Traefik Hub AI Gateway
Masking or blocking using combinable protective components.
Metrics, traces, and access logs. ¹¹
Amazon Bedrock
Guardrails can block or mask sensitive information in inputs and responses.
Cloudflare AI Gateway
DLP checks prompts and responses and can flag matches or block traffic.
Google Apigee und Model Armor
Verification and blocking; configurable de-identification via Sensitive Data Protection.
Kong AI Gateway
AI Sanitizer and other guardrails check or clean up inputs and responses.
LiteLLM
Masking or blocking with Presidio; restoring the original values in responses is configurable.
Microsoft Azure API Management
Content Safety Guidelines; for personal data, Azure Language can be integrated as an additional component.
Myra AI Workspace
Reversible tokenization for external models. With fully local Myra processing, masking is not required.
OpenRouter
Placeholders or blocked input. The Sensitive Info Guardrail does not check model responses.
Portkey / Prisma AIRS AI Gateway
Non-reversible redaction of sensitive information; configurable for both input and output.
Traefik Hub AI Gateway
Masking or blocking using combinable protective components.
Which use case corresponds to which AI gateway provider
Most of their AI applications run on AWS.
Amazon Bedrock – because of its integration with the existing AWS architecture. ²
You're developing with Cloudflare Workers and need a managed gateway service.
Cloudflare AI Gateway. ³
They want to extend existing Apigee governance to AI traffic.
Google Apigee with Model Armor. ⁴
You are already using Kong for API management.
Kong AI Gateway. ⁵
Your platform team would like to choose the gateway, location, and integrations themselves.
LiteLLM, Kong self-hosted, or Traefik Hub. ⁶, ⁵, ¹¹
Their applications and identity structures are based on Microsoft Azure.
Azure API Management. ⁷
You need a gateway API and a shared AI interface for business units.
Myra AI Workspace. ⁸
Personal values should be replaced before the external model and restored in the response.
Myra with PII Protector or LiteLLM with Presidio and the appropriate configuration. ⁸, ⁶
You are looking for hosted access to many models with shared billing.
OpenRouter. ⁹
The data layer should run in your environment, but management should be handled as a service.
Portkey in hybrid mode. ¹⁰
API, model, and MCP traffic are to be managed through a shared infrastructure.
Kong or Traefik Hub. ⁵, ¹¹
Most of their AI applications run on AWS.
Amazon Bedrock – because of its integration with the existing AWS architecture. ²
You're developing with Cloudflare Workers and need a managed gateway service.
Cloudflare AI Gateway. ³
They want to extend existing Apigee governance to AI traffic.
Google Apigee with Model Armor. ⁴
You are already using Kong for API management.
Kong AI Gateway. ⁵
Your platform team would like to choose the gateway, location, and integrations themselves.
LiteLLM, Kong self-hosted, or Traefik Hub. ⁶, ⁵, ¹¹
Their applications and identity structures are based on Microsoft Azure.
Azure API Management. ⁷
You need a gateway API and a shared AI interface for business units.
Myra AI Workspace. ⁸
Personal values should be replaced before the external model and restored in the response.
Myra with PII Protector or LiteLLM with Presidio and the appropriate configuration. ⁸, ⁶
You are looking for hosted access to many models with shared billing.
OpenRouter. ⁹
The data layer should run in your environment, but management should be handled as a service.
Portkey in hybrid mode. ¹⁰
API, model, and MCP traffic are to be managed through a shared infrastructure.
Kong or Traefik Hub. ⁵, ¹¹
FAQ: The Best AI Gateway Providers
These terms are often used to describe similar functions: a central layer between applications and model providers. The range of functions is more important than the terminology. Some solutions focus on language model access, while others also incorporate MCP tools and communication between agents. ⁵, ⁶
Sources
Here you will find the sources used for this article, listed in chronological order along with the date each website was accessed.
- 2026
Bitkom: Künstliche Intelligenz in Deutschland | Studie 2025: https://www.bitkom.org/sites/main/files/2026-02/bitkom-studienbericht-ki.pdf
- 2026
Amazon Web Services: Amazon Bedrock Guardrails. https://docs.aws.amazon.com/bedrock/latest/userguide/guardrails.html
Amazon Web Services: Remove PII from conversations by using sensitive information filters. https://docs.aws.amazon.com/bedrock/latest/userguide/guardrails-sensitive-filters.html
Amazon Web Services: Track usage and costs in Amazon Bedrock. https://docs.aws.amazon.com/bedrock/latest/userguide/cost-management.html
Amazon Web Services: Managing your costs with AWS Budgets. https://docs.aws.amazon.com/cost-management/latest/userguide/budgets-managing-costs.html
Amazon Web Services: Monitor model invocation using CloudWatch Logs and Amazon S3. https://docs.aws.amazon.com/bedrock/latest/userguide/model-invocation-logging.html
- 2026
Cloudflare: AI Gateway: Features. https://developers.cloudflare.com/ai-gateway/features/
Cloudflare: Data Loss Prevention (DLP). https://developers.cloudflare.com/ai-gateway/features/dlp/
Cloudflare: Spend limits. https://developers.cloudflare.com/ai-gateway/features/spend-limits/
Cloudflare: Logging. https://developers.cloudflare.com/ai-gateway/observability/logging/
- 2026
Google Cloud: Model Armor integration with Google Cloud services. https://docs.cloud.google.com/model-armor/integrations
Google Cloud: Model Armor overview. https://docs.cloud.google.com/model-armor/overview
Google Cloud: Create and manage templates. https://docs.cloud.google.com/model-armor/manage-templates
- 2026
Kong: AI Gateway. https://developer.konghq.com/ai-gateway/
- 2026
LiteLLM: LiteLLM AI Gateway. https://docs.litellm.ai/docs/simple_proxy
LiteLLM: PII, PHI Masking – Presidio. https://docs.litellm.ai/docs/proxy/guardrails/pii_masking_v2
LiteLLM: Enterprise. https://docs.litellm.ai/docs/enterprise
- 2026
Microsoft: AI gateway capabilities in Azure API Management. https://learn.microsoft.com/en-us/azure/api-management/genai-gateway-capabilities
Microsoft: What is PII detection in Azure Language?. https://learn.microsoft.com/en-us/azure/ai-services/language-service/personally-identifiable-information/overview
- 2026
Myra Security: Myra AI Workspace – Documentation. https://ai-docs.myra.eu/
Myra Security: Getting access. https://ai-docs.myra.eu/getting-started/installation/
Myra Security: PII Protector. https://ai-docs.myra.eu/settings/security/guardrails/pii-protector/
Myra Security: Data protection and compliance. https://ai-docs.myra.eu/settings/security/data-protection/
Myra Security: Projects. https://ai-docs.myra.eu/workspace/projects/
- 2026
OpenRouter: Model and provider overview. https://openrouter.ai/
OpenRouter: Guardrails. https://openrouter.ai/docs/guides/features/guardrails
OpenRouter: Sensitive Info Guardrail. https://openrouter.ai/docs/guides/features/guardrails/sensitive-info
OpenRouter: In-Region Routing. https://openrouter.ai/docs/guides/features/in-region-routing
OpenRouter: Data collection. https://openrouter.ai/docs/guides/privacy/data-collection
- 2026
Portkey: Prisma AIRS AI Gateway. https://portkey.ai/
Portkey: Feature Comparison. https://portkey.ai/docs/product/product-feature-comparison
Portkey: PII Redaction. https://portkey.ai/docs/product/guardrails/pii-redaction
Portkey: Budget Limits. https://portkey.ai/docs/product/ai-gateway/virtual-keys/budget-limits
Portkey: Audit Logs. https://docs.portkey.ai/docs/product/enterprise-offering/audit-logs
Portkey: Model Pricing for Air-Gapped Deployments. https://docs.portkey.ai/docs/self-hosting/airgapped/model-pricing
- 2026
Traefik Labs: Traefik AI Gateway. https://traefik.io/solutions/ai-gateway
Traefik Labs: Composable, Multi-Vendor AI Safety Pipeline. https://traefik.io/press/traefik-labs-new-multi-vendor-composable-ai-safety-pipeline
Traefik Labs: AI Governance at the Gateway with Jev and Traefik Hub. https://traefik.io/blog/three-ways-to-run-jev-behind-traefik-hub
Traefik Labs: Offline Mode. https://doc.traefik.io/traefik-hub/api-gateway/setup/installation/offline-mode
About the author
Stefan Bordel
Senior Editor
About the author
Stefan Bordel has been working as Editor and Technical Writer at Myra Security since 2020. He is responsible for the strategic development and editorial management of all content formats – from website content and specialist publications to whitepapers, social media communication, and technical documentation. In this role, he combines solid expertise from IT journalism with in-depth technical understanding in the field of cybersecurity. As a long-time Linux enthusiast, he closely follows developments in the IT industry both professionally and personally.