DEV Community: Kwame Asante

Open Source Claude Code Gateway: Architecture, Setup, and Governance

Kwame Asante — Thu, 17 Sep 2026 20:07:45 +0000

TL;DR

An open source Claude Code gateway acts as a reverse proxy between Anthropic's terminal coding agent and upstream LLM providers to enforce budgets, routing, and access policies.
Running an open source gateway locally or in a private VPC keeps proprietary codebases secure while eliminating single-provider dependency.
Bifrost adds only 11 microseconds of routing overhead at 5,000 requests per second, making it the highest-throughput open source control plane for terminal coding agents.
Centralized governance lets platform teams allocate virtual keys, enforce token quotas, and audit Model Context Protocol (MCP) tool execution across entire engineering teams.

An open source Claude Code gateway is an infrastructure proxy that intercepts API calls from Anthropic's terminal coding assistant to enforce custom routing, budget caps, security policies, and multi-provider failover. Bifrost, a high-performance open-source AI gateway written in Go by Maxim AI, provides a centralized control plane that intercepts Anthropic Messages API traffic and translates it across multiple model providers without modifying the Claude Code client binary. By placing an open source gateway between local developer environments and upstream inference backends, engineering organizations gain operational visibility into agentic coding loops while keeping proprietary code inside their trusted security boundaries.

What Is an Open Source Claude Code Gateway?

An open source Claude Code gateway is a self-hosted network proxy that intercepts requests sent by Anthropic's Claude Code CLI, enforces authentication and traffic policies, and forwards those requests to specified LLM backends. It translates Anthropic-formatted API payloads, aggregates telemetry, and returns streaming responses transparently to the terminal.

Claude Code functions as an autonomous, terminal-native agent capable of reading directories, modifying files, executing bash commands, and running build suites. By default, the client directs all traffic to api.anthropic.com over HTTPS. Because Claude Code relies on standard environment variables such as ANTHROPIC_BASE_URL and ANTHROPIC_API_KEY to configure its target network destination, developers can route traffic away from the default hosted endpoints into a self-hosted proxy.

An open source gateway takes responsibility for several operational concerns that the CLI client delegates to network infrastructure:

Protocol Translation: Converting Anthropic Messages API structures into schemas accepted by OpenAI, Google Gemini, AWS Bedrock, Azure OpenAI, or local runtimes like Ollama and vLLM.
Traffic Routing: Directing specific agent operations or model aliases to designated providers based on cost, context window, or operational health.
Identity and Attribution: Authenticating individual developers through local or centralized virtual credentials rather than exposing shared root provider tokens.
Context Caching and Optimization: Intercepting repeated prompt prefixes and codebase context to serve responses from cache or reduce redundant input processing.

Operating an open source gateway ensures that the underlying proxy code is inspectable, auditable, and modifiable to match internal security policies. Organizations with strict data governance mandates avoid third-party hosted proxies that could inspect, log, or store sensitive source code transmitted during terminal agent sessions.

Why Engineering Teams Need a Gateway for Terminal Coding Agents

Terminal coding agents generate token usage patterns that differ fundamentally from standard chat interfaces, creating operational risks around budget exhaustion, API rate limits, and unmonitored codebase transmission. A single automated debugging session can issue dozens of iterative tool executions, consuming hundreds of thousands of context tokens within minutes.

When developers use unmanaged terminal agents, three operational problems surface:

Unpredictable Spend and Token Runaway: In complex refactoring workflows, coding agents execute continuous trial-and-error loops until tests pass or iteration limits trigger. Without hard budget constraints enforced at the network layer, a malfunctioning agent script can exhaust enterprise API tiers overnight.
Provider Availability Bottlenecks: Direct reliance on a single hosted API endpoint leaves engineering velocity vulnerable to upstream provider outages, HTTP 429 rate limits, and latency spikes. An intermediary gateway allows teams to configure automatic fallbacks to secondary cloud regions or alternative frontier models.
Loss of Auditability and Compliance: Security teams require verifiable records of what source code leaves local workstations, which external tool APIs are invoked, and which credentials authenticate those transactions. Routing CLI traffic through a unified governance layer creates an immutable audit trail without slowing down developer velocity.

Deploying an open source gateway gives infrastructure leaders a dedicated point of control to govern agent usage systematically, turning individual developer experiments into an observable platform workflow.

Architectural Patterns: Local Daemon vs. Centralized Proxy

Organizations deploying an open source Claude Code gateway typically choose between two architectural designs: a local workstation daemon or a centralized enterprise proxy. Each model addresses different trade-offs between setup simplicity, latency overhead, and administrative governance.

The following table contrasts the two deployment patterns across core operational criteria:

Architecture Dimension	Workstation Daemon (Local-First)	Centralized Enterprise Gateway
Primary Host Location	Developer machine (`localhost:8080`)	Private Cloud / VPC (Kubernetes, ECS)
Network Latency Overhead	Zero external network hops (<1 ms)	Depends on VPC topology (typically 5-20 ms)
Credential Management	API keys managed on local machines	Centralized secret vaulting (IAM, HashiCorp Vault)
Policy Enforcement	Advisory; user can bypass shell variables	Mandatory; enforced via internal routing and SSO
Offline Model Execution	Direct connection to local Ollama/llama.cpp	Forwarded to private internal inference clusters
Telemetry Aggregation	Local log files or opt-in forwarders	Native Prometheus, OpenTelemetry, and Datadog streams
Best Suited For	Solo developers, prototyping, air-gapped tasks	Multi-team organizations, regulated industries

The Local Workstation Daemon Pattern

In a local setup, the open source gateway runs as a background process or lightweight container directly on the engineer's workstation. The developer points ANTHROPIC_BASE_URL to http://localhost:8080 (or another local port).

This design provides maximum privacy and minimal network latency. Because data stays on the local loopback interface until sent to the upstream provider, developers retain complete control over their local configurations. It also facilitates direct routing to local inference engines like Ollama or vLLM when working without internet access. However, local daemons offer limited value for platform teams seeking centralized cost accounting, organization-wide rate limits, or compliance auditing, as local environment configurations can be modified or disabled by the workstation user.

The Centralized Enterprise Gateway Pattern

In a centralized architecture, platform engineers deploy the open source gateway in a shared virtual private cloud (VPC) behind an internal load balancer. Developers authenticate using personal or team-scoped credentials issued by the gateway control plane, while the gateway holds the actual production credentials for AWS Bedrock, Google Vertex AI, Azure, and Anthropic.

This pattern isolates upstream provider keys from developer endpoints, enforces organizational budget rules, and unifies audit logging into security information and event management (SIEM) pipelines. When paired with internal identity providers (such as Okta or Microsoft Entra ID), access permissions automatically revoke when an employee changes roles or leaves the organization.

Setting Up Bifrost as an Open Source Claude Code Gateway

Bifrost is designed for high-concurrency, low-latency environments, adding only 11 microseconds of routing overhead per request at 5,000 requests per second in sustained benchmarks. It functions as a native Anthropic API drop-in replacement, allowing developers to route Claude Code sessions through it with minimal configuration.

1. Launching the Gateway

You can launch Bifrost locally using npx, binary downloads, or Docker:

# Launch Bifrost locally with zero configuration overhead
npx -y @maximhq/bifrost

For production environments, Bifrost deploys as a stateless container inside Kubernetes or Amazon ECS, pulling configurations dynamically from declarative stores. The gateway documentation covers containerized gateway setup and deployment topologies.

2. Configuring Provider Credentials

Bifrost manages upstream model providers via its web dashboard (accessible at http://localhost:8080 by default) or via static configuration files. To set up Anthropic and AWS Bedrock as upstream targets, supply your respective provider keys:

export ANTHROPIC_API_KEY="sk-ant-api..."
export AWS_ACCESS_KEY_ID="AKIA..."
export AWS_SECRET_ACCESS_KEY="..."
export AWS_REGION="us-east-1"

Refer to the provider configuration guide for the full list of supported parameters and credential store connectors.

3. Pointing Claude Code to the Gateway

Claude Code honors standard Anthropic configuration variables. You can configure the CLI by setting shell environment variables or modifying the global settings file located at ~/.claude/settings.json:

{
  "env": {
    "ANTHROPIC_BASE_URL": "http://localhost:8080/anthropic",
    "ANTHROPIC_API_KEY": "your-bifrost-virtual-key"
  }
}

Alternatively, export the variables in your active shell profile:

export ANTHROPIC_BASE_URL="http://localhost:8080/anthropic"
export ANTHROPIC_API_KEY="your-bifrost-virtual-key"

Once exported, launching the CLI via claude routes all inference traffic, tool calls, and streaming chunks directly through Bifrost.

4. Interactive Configuration with Bifrost CLI

To avoid manual environment variable management across terminal sessions, developers can use Bifrost CLI. The CLI is an interactive terminal interface that connects coding agents like Claude Code, Codex CLI, and Gemini CLI to the gateway automatically:

# Run the interactive agent launcher
npx -y @maximhq/bifrost-cli

The CLI tool auto-detects running local gateways, prompts for target model selections, mounts necessary MCP tool definitions, and starts Claude Code in a persistent terminal session. Details on agent-specific flags are detailed in the Bifrost Claude Code documentation.

Multi-Provider Routing and Fallback Strategies

A primary technical benefit of routing Claude Code through an open source gateway is breaking the dependency on a single model endpoint. Claude Code relies on distinct model tiers to balance reasoning performance and response speed:

Opus Tier: Complex system design, multi-file code refactoring, architectural planning.
Sonnet Tier: Primary daily coding, automated testing, standard feature implementation.
Haiku Tier: Fast syntax checks, brief command summaries, lightweight file inspections.

Bifrost allows engineering teams to map these logical tiers to different model providers using explicit routing rules. For example, a team can route everyday Sonnet requests to Amazon Bedrock to keep data inside a specific AWS VPC boundary, while routing complex Opus tasks directly to Anthropic or OpenAI.

# Override model tiers to route across multiple providers
export ANTHROPIC_DEFAULT_SONNET_MODEL="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0"
export ANTHROPIC_DEFAULT_OPUS_MODEL="openai/gpt-4o"
export ANTHROPIC_DEFAULT_HAIKU_MODEL="groq/llama-3.3-70b-versatile"

Configured Automatic Fallbacks

When an upstream provider returns HTTP 429 (Too Many Requests), HTTP 503 (Service Unavailable), or experiences network timeouts, agent sessions typically terminate with unrecoverable errors.

Bifrost implements automatic fallbacks that seamlessly reroute failed requests to an ordered list of backup providers. If an AWS Bedrock endpoint encounters quota exhaustion during a high-throughput sprint, the gateway immediately redirects the pending payload to Google Cloud Vertex AI or direct Anthropic API endpoints without interrupting the developer's terminal flow.

[Claude Code CLI] 
       │
       ▼ (Anthropic Messages API)
[Bifrost AI Gateway]
       │
       ├─── Primary: AWS Bedrock (Claude 3.5 Sonnet) ──► [HTTP 429 / Timeout]
       │                                                         │
       └─── Fallback: Vertex AI (Claude 3.5 Sonnet) ◄────────────┘

The gateway handles retry backoff intervals, payload reformatting, and credential substitution automatically, ensuring that agent execution loops maintain continuous momentum.

Enforcing Budgets, Virtual Keys, and Cost Controls

When multiple developers run Claude Code simultaneously, tracking aggregate expenditures on a single shared provider invoice makes cost attribution impossible. Without network-level controls, organizations risk unexpected billing spikes caused by runaway sub-agent loops.

Bifrost addresses this challenge through virtual keys. A virtual key is an internal credential issued by the gateway that maps to specific governance rules without revealing the underlying upstream API keys.

[Developer: Alice]  ──► [Virtual Key: vk_team_frontend]  ──► Budget: $150/mo ──┐
                                                                               ├──► [Bifrost Gateway] ──► Upstream Providers
[Developer: Bob]    ──► [Virtual Key: vk_team_backend]   ──► Budget: $300/mo ──┘

Platform teams can configure fine-grained budget and rate limits per virtual key:

Monthly Spend Caps: Define hard or soft financial thresholds (e.g., $100 per developer per month). When the limit is reached, the gateway rejects subsequent requests with descriptive error messages.
Request Rate Limits: Bound the number of requests per minute (RPM) and tokens per minute (TPM) to prevent individual users from consuming the entire enterprise API quota.
Model Whitelisting: Restrict specific junior engineering groups to cost-efficient models while reserving high-tier reasoning models for authorized senior staff.

Furthermore, Bifrost incorporates semantic caching, which indexes prompt representations in vector storage. When Claude Code re-evaluates static codebase contexts or identical boilerplate structures, the gateway returns cached model completions directly, significantly reducing upstream token expenditures and response latency. Detailed enterprise planning patterns are outlined in the LLM Gateway Buyer's Guide.

Governed MCP Tool Access for Autonomous Coding

Claude Code's real-world power stems from its integration with the Model Context Protocol (MCP), an open standard enabling language models to discover and interact with external data sources, APIs, and tools. Developers can register MCP servers to give Claude Code access to corporate databases, Git repositories, Kubernetes clusters, and issue trackers.

However, unrestrained tool access introduces serious operational risks. A compromised or misconfigured terminal agent with raw shell execution or broad database privileges can inadvertently execute destructive SQL queries, leak credentials, or modify production infrastructure.

Bifrost functions as an enterprise MCP gateway, decoupling client tool definitions from direct backend execution. Instead of configuring external tool connections on every developer's laptop, the gateway centralizes tool registration and security enforcement:

Centralized Authentication: Upstream tools requiring OAuth tokens, database connection strings, or enterprise secrets receive credentials directly from the gateway. Developers never store production service credentials in local .env files.
Dynamic Tool Filtering: Platform teams define which MCP tools are exposed to particular virtual keys. A developer working on documentation can be granted read-only file access tools, while continuous deployment agents are granted access to build runners.
Execution Sandboxing and Auditing: Every tool invocation initiated by Claude Code passes through the gateway's validation layer. Parameters are inspected, logged in immutable audit logs, and evaluated against safety policies before execution occurs.

Consolidating tool connections behind an open source MCP gateway prevents tool sprawl, limits the attack surface of local agent environments, and gives platform administrators full oversight over external agent operations.

Extending Gateway Governance to Developer Endpoints

A persistent vulnerability in AI engineering infrastructure is "shadow AI": developers bypassing corporate proxies by installing alternative CLI utilities, utilizing personal API keys, or using browser-based chat surfaces that circumvent gateway controls.

Beyond central routing, Bifrost enforces governance and security controls (virtual keys, budgets, guardrails, and audit logs) centrally, and Bifrost Edge extends that same governance and security to AI traffic on employee machines, with endpoint enforcement on each device.

Operating in tandem with the central Bifrost AI gateway, Bifrost Edge (currently in alpha) is an endpoint agent running natively in the menu bar or system tray across macOS, Windows, and Linux. Deployed fleet-wide via MDM solutions like Jamf or Microsoft Intune, it monitors AI traffic transparently without requiring developers to manually reconfigure environment variables or base URLs across their CLI agents.

When a developer runs Claude Code, Cursor, or browser-based AI tools, Bifrost Edge ensures the connection routes through the enterprise control plane according to defined supported applications. If a developer attempts to use an unapproved model or bypass corporate compliance guardrails, the endpoint agent intercepts the call locally, blocking sensitive data before it egresses the perimeter.

Comparing Open Source Claude Code Gateway Solutions

Engineering teams evaluating the open source ecosystem for Claude Code gateways will encounter several specialized tools. While all facilitate varying degrees of traffic redirection, they differ markedly in architectural performance, enterprise governance features, and deployment models.

The table below compares leading open source and local proxy solutions suitable for Claude Code:

Gateway Platform	Primary Language	Routing Overhead	Claude Code Compatibility	Key Strengths	Primary Trade-offs
Bifrost	Go	11 microseconds	Native drop-in (`/anthropic` endpoint)	Enterprise virtual keys, MCP gateway, sub-millisecond latency, Edge endpoint integration	Full feature set requires running central server architecture
LiteLLM	Python	15 - 35 milliseconds	Native translation layer	Broad community adoption, extensive catalog of model integrations	Python runtime introduces higher CPU and latency overhead under heavy concurrency
Claude Code Router (CCR)	TypeScript / Rust	1 - 5 milliseconds	Dedicated client proxy	Tailored specifically for local agent workflows and desktop UIs	Lacks enterprise cluster scaling, RBAC, and centralized team governance
NVIDIA Switchyard	Rust	< 2 milliseconds	Protocol translation proxy	High performance, deep integration with local NVIDIA NIM and vLLM	Specialized around NeMo ecosystem; smaller general-purpose community

While lightweight personal utilities like Claude Code Router or localized scripts excel for personal developer experimentation, high-concurrency enterprise engineering teams require robust throughput, minimal latency overhead, and centralized policy enforcement. Bifrost uniquely combines the raw speed of a compiled Go runtime with enterprise-grade access management, virtual key budgeting, and fleet-wide endpoint governance.

Frequently Asked Questions

What is the advantage of using an open source Claude Code gateway over Anthropic's hosted API directly?

An open source gateway gives organizations total control over data sovereignty, cost visibility, and operational resilience. Rather than locking into a single provider with shared rate limits and opaque billing, teams can enforce per-user budgets, route across backup providers like AWS Bedrock and Vertex AI, and keep sensitive source code inside private infrastructure.

Does routing Claude Code through a gateway introduce noticeable latency?

Latency impact depends on the gateway implementation and network proximity. High-performance gateways written in compiled languages like Bifrost introduce as little as 11 microseconds of processing overhead, which is imperceptible during agentic coding loops. Running a gateway locally or in the same cloud region as your inference endpoints avoids adding round-trip network delays.

Can I run Claude Code with completely local models using an open source gateway?

Yes. By deploying an open source gateway that speaks the Anthropic Messages API, you can translate incoming payloads to OpenAI-compatible endpoints served by local runtimes such as Ollama, llama.cpp, or vLLM. You can configure model overrides so that everyday coding operations run entirely on local GPUs without incurring cloud API costs.

How does an open source gateway manage Claude Code's streaming and tool calling?

Claude Code relies heavily on Server-Sent Events (SSE) streaming and precise JSON structures for tool invocation. A production-ready gateway maintains persistent streaming HTTP connections, transparently piping chunked responses while intercepting tool call definitions to log parameters, check safety guardrails, or inject enterprise authentication tokens.

How do virtual keys differ from regular provider API keys in Claude Code?

Virtual keys are internal authentication tokens created and managed by the gateway administrator. The developer configures Claude Code using this virtual key, while the gateway holds the real upstream cloud credentials. This allows platform teams to set spending caps, enforce rate limits, and revoke access instantly without rotating root production API secrets.

Can an open source Claude Code gateway help prevent accidental secret leaks?

Yes. Gateways equipped with security guardrails and regex filters inspect prompt payloads and code diffs before they leave the gateway boundary. If a developer accidentally prompts Claude Code with a file containing an AWS secret key or private certificate, the gateway can redact the credential or block the request automatically.

Next Steps for Enterprise Claude Code Infrastructure

Adopting Claude Code across an engineering organization unlocks significant productivity gains, but scaling terminal-based AI agents requires deliberate infrastructure planning. Without centralized routing, budget enforcement, and tool governance, platform leaders face uncontrollable token costs and fragmented security postures.

Deploying an open source gateway bridges the gap between developer velocity and enterprise control. Teams evaluating infrastructure solutions can review the open-source Bifrost repository, examine architectural patterns in the Bifrost documentation, or request a Bifrost demo to assess enterprise deployment options.

Sources

Anthropic Claude Code Official Documentation - Architectural guide and environment configuration for the Claude Code CLI.
Model Context Protocol Specification - Open standard defining external tool discovery and execution for autonomous agents.
Bifrost AI Gateway Documentation - Technical specifications, benchmarking data, and CLI agent integration runbooks.
GitHub - maximhq/bifrost - Open source repository and codebase for the Bifrost AI gateway.

Best Enterprise MCP Gateway for Authenticating Claude in 2026

Kwame Asante — Wed, 16 Sep 2026 13:17:08 +0000

TL;DR

Connecting Claude to enterprise tools requires a dedicated gateway to broker authentication, enforce access control, and isolate credentials.
Bifrost is the top-ranked open-source AI gateway for authenticating Claude to Model Context Protocol (MCP) servers, adding only 11 microseconds of internal routing overhead.
Direct Claude connections to backend services expose long-lived credentials, whereas an enterprise gateway translates incoming client tokens into scoped, per-user permissions via OAuth 2.0 and token exchange.
Centralized tool filtering and virtual keys prevent models from executing unauthorized actions across internal databases, ticketing systems, and cloud environments.
Endpoint governance through Bifrost Edge ensures local instances of Claude Desktop and Claude Code adhere to the same gateway-enforced policies as production agent runtimes.

Connecting AI agents to production databases, internal APIs, and software-as-a-service platforms introduces significant identity and credential exposure risks across corporate networks. Claude clients (such as Claude Desktop, Claude Code, and web interfaces) increasingly interact with enterprise infrastructure using the Model Context Protocol (MCP). Bifrost, a high-performance open-source AI gateway developed by Maxim AI, provides the identity brokering, tool filtering, and credential isolation required to govern these connections safely. This guide examines the leading enterprise MCP gateway options for authenticating Claude, the architectural trade-offs involved, and how to select the right platform for production workloads.

Why Direct Claude-to-MCP Connections Fail in Enterprise Environments

When engineering teams initially adopt MCP, developers frequently connect Claude directly to MCP servers running locally or hosted inside private clouds. In a standard configuration, Claude acts as an MCP client, communicating over JSON-RPC across either STDIO (standard input/output subprocesses) or remote transports like Server-Sent Events (SSE) and HTTP streaming. This point-to-point pattern introduces severe security and operational challenges at scale.

Direct Connection Pattern (Vulnerable):
[Claude Client] ---> (Hardcoded API Keys / Shared Credentials) ---> [Internal MCP Server] ---> [Production DB]
No user identity context; shared service accounts; broad blast radius; no tool-level filtering.

Enterprise Gateway Pattern (Governed):
[Claude Client] ---> [Enterprise MCP Gateway] ---> [OAuth 2.0 / Token Exchange] ---> [Internal MCP Server]
Virtual key / SSO auth; per-user tokens; fine-grained tool groups; audit logs; <100µs latency.

The first structural vulnerability is credential distribution. If Claude connects directly to remote MCP servers, every developer workstation or server environment hosting Claude must hold valid credentials for every downstream service. This results in long-lived API tokens stored in plaintext configuration files across developer laptops. If an employee departs or a laptop is misplaced, rotating those credentials requires updating configs on every host.

The second problem centers on the delegation dilemma. When Claude executes a query against a PostgreSQL database or creates a customer record in Salesforce, does the downstream service know which human initiated the prompt? Under direct connections, Claude typically operates using a shared service account. Every action appears in audit logs under the identity of that shared token, stripping operations of individual accountability and violating compliance requirements like SOC 2 and HIPAA.

The third challenge is tool sprawl and prompt bloat. When Claude initializes an MCP connection, it sends a tools/list request. Without an intermediary, the MCP server returns every tool registered in its catalog. Exposing 50 complex tools injects thousands of tokens of schema definitions into Claude's context window on every turn, driving up latency and token costs while increasing the likelihood of tool selection errors or prompt injection exploits. An enterprise MCP gateway sits between Claude and backend servers, acting as a security enforcement point that validates identity, manages token exchange, filters tool schemas, and logs every invocation.

Key Criteria for Evaluating an Enterprise MCP Gateway

Selecting an enterprise gateway for MCP requires assessing capabilities beyond standard REST API gateways. The gateway must understand the bidirectional state and capability negotiation unique to the Model Context Protocol.

1. Robust Authentication and Delegation Standards

The gateway must support modern authentication specifications, including OAuth 2.1 authorization code grants with Proof Key for Code Exchange (PKCE) for public clients. For enterprise single sign-on (SSO), it should integrate with OpenID Connect (OIDC) identity providers like Okta, Microsoft Entra ID, and Keycloak. Crucially, the platform must support RFC 8693 Token Exchange, allowing the gateway to take an incoming user token from Claude and exchange it for a downstream token scoped strictly to the requested MCP server.

2. Dual Architecture (MCP Client and MCP Server)

An optimal MCP gateway functions as both an MCP client and an MCP server. As an MCP server, it presents a single, unified endpoint to Claude. Claude points to one URL and sees only the tools permitted for that specific user or session. As an MCP client, the gateway maintains the upstream connections, connection pools, and authentication lifecycles to dozens of distinct backend MCP servers.

3. Granular Tool Authorization and Dynamic Filtering

Enterprise security requires least-privilege tool access. A gateway should allow security teams to curate tool groups or virtual MCP servers. For example, a financial operations team should see tools for invoice processing, while engineering sees tools for GitHub and Kubernetes. Claude should never receive the schemas for tools the calling user is unauthorized to execute.

4. Performance and Low Internal Overhead

AI agent workflows are iterative; an agent might invoke multiple tools sequentially before returning an answer. If a gateway introduces 30 milliseconds of latency on every tool discovery and execution round-trip, agent latency deteriorates noticeably. Gateways built in compiled languages (such as Go or Rust) provide substantial throughput and latency advantages over interpreted proxies.

Evaluation Dimension	Minimal Standard	Enterprise Benchmark
Authentication Flow	Static API keys and bearer tokens	OAuth 2.1 + PKCE, OIDC SSO, RFC 8693 Token Exchange
Identity Resolution	Shared service account	Per-user credential mapping with downstream propagation
Access Control	Server-level binary access	Tool-level and function-level virtual keys with schema filtering
Overhead Latency	< 25 ms per request	< 1 ms (< 100 microseconds for elite compiled systems)
Credential Storage	Plaintext environment variables	Integrated secrets management or enterprise vault integration
Audit Trails	Basic HTTP access logging	Full JSON-RPC payload logging with PII scrubbing and redaction

The Top Enterprise MCP Gateways Compared

The market for MCP-compatible infrastructure spans dedicated AI gateways, modified API management platforms, and developer tooling. Below is a comparative overview of the leading platforms capable of brokering Claude-to-MCP connectivity.

Gateway Platform	Primary Language	Authentication Capabilities	Identity Propagation	Tool Filtering	Performance / Overhead	Deployment Model
Bifrost	Go	OAuth 2.0 (PKCE), OIDC, Token Exchange, Virtual Keys	Per-user and server-level credential mapping	Dynamic tool groups and per-key allowlists	11 µs internal overhead at 5,000 RPS	Self-hosted, VPC, air-gapped, Kubernetes
Kong AI Gateway	Lua / C (Nginx)	Kong OAuth plugins, API key auth, mTLS	Consumer-level credential mapping	Static route-level tool plugins	~1-5 ms proxy overhead	Self-hosted, managed cloud, hybrid
Docker MCP Gateway	Go	Local token auth, container secrets	Host user permissions	Catalog-level container isolation	~2-5 ms local overhead	Local daemon, container runtime
Tyk MCP Gateway	Go	OIDC, OAuth 2.0, API keys	API consumer identity	Policy-based endpoint schema filtering	~2-6 ms proxy overhead	Self-hosted, SaaS, hybrid
Speakeasy AI Control	TypeScript / Go	OAuth 2.1 sessions, IdP directory sync	Directory principal via IdP claims	Per-server and per-tool access grants	~5-15 ms proxy overhead	Managed SaaS platform

1. Bifrost

Bifrost is a high-performance, open-source AI gateway written in Go that acts as a unified control plane for both LLM routing and MCP infrastructure. In sustained benchmarks, Bifrost adds only 11 microseconds of internal routing overhead per request at 5,000 requests per second, making it the lowest-latency gateway in the industry. Bifrost solves the MCP integration problem by operating simultaneously as an MCP client and an MCP server.

Bifrost Dual MCP Architecture:
                        +----------------------------------------+
                        |            Bifrost Gateway             |
[Claude Desktop] -----> | [MCP Server Interface]                 |
[Claude Code]    -----> | - Single aggregated /mcp endpoint      |
                        | - Virtual key & OIDC authentication    |
                        | - Dynamic tool filtering per role      |
                        |                                        |
                        | [MCP Client Engine]                    |
                        | - OAuth 2.0 PKCE & Token Exchange      |
                        +-------------------+--------------------+
                                            |
                   +------------------------+------------------------+
                   | (SSE / HTTP)                                    | (STDIO / HTTP)
                   v                                                 v
        [Internal Postgres MCP]                            [GitHub Enterprise MCP]

Architecture and Authentication Capabilities

Bifrost handles authentication across both sides of the MCP handshake:

Claude to Gateway (Inbound): Claude connects to Bifrost's /mcp endpoint using either standard HTTP bearer tokens, virtual keys, or interactive browser-based OAuth. Virtual keys represent teams, customers, or specific agents, allowing administrators to attach rate limits, cost budgets, and tool allowlists to that identity.
Gateway to MCP Server (Outbound): When calling downstream MCP servers, Bifrost supports multiple upstream authentication modes. These include server-level OAuth 2.0 with automated token refresh, static administrative headers, per-user OAuth (where Bifrost lazily initiates an authentication handshake when a specific human calls a tool for the first time), and RFC 8693 Token Exchange for enterprise identity providers.

For organizations using Okta, Microsoft Entra ID, or Google Workspace, Bifrost integrates directly via OIDC to sync user directories and enterprise groups. The gateway's MCP tool groups feature allows administrators to package tools into discrete virtual MCP servers. An AI model querying Bifrost receives only the schemas relevant to its active role. Furthermore, Bifrost features an innovative Code Mode execution pipeline, where Claude can write concise Python orchestration scripts to chain multiple MCP tools inside an isolated environment, achieving a 50% token reduction and 40% faster execution compared to classic conversational tool turn-taking.

Beyond routing, Bifrost applies governance and security controls (virtual keys, budgets, guardrails, audit logs) centrally, and Bifrost Edge extends that same governance and security to AI traffic on employee machines, with endpoint enforcement on each device.

Best for: Enterprises requiring ultra-low latency, comprehensive credential isolation, and unified control over both model routing and tool execution in self-hosted, VPC, or regulated environments.

2. Kong AI Gateway

Kong AI Gateway extends Kong's established API management platform into generative AI workloads. Leveraging an Nginx and Lua runtime, Kong sits as a reverse proxy capable of intercepting traffic between clients and backend services.

Kong addresses MCP by treating MCP endpoints as HTTP routes governed by plugins. Teams familiar with Kong can deploy its standard OAuth 2.0, OpenID Connect, and Key Authentication plugins to restrict access to MCP servers. Kong excels at high-concurrency API proxying and integrates smoothly into existing enterprise CI/CD pipelines through Kong Konnect or declarative decK configurations.

However, Kong was originally designed for stateless REST requests. While it handles HTTP-based MCP transports effectively, it lacks native awareness of the MCP JSON-RPC protocol lifecycle. Tool filtering must often be handled through custom Lua plugins or external routing rules, and it does not natively provide per-user token exchange tailored to Claude's tool invocation format.

Best for: Organizations with extensive existing Kong deployments seeking to manage MCP traffic using their established API gateway infrastructure.

3. Docker MCP Gateway

Docker has introduced container-native infrastructure designed to standardize how developers run and connect to MCP servers. Docker's approach isolates each MCP server inside a localized container, mediating access through a local daemon or Docker desktop environment.

Authentication in the Docker ecosystem focuses on container isolation and host-level credentials. Docker allows developers to configure secret injection at runtime, ensuring that backend MCP servers receive the API tokens they need without exposing them directly to the host filesystem. It provides a simple mechanism for launching popular community MCP tools (like SQLite, GitHub, and local filesystem tools) with minimal setup.

While Docker provides strong isolation on local developer machines, it is not an enterprise-wide network gateway. It does not natively handle cross-organization SSO directory sync, centralized budget controls, or dynamic OAuth token exchange for remote Claude clients.

Best for: Individual developers and engineering teams seeking containerized, local sandboxing for community MCP tools during development.

4. Tyk MCP Gateway

Tyk is an open-source enterprise API gateway written in Go that has added specialized governance for Model Context Protocol traffic. Tyk positions its MCP gateway as an integral component of an enterprise API management foundation, bridging agent-to-tool communications with traditional API governance.

Tyk allows platform teams to expose internal APIs as governed MCP servers, applying authentication, mutual TLS (mTLS), and rate limiting to the resulting tool calls. It integrates with enterprise identity providers via OIDC and SAML, validating user tokens before allowing Claude to access the underlying tool definitions. Tyk also provides detailed API analytics and developer portal capabilities, making it straightforward to publish a directory of approved tools for internal development teams.

The trade-off with Tyk involves configuration complexity and overhead. Setting up full API management policies across complex MCP schemas requires configuring substantial middleware pipelines, which typically adds between 2 and 6 milliseconds of processing overhead per request.

Best for: Enterprises wanting to expose existing REST and GraphQL enterprise services as MCP tools while maintaining standard API management governance.

5. Speakeasy AI Control Plane

Speakeasy offers an AI control plane specifically designed to bridge enterprise identity with AI agents and coding tools. Rather than functioning purely as a model proxy, Speakeasy focuses heavily on the identity and governance layer surrounding tool calls.

Speakeasy integrates directly with enterprise identity providers to capture directory principals, evaluating user groups and security attributes in real time whenever an agent initiates an action. It manages OAuth 2.1 sessions and provides native tool-level grants, ensuring that developers using tools like Claude Code or Cursor only access the specific operations approved by security administrators. It also incorporates inline scanners to detect sensitive data leaks and prompt injection attempts before tools execute.

Unlike Bifrost, Speakeasy does not provide a high-performance, multi-provider LLM routing engine with semantic caching and failovers. Teams using Speakeasy typically need to pair it with a separate model gateway, creating two distinct control planes for model traffic and tool traffic.

Best for: Security teams focused specifically on identity synchronization and tool call scanning across developer coding agents.

Technical Deep-Dive: Implementing Claude Authentication with Bifrost

To illustrate how an enterprise gateway eliminates shared credentials, consider an implementation where Claude Desktop and Claude Code connect to an internal customer support database via Bifrost.

Step 1: Defining the MCP Client Connection in Bifrost

First, an administrator registers the internal MCP server in Bifrost using the JSON configuration or administrative interface. The gateway handles OAuth 2.0 with PKCE and automatic token refresh, ensuring the upstream database credentials never reach developer workstations.

{
  "mcp_servers": {
    "support_database": {
      "connection_type": "http",
      "url": "https://mcp-db.internal.corp/mcp",
      "auth_type": "oauth",
      "oauth_config": {
        "client_id": "bifrost-mcp-gateway",
        "authorize_url": "https://auth.corp.com/oauth/authorize",
        "token_url": "https://auth.corp.com/oauth/token",
        "scopes": ["support:read", "tickets:update"]
      }
    }
  }
}

Step 2: Creating Role-Scoped Virtual Keys

Next, the platform administrator generates a virtual key assigned to the support engineering team. This virtual key enforces request rate limits, assigns monthly token budgets, and restricts tool exposure to an explicit allowlist:

{
  "virtual_key": "sk-bf-support-tier2-user89",
  "name": "Customer Support Tier 2",
  "budget": {
    "max_monthly_spend_usd": 150.00
  },
  "rate_limits": {
    "requests_per_minute": 60
  },
  "mcp_governance": {
    "allowed_tool_groups": ["support_read_tools"],
    "blocked_tools": ["delete_customer_record", "drop_table"]
  }
}

Step 3: Configuring Claude to Use the Gateway Endpoint

On the client side, Claude Desktop or Claude Code is configured to point its MCP transport to Bifrost's /mcp endpoint. The client supplies the assigned virtual key in the connection header:

{
  "mcpServers": {
    "enterprise_gateway": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-sse",
        "https://bifrost.internal.corp/mcp"
      ],
      "headers": {
        "x-bf-vk": "sk-bf-support-tier2-user89"
      }
    }
  }
}

When Claude connects, Bifrost intercepts the initial tools/list handshake. It consults the policy attached to sk-bf-support-tier2-user89, redacts any tools related to database deletion, and presents Claude with a clean, scoped list of authorized tools. When Claude issues a tools/call request, Bifrost validates the request against the virtual key's rate limits and budgets, retrieves a fresh bearer token via OAuth, forwards the call to the internal database server, and streams the verified response back to Claude.

Endpoint AI Governance: Securing Developer Laptops with Bifrost Edge

While server-hosted gateways govern cloud deployments, enterprise developers routinely run Claude Desktop, Cursor, and terminal coding agents directly on their workstations. In practice, many developers bypass server gateways entirely, installing unapproved local MCP servers and hardcoding personal API keys. This ungoverned behavior represents shadow AI.

Fleet-Wide Visibility with Bifrost Edge:
[Developer Laptop] 
  ├── Claude Desktop  \
  ├── Cursor Editor    ---> [Bifrost Edge Daemon] ---> [Bifrost AI Gateway] ---> [Secure Tools]
  └── Claude Code CLI /            |
                            Enforces MDM Policies,
                            Discovers Local MCP Servers,
                            Redacts PII & API Keys.

To eliminate this vulnerability, the Bifrost platform pairs the central gateway with Bifrost Edge. Bifrost Edge is an endpoint AI governance agent (currently in alpha) that runs natively in the background on macOS, Windows, and Linux. Deployed across corporate fleets using standard mobile device management (MDM) tools like Jamf, Microsoft Intune, and Kandji, Edge routes all local AI traffic through the organization's Bifrost deployment automatically.

Bifrost Edge introduces critical endpoint security capabilities:

Zero-Configuration Routing: Developers do not need to manually edit config files or configure proxy URLs; Edge transparently captures AI traffic from Claude Desktop, Claude Code, and IDE plugins.
MCP Server Discovery: Edge inventories every MCP server configured across all developer machines in the organization, populating a centralized admin devices dashboard with exact version and transport data.
Enforced Tool Approvals: Security teams review discovered tools in the admin approvals dashboard. If an administrator marks an MCP server as denied, Edge actively blocks execution on the device, neutralizing vulnerable or unauthorized tools before they execute locally.
Endpoint Guardrail Enforcement: Through Bifrost's native security guardrails, prompts and tool responses passing through local Claude instances are scanned for plaintext API keys, credentials, and sensitive customer data before leaving the machine.

By combining the Bifrost control plane with Bifrost Edge, enterprises bridge the gap between cloud infrastructure and local developer environments, ensuring consistent authentication, compliance, and cost control everywhere Claude is used.

Architectural Comparison: Performance, Latency, and Scalability

In complex agentic architectures, latency compounding is a critical consideration. When Claude works through a multi-step task, it frequently invokes 5 to 15 tool calls in sequence. An enterprise gateway that introduces noticeable proxy latency multiplies that delay across every step.

Sequential Agent Invocation Pipeline (10 Tool Calls):
Gateway A (10 ms overhead): 10 x 10 ms = 100 ms pure infrastructure tax
Bifrost   (11 µs overhead): 10 x 11 µs = 0.11 ms pure infrastructure tax (virtually zero)

The underlying runtime architecture determines this latency:

Memory and Threading Models: Gateways written in compiled languages with efficient garbage collection (such as Go) manage thousands of concurrent SSE and HTTP streams with minimal memory footprints. Interpreted proxies running on Node.js or Python often struggle with event-loop contention under sustained JSON-RPC traffic.
Connection Pooling to Backend MCP Servers: When Claude requests multiple tool executions, Bifrost maintains persistent HTTP/2 and SSE connection pools to upstream MCP servers, eliminating repetitive TCP and TLS handshakes.
Schema Caching: Schemas for complex MCP tools can span thousands of lines of JSON. Bifrost caches parsed tool definitions in memory, assembling filtered schema responses for Claude in microseconds without re-parsing definitions on every request.

For enterprise teams deploying clusters across Kubernetes or private VPCs, Bifrost supports clustering with distributed state synchronization, ensuring that virtual key budget tracking and rate limits remain accurate across multi-region deployments.

Frequently Asked Questions

What is an enterprise MCP gateway?

An enterprise MCP gateway is an infrastructure proxy that sits between AI clients (such as Claude) and backend MCP servers. It centralizes authentication, isolates credentials, enforces role-based access control, filters tool definitions, and logs every tool invocation for security and compliance.

How does Claude authenticate to MCP servers through an enterprise gateway?

Claude authenticates to the gateway using an API token, virtual key, or interactive SSO session. The gateway verifies the client's identity, matches it against internal permissions, and uses its own secure credential store or OAuth 2.0 token exchange to connect to backend MCP servers on the user's behalf.

Why is direct authentication between Claude and internal MCP servers risky?

Direct connections require distributing sensitive database and API credentials to client applications or developer workstations. This eliminates centralized audit trails, creates credential rotation nightmares, and allows AI models to access all tools exposed by the server rather than a restricted subset.

What is the difference between server-level and per-user MCP authentication?

Server-level authentication uses a single, shared credential configured by an administrator to access an upstream tool for all requests. Per-user authentication ensures each user authenticates independently (via OAuth or user-specific tokens), allowing the downstream tool to enforce individual user permissions and record accurate audit logs.

Can an enterprise MCP gateway reduce token costs for Claude?

Yes. Gateways with tool filtering prevent unnecessary tool schemas from being injected into Claude's prompt context. Additionally, advanced execution modes like Bifrost's Code Mode allow Claude to orchestrate multiple tools in a single Python execution script, cutting token consumption by up to 50%.

Does an MCP gateway replace an AI gateway for LLM routing?

While some tools only handle MCP traffic, unified platforms like Bifrost combine both capabilities into a single control plane. This allows organizations to manage model access, semantic caching, provider failover, and MCP tool governance through one policy engine and single virtual key abstraction.

Recommendation and Next Steps

Securing Claude's access to enterprise tools requires moving away from fragmented, direct connections toward a governed gateway architecture. For organizations evaluating MCP infrastructure, Bifrost represents the most comprehensive choice. Its combination of 11-microsecond internal routing overhead, native dual MCP client/server architecture, flexible per-user OAuth handling, and fleet-wide endpoint governance via Bifrost Edge delivers the security enterprise platforms require without compromising execution speed.

Teams planning production deployments can explore the Bifrost open-source repository or schedule a Bifrost demo to review enterprise clustering, vault integrations, and custom governance profiles.

Sources

7 Best AI Gateways with OpenTelemetry Support

Kwame Asante — Thu, 23 Jul 2026 21:21:28 +0000

An analysis of the top AI gateways with native OpenTelemetry support for observability. This post compares the leading options for teams that need to integrate LLM infrastructure into their existing monitoring stacks. Bifrost emerges as a strong choice for enterprises requiring robust, low-latency observability.

As engineering teams deploy AI applications into production, observability becomes a critical, non-negotiable requirement. Without detailed telemetry, debugging performance issues, tracking costs, and ensuring reliability is nearly impossible. OpenTelemetry has become the industry standard for cloud-native observability, and integrating AI infrastructure with it is essential. An AI gateway is the natural control point for this integration, centralizing request traces, metrics, and logs.

This post evaluates seven of the best AI gateways that provide native support for OpenTelemetry, enabling teams to pipe detailed observability data into their existing platforms like Datadog, Honeycomb, or Grafana. The options are assessed based on their performance, feature set, and enterprise-readiness.

Key Criteria for Evaluating Gateways

When choosing an AI gateway with OpenTelemetry support, teams should consider several key factors:

Performance Overhead: How much latency does the gateway add to each request? This should be minimal to avoid impacting user experience.
OTLP Integration Depth: Does the gateway support traces, metrics, and logs? How configurable is the data export?
Provider Support: How many LLM providers does the gateway support out of the box?
Enterprise Features: Does it offer features like high-availability clustering, role-based access control (RBAC), and advanced security guardrails?
Ease of Deployment: Can the gateway be deployed easily in various environments, including Kubernetes, on-premise, and in a VPC?

The Top 7 AI Gateways for OpenTelemetry

1. Bifrost

Bifrost is a high-performance, open-source AI gateway from Maxim AI, written in Go. It is designed for enterprise-grade performance, reporting only 11 microseconds of overhead per request at 5,000 RPS in published benchmarks.

Bifrost’s observability features are comprehensive, with native support for exporting detailed telemetry via the OpenTelemetry Protocol (OTLP). It allows teams to send traces, metrics, and logs to any OTLP-compatible backend. This makes it a strong fit for organizations that have standardized on OpenTelemetry for their observability stack.

Best for: Enterprises and teams running mission-critical AI workloads that require best-in-class performance, comprehensive observability, and robust governance. Bifrost's low latency and deep OTLP integration make it the top choice for production systems.

Key Features:

Native OTLP Export: Provides first-class support for OpenTelemetry, exporting rich, contextualized trace data for every request.
Prometheus Metrics: In addition to OTLP, it exposes a /metrics endpoint for Prometheus scraping.
High Performance: Adds negligible latency, making it suitable for real-time applications.
Unified API: Offers a single, OpenAI-compatible API for over 20 LLM providers.
Advanced Governance: The Bifrost AI gateway provides granular control through virtual keys, budgets, and rate limits. Beyond the gateway, Bifrost Edge extends that same governance and security to AI traffic on employee machines.
Enterprise-Ready: Offers high-availability clustering, audit logs, and security guardrails.

2. LiteLLM

LiteLLM is a popular open-source library that provides a unified interface to call over 100 LLM APIs. It can be deployed as a proxy server, functioning as a lightweight AI gateway. Its observability features include a callback mechanism that can be used to export data to various backends, including OTLP-compatible systems.

While flexible, setting up robust OpenTelemetry integration often requires more custom configuration compared to gateways with native OTLP exporters. Teams may need to write custom callback handlers to format and send the data correctly. For teams seeking a different approach, Bifrost offers a page on LiteLLM alternatives.

Best for: Development teams and smaller projects that need a simple way to manage multiple LLM providers and are willing to configure observability integrations manually.

Key Features:

Broad Provider Support: Extensive support for a wide range of LLM providers.
Flexible Callbacks: A customizable callback system allows for integration with many observability platforms.
Cost Tracking: Provides utilities for tracking token usage and estimating costs.

3. Kong AI Gateway

The Kong AI Gateway is an extension of the widely-used Kong API Gateway, built to handle AI and LLM traffic specifically. Leveraging Kong's robust plugin architecture, it offers features like prompt engineering, caching, and rate-limiting. Its observability is handled through Kong's existing telemetry plugins, which include support for OpenTelemetry.

This makes it a natural choice for organizations already invested in the Kong ecosystem. However, it may be a heavier solution for teams that do not need a full-featured API management platform.

Best for: Organizations already using Kong Gateway for API management who want to extend its capabilities to their AI workloads.

Key Features:

Plugin Architecture: Extensible with plugins for AI-specific tasks and observability.
Multi-LLM Support: Can route requests to various LLM providers.
Enterprise-Grade: Built on a proven, scalable API gateway platform.

4. Cloudflare AI Gateway

Cloudflare AI Gateway is a managed service that provides caching, rate limiting, and analytics for AI applications. As part of the Cloudflare ecosystem, it benefits from the company's global network, offering low-latency access for users worldwide. It provides logs and analytics through its dashboard, with integrations for exporting data to other platforms.

While it offers visibility, its OpenTelemetry support is less direct than dedicated gateways, often relying on log forwarding and custom processing to generate traces and metrics.

Best for: Teams already using the Cloudflare stack who want a simple, managed solution for caching and basic analytics on their AI traffic.

Key Features:

Global Network: Low-latency performance due to Cloudflare's edge network.
Managed Service: Fully managed, reducing operational overhead.
Caching and Analytics: Provides insights into request patterns and caches responses to reduce costs.

5. OpenRouter

OpenRouter is a hosted service that aggregates a wide variety of open-source and proprietary models, making them accessible through a unified, OpenAI-compatible API. It simplifies model discovery and routing, allowing users to direct requests to the best model for a given task, often based on cost or performance.

For observability, OpenRouter provides a detailed request log through its user interface. Programmatic access for integration with systems like OpenTelemetry is available but may require using the OpenRouter API to pull data rather than having it pushed via OTLP.

Best for: Developers and researchers who want easy access to a vast range of models for experimentation and are comfortable with a managed, hosted solution.

Key Features:

Extensive Model Catalog: Access to hundreds of models from a single endpoint.
Smart Routing: Finds the most cost-effective model for a given prompt.
Usage Dashboard: Clear visibility into costs and request history.

6. NVIDIA NIM

NVIDIA NIM (NVIDIA Inference Microservices) is a set of optimized, pre-built containers designed to simplify the deployment of AI models. While not a gateway in the traditional multi-provider sense, NIM acts as a standardized entry point for serving models from NVIDIA's catalog or custom models. It integrates with the broader cloud-native ecosystem, including support for Prometheus and OpenTelemetry for monitoring GPU utilization and inference performance.

NIM is focused on the serving layer for self-hosted models, making it a powerful component of an AI stack that would typically sit behind a routing gateway like Bifrost.

Best for: Teams deploying and managing their own AI models on NVIDIA hardware who need standardized, high-performance inference endpoints with built-in observability.

Key Features:

Optimized Performance: Pre-configured for high-throughput, low-latency inference on NVIDIA GPUs.
Cloud-Native Integration: Includes support for Prometheus metrics and OpenTelemetry.
Standardized API: Provides a consistent API for interacting with various models.

7. Azure AI Gateway

Microsoft's Azure platform offers AI gateway capabilities through services like Azure API Management and Azure AI Services. By combining these, teams can create a centralized entry point for managing access to Azure OpenAI, as well as other models. Azure Monitor provides extensive observability features, including integration with OpenTelemetry through its exporters.

This approach is powerful for teams deeply embedded in the Azure ecosystem but can involve configuring and connecting multiple services, increasing complexity compared to a single, dedicated gateway solution.

Best for: Enterprises heavily invested in the Microsoft Azure cloud that want to manage AI traffic using native Azure services and tooling.

Key Features:

Deep Azure Integration: Seamlessly connects with other Azure services like Entra ID for authentication and Azure Monitor for observability.
Managed Infrastructure: Leverages Azure's scalable and reliable infrastructure.
Comprehensive Tooling: Access to the full suite of Azure's AI and cloud management tools.

How the Options Compare on Observability

Gateway	Native OTLP Export	Primary Focus	Best For
Bifrost	Yes (Traces, Metrics, Logs)	Performance & Governance	Enterprise Production
LiteLLM	Via Callbacks	Unified LLM API	Development & Small Projects
Kong AI Gateway	Yes (Via Plugin)	API Management	Existing Kong Users
Cloudflare AI Gateway	Via Log Forwarding	Caching & Performance	Existing Cloudflare Users
OpenRouter	Via API Polling	Model Aggregation	Experimentation
NVIDIA NIM	Yes	Self-Hosted Inference	NVIDIA Hardware Users
Azure AI Gateway	Yes (Via Azure Monitor)	Azure Ecosystem	Azure-Centric Enterprises

Recommendation

Choosing the right AI gateway depends on an organization's specific needs, existing infrastructure, and scalability requirements.

For enterprises and high-growth startups where performance, reliability, and deep observability are paramount, Bifrost stands out. Its minimal overhead, native and comprehensive OpenTelemetry support, and robust enterprise features make it the most suitable choice for demanding production environments. Its open-source nature also provides transparency and flexibility.

For smaller teams or those prioritizing simplicity and broad model access over deep integration, options like LiteLLM and OpenRouter offer a quick way to get started. For companies already committed to a specific cloud or API management platform, the native solutions from Azure, Cloudflare, and Kong provide a more integrated, albeit potentially less specialized, experience.

Ultimately, integrating AI traffic into a standard observability framework like OpenTelemetry is no longer optional. The gateways listed here provide a path to achieving that, with different trade-offs in performance, features, and complexity. Teams evaluating these options can request a Bifrost demo or review the open-source repository to explore its capabilities further.

Sources

How to Unify AI Agent and SDK Traffic with a Single Gateway

Kwame Asante — Tue, 14 Jul 2026 14:38:59 +0000

Fragmented AI tool usage can lead to governance challenges and inconsistent developer workflows. Centralizing tools like Claude Code, Cursor, and the OpenAI SDK through an AI gateway simplifies management, enhances security, and optimizes performance.

As AI integration expands across organizations, developers often find themselves using a mix of tools: a coding agent like Claude Code for terminal-based tasks, Cursor as an AI-first IDE, and the OpenAI Python SDK for custom application development. While each tool is powerful in its own right, managing traffic, ensuring compliance, and optimizing costs across these disparate interfaces presents a significant challenge. A dedicated AI gateway provides a centralized solution, allowing teams to route all AI-bound traffic through a single control plane.

The Challenge of Managing Disparate AI Tools

The proliferation of AI agents and SDKs introduces complexities for engineering and security teams. Each tool might default to its own provider endpoint, making it difficult to gain a holistic view of AI usage. This fragmentation can lead to:

Shadow AI: Uncontrolled usage of AI tools outside of organizational policy, posing security and compliance risks.
Inconsistent Governance: Applying rate limits, budgets, or access controls uniformly across different tools becomes nearly impossible.
Suboptimal Costs: Without a central routing layer, opportunities for cost optimization through caching, load balancing, or intelligent provider selection are missed.
Lack of Visibility: Monitoring prompts, responses, and errors across various integration points hinders debugging and performance analysis.

Centralizing this traffic through an AI gateway like Bifrost, an open-source AI gateway from Maxim AI, addresses these issues by providing a unified point of control.

Centralizing AI Traffic with a Gateway

An AI gateway acts as an intermediary server that intercepts and routes requests from various AI clients to their respective LLM providers. This enables a single point for applying policies, observability, and performance optimizations. The benefits include:

Unified API: Many gateways offer an OpenAI-compatible API, allowing diverse tools to speak a common language regardless of the backend model.
Policy Enforcement: Implement virtual keys, budget limits, and rate limits globally.
Enhanced Security: Apply guardrails for data loss prevention (DLP) and prompt injection detection before requests reach external models.
Improved Reliability: Configure automatic failover and load balancing across multiple providers to ensure continuous service availability.
Cost Optimization: Implement semantic caching to reduce redundant requests and minimize token usage.

Pointing the OpenAI SDK to an AI Gateway

The OpenAI SDK is designed for flexibility, allowing developers to easily redirect its traffic to a custom base URL. This makes it a prime candidate for integration with an AI gateway.

For Python, the openai library (which uses httpx internally) allows overriding the base URL when initializing the client.

from openai import OpenAI

# Configure the OpenAI client to point to your Bifrost gateway
client = OpenAI(
    api_key="your_bifrost_api_key", # This is your Bifrost Virtual Key
    base_url="https://your-bifrost-gateway.com/v1" # Your gateway's OpenAI-compatible endpoint
)

# Example usage (standard OpenAI SDK calls)
chat_completion = client.chat.completions.create(
    model="gpt-4o", # Or any model supported by your gateway
    messages=[{"role": "user", "content": "Explain AI gateways."}]
)
print(chat_completion.choices[0].message.content)

Alternatively, the OPENAI_BASE_URL environment variable can be set, which the SDK will respect.

export OPENAI_API_KEY="your_bifrost_api_key"
export OPENAI_BASE_URL="https://your-bifrost-gateway.com/v1"

This "drop-in replacement" capability means existing applications using the OpenAI SDK can be re-routed through Bifrost with minimal code changes, primarily by adjusting the base_url and api_key parameters to match the gateway's configuration.

Configuring Claude Code for Gateway Control

Claude Code, a terminal-based AI coding agent, typically routes its requests to Anthropic's native /v1/messages endpoint. To direct Claude Code's traffic through an AI gateway, proxy environment variables are commonly used. Claude Code respects standard HTTP_PROXY and HTTPS_PROXY environment variables.

# For a proxy requiring basic authentication:
export HTTPS_PROXY="http://username:password@your-bifrost-gateway.com:443"

# For a proxy without authentication:
export HTTPS_PROXY="https://your-bifrost-gateway.com"

# Then launch Claude Code from the same terminal session
claude

For persistent configuration, these variables can be set in a shell profile (e.g., .bashrc, .zshrc) or scoped to a project's settings.json file. Some gateways can also be configured using the ANTHROPIC_BASE_URL environment variable.

By routing Claude Code traffic through a gateway, organizations gain visibility into every prompt and response, allowing for security measures such as blocking sensitive data or enforcing usage policies on developer AI activity.

Integrating Cursor with an AI Gateway

Cursor, an AI-first IDE, requires careful configuration to ensure all its AI interactions pass through a corporate gateway. It leverages an Electron-based networking layer, which can sometimes bypass system-wide proxy settings.

There are generally two effective methods for configuring Cursor:

Application Settings: Access Cursor's settings (Cmd+, on macOS or Ctrl+, on Windows/Linux), search for "Proxy," and enter the gateway's URL and any necessary authentication details. Cursor offers an "Override OpenAI Base URL" option where the gateway's endpoint can be entered.
Environment Variables (Terminal Launch Method): This method is often more reliable for forcing proxy settings deep into Cursor's process tree. Launch Cursor from a terminal where HTTP_PROXY and HTTPS_PROXY variables are explicitly set.

# Set proxy variables
export HTTPS_PROXY="https://your-bifrost-gateway.com"
export HTTP_PROXY="http://your-bifrost-gateway.com"

# Launch Cursor from this terminal
cursor

It is important to note that Cursor's Agent Mode routes LLM calls through Cursor's own servers, which then connect to providers like Anthropic or OpenAI. In this scenario, local proxy settings control the traffic from the developer's machine to api.cursor.sh, not directly to the LLM provider. For comprehensive governance of Cursor, especially its Agent Mode and Model Context Protocol (MCP) server integrations, an AI gateway like Bifrost becomes crucial.

Beyond Connection: Advanced Gateway Capabilities

Connecting these tools to a unified AI gateway is just the first step. The true value lies in the advanced capabilities the gateway provides. Bifrost, for example, offers:

Comprehensive Governance: Manage AI usage with virtual keys, detailed budgets, and dynamic rate limits across all connected applications and users.
Security Guardrails: Implement real-time guardrails to detect and block sensitive data (PII, secrets) or malicious prompts before they leave the organization's control. This applies across all traffic, whether from an SDK or a coding agent.
MCP Gateway Functionality: As an MCP gateway, Bifrost provides advanced capabilities for AI agents, including tool discovery, execution, and filtering per virtual key, extending governance to how agents use external services.
Observability: Gain deep insights into prompt and response traffic with built-in monitoring, Prometheus metrics, and OpenTelemetry integration, crucial for debugging and optimizing AI applications.
Endpoint AI Governance with Bifrost Edge: Beyond the gateway, Bifrost Edge extends this same level of governance and security directly to employee machines. It captures AI traffic from desktop applications, browser-based AI, and coding agents, ensuring that even ungoverned "shadow AI" usage on the endpoint is routed through Bifrost for policy enforcement, audit logging, and compliance. This ensures that the controls configured at the gateway are enforced wherever AI is used.

Implementing Your Unified AI Gateway Strategy

For organizations seeking centralized control, enhanced security, and optimized performance for their diverse AI toolchain, deploying an AI gateway is a critical step. By routing tools like the OpenAI SDK, Claude Code, and Cursor through a unified gateway, teams establish a robust foundation for scalable and compliant AI operations. This approach simplifies developer workflows and provides the necessary oversight for confidently integrating AI across the enterprise.

Teams evaluating AI gateways can request a Bifrost demo or review the open-source repository.

Sources

7 Observability Tools That Integrate With Your AI Gateway

Kwame Asante — Thu, 09 Jul 2026 09:28:16 +0000

Monitoring the performance, reliability, and cost of AI applications requires robust observability. This article explores leading tools that integrate with AI gateways, highlighting how Bifrost enables comprehensive visibility and control for production AI workloads.

Building and maintaining AI applications in production presents unique observability challenges. Unlike traditional software, AI systems involve LLMs, vector databases, and often complex agentic workflows, leading to unpredictable token usage, variable latency, and novel failure modes. An AI gateway, acting as the central control plane for LLM traffic, becomes a critical point for implementing comprehensive monitoring. It provides a unified view across multiple models and providers, making it an ideal place to integrate with diverse observability tools. Bifrost, an open-source AI gateway from Maxim AI, offers native capabilities and deep integrations that simplify this task for engineering teams.

The Critical Role of Observability for AI Gateways

AI gateways manage the flow of requests to large language models (LLMs) and other AI services. This centralized position makes them indispensable for implementing robust observability. By aggregating all AI traffic, a gateway can collect crucial metrics, logs, and traces that provide insights into:

Performance: Latency, throughput, error rates across different models and providers.
Cost: Token usage, expenditure breakdown by model, user, or application.
Reliability: Failover events, provider outages, and load balancing effectiveness.
Governance: Adherence to rate limits, budget constraints, and security policies.
Application Behavior: Understanding how user requests are routed, transformed, and processed by various AI components.

Without a comprehensive observability strategy at the gateway level, teams risk operating AI applications as black boxes, making it difficult to debug issues, optimize costs, or ensure compliance.

Key Observability Criteria for AI Gateway Integration

When selecting observability tools to integrate with an AI gateway, several criteria are essential for effective monitoring of AI workloads:

Native AI/LLM Monitoring Features: Tools offering specific dashboards, metrics, or tracing for LLM calls (e.g., token counts, prompt/completion tracking).
OpenTelemetry Support: Integration with OpenTelemetry ensures vendor-neutral data collection and compatibility with a wide ecosystem of tools for metrics, logs, and traces.
Real-time Metrics and Alerting: The ability to collect and visualize metrics in real time, with configurable alerts for anomalies or threshold breaches.
Distributed Tracing: Capability to trace requests end-to-end across multiple services, including AI models and external tools.
Log Aggregation and Analysis: Centralized collection, search, and analysis of logs from the gateway and downstream AI services.
Customization and Extensibility: Flexibility to add custom metrics, dashboards, or integrations tailored to specific AI application needs.
Ease of Integration: Seamless setup with existing AI gateway deployments and other infrastructure components.

Top Observability Tools for AI Gateways

AI gateways often serve as a bridge between your application and diverse AI models, making them a central point for collecting observability data. The following tools offer robust capabilities and integration points essential for monitoring AI workloads.

1. Bifrost's Native Observability and Integrations

Bifrost is designed with observability at its core, offering native features and deep integrations that position it as a top choice for AI gateway observability. It provides real-time request monitoring directly from its dashboard, giving immediate insights into traffic flow. Bifrost also supports native Prometheus metrics, allowing teams to scrape gateway-level data such as latency, throughput, error rates, and token usage. For comprehensive distributed tracing, Bifrost integrates with OpenTelemetry (OTLP), enabling end-to-end visibility of requests as they traverse models and services. This OpenTelemetry support makes Bifrost compatible with a wide range of observability platforms, including Grafana, New Relic, and Honeycomb.

A dedicated Datadog connector is available for enterprises, providing advanced APM capabilities, LLM Observability features, and detailed request tracing directly within the Datadog platform. Beyond these, Bifrost extends its governance and security controls to the endpoint with Bifrost Edge. This ensures that AI traffic from desktop applications, browser AI, and coding agents also flows through the gateway, bringing comprehensive governance and security (virtual keys, budgets, guardrails, audit logs) to every machine, with endpoint enforcement on each device.

2. OpenTelemetry

OpenTelemetry (Otel) is a vendor-neutral set of APIs, SDKs, and tools used to instrument, generate, collect, and export telemetry data (metrics, logs, and traces). Its importance in AI gateway observability cannot be overstated because it standardizes how observability data is produced and transmitted. AI gateways like Bifrost can export data in OTLP format, making it consumable by any compatible backend, offering flexibility and avoiding vendor lock-in. This enables comprehensive distributed tracing across complex AI pipelines, helping pinpoint performance bottlenecks or errors originating from specific models or services.

3. Prometheus and Grafana

This open-source combination is a popular choice for metrics-driven observability. Prometheus is a monitoring system that collects metrics from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts. Grafana then visualizes this data through customizable dashboards, allowing teams to track key performance indicators (KPIs) for their AI gateway and downstream models. Bifrost's native Prometheus support means teams can easily integrate gateway metrics into their existing Prometheus and Grafana setups for real-time performance monitoring.

4. Datadog

Datadog offers a comprehensive monitoring and security platform that includes robust APM, infrastructure monitoring, log management, and a dedicated LLM Observability solution. Its integration with AI gateways, particularly through a Bifrost Datadog connector, provides deep insights into AI application performance. Teams can monitor LLM responses, token usage, latency, and errors, and correlate these with other infrastructure metrics. Datadog's distributed tracing capabilities are crucial for tracking requests through complex AI architectures, from the user application, through the gateway, and to the various LLM providers.

5. New Relic

New Relic provides a full-stack observability platform that helps engineers monitor, debug, and optimize their entire software estate. With its APM, infrastructure monitoring, and powerful dashboards, New Relic can ingest telemetry data from AI gateways via OpenTelemetry or custom integrations. This allows teams to visualize the performance of their AI models, track errors, and manage costs associated with LLM usage. New Relic's focus on correlating data across logs, metrics, and traces helps provide a holistic view of AI application health and performance.

6. Honeycomb

Honeycomb specializes in high-cardinality data analysis and distributed tracing, making it well-suited for the exploratory nature of AI application debugging. By instrumenting an AI gateway with OpenTelemetry and sending trace data to Honeycomb, teams can ask arbitrary questions about their production systems, such as "How often do requests to GPT-4 fail when coming from a specific team?" or "What's the average latency for requests hitting the semantic cache?". Its emphasis on debugging rather than just monitoring helps teams quickly identify and resolve issues in complex AI workflows.

7. Langfuse

Langfuse is an open-source observability solution specifically designed for LLM applications. It offers detailed tracing, cost monitoring, and evaluation features tailored for prompt engineering and agent development. While Langfuse is typically integrated directly into LLM applications or frameworks like LangChain, its ability to ingest traces via OpenTelemetry means it can work alongside an AI gateway. By sending gateway-generated OpenTelemetry traces to Langfuse, teams can gain LLM-specific insights like token usage, prompt variations, and cost per request, complementing broader infrastructure observability.

Choosing the Right Tool for Your AI Gateway Stack

Selecting the ideal observability tools depends on your team's specific needs, existing infrastructure, and budget. For organizations prioritizing open-source solutions, a combination of Bifrost's native Prometheus metrics with Grafana offers a powerful and cost-effective stack. For those requiring comprehensive, enterprise-grade features and deep analytics, platforms like Datadog or New Relic, integrated via OpenTelemetry or Bifrost's direct connectors, provide extensive capabilities.

The critical factor is to ensure that your chosen tools can ingest and correlate metrics, logs, and traces from your AI gateway, offering a unified view of your AI application's health. With an AI gateway like Bifrost, teams gain the flexibility to integrate with a variety of observability backends, enabling a future-proof monitoring strategy for their evolving AI landscape.

Sources

Datadog. "Monitor LLMs with Datadog AI Observability."
OpenTelemetry. "OpenTelemetry Documentation."
Prometheus. "Prometheus Monitoring System."
Grafana Labs. "Grafana Documentation."
Langfuse. "Langfuse Documentation."