AI gateway alternatives

Resources

• [LiteLLM] [LiteLLM Alternatives]. Compare LiteLLM alternatives for production AI gateway performance, reliability, governance, observability, MCP support, and migration path.
• [Portkey] [Portkey Alternatives]. Compare Portkey alternatives for multi-provider routing, SDK compatibility, latency overhead, MCP capabilities, deployment model, and enterprise controls.

Comparison hub

AI Gateway Alternative Evaluation

The alternatives hub compares AI gateway platforms for production workloads. It focuses on multi-model routing, MCP support, governance, cost tracking, scalability, enterprise-grade security, and production reliability.

Detailed LiteLLM and Portkey comparisons cover architecture, performance, migration fit, governance, SDK compatibility, MCP support, and enterprise controls. [LiteLLM alternatives] [Portkey alternatives] [Buyer guide]

What production teams compare

Evaluation Criteria

• Performance. Latency overhead, throughput, success rate under load, memory usage, and runtime architecture.
• Reliability. Automatic fallbacks, routing rules, load balancing, backpressure, circuit breaking, and health-aware routing.
• Governance. Virtual keys, budgets, rate limits, RBAC, SSO, audit logs, guardrails, and enterprise deployment controls.
• Agent tooling. MCP Gateway support, MCP Code Mode, tool hosting, federated authentication, and CLI coding-agent governance.
• Migration fit. Drop-in SDK compatibility, base URL changes, setup time, self-hosting complexity, and operational dependencies.

Open Source & Enterprise

Drop-in replacement for compatible AI SDKs

Change one line of code to point compatible SDKs at Bifrost. Works with OpenAI, Anthropic, LiteLLM, Google GenAI, LangChain, and Vercel AI SDK. [Gateway setup docs] [Drop-in replacement docs]

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("OPENAI_API_KEY"),
    base_url="https://<bifrost_url>/openai",
)

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello!"}],
)

import os
from anthropic import Anthropic

anthropic = Anthropic(
    api_key=os.environ.get("ANTHROPIC_API_KEY"),
    base_url="https://<bifrost_url>/anthropic",
)

message = anthropic.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello, Claude"}],
)

import litellm

# Set the base URL to your Bifrost deployment
litellm.api_base = "https://<bifrost_url>"

response = litellm.completion(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello!"}],
)

import google.generativeai as genai

genai.configure(
    api_key="YOUR_API_KEY",
    transport="rest",
    client_options={"api_endpoint": "<bifrost_url>/google"},
)

model = genai.GenerativeModel("gemini-pro")
response = model.generate_content("Hello!")

• Point the SDK base URL at your Bifrost deployment.
• Keep API keys in your environment or secret manager.
• See the docs for provider-specific configuration and deployment steps.

Trust

• Open Source. Bifrost is open source under the Apache 2.0 License. [GitHub]
• Publisher. Bifrost is published by H3 Labs Inc. and Maxim AI.
• Compliance. The site references SOC 2 Type II, GDPR, HIPAA, and ISO 27001 signals. [Enterprise deployment]
• Deployment. Enterprise resources cover VPC, on-premise, air-gapped, and multi-cloud use. [Enterprise deployment]

FAQ

Related resources

• [LiteLLM alternatives]
• [Portkey alternatives]
• [Performance benchmarks]
• [LLM gateway buyer's guide]
• [Pricing]
• [Docs]
• [GitHub]