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Debby McKinney
Debby McKinney

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Top 5 LiteLLM Alternatives in 2025

#mcp #llm #chatgpt #ai

TL;DR

As enterprise LLM spending surges past $8.4 billion in 2025, teams building production AI applications need LLM gateways that scale without becoming bottlenecks. While LiteLLM has been a popular choice for multi-provider routing, production teams increasingly face performance degradation, memory leaks, and latency overhead issues at scale. This comprehensive guide explores the top 5 LiteLLM alternatives for 2025: Bifrost by Maxim AI delivering 50x faster performance with 11µs overhead at 5K RPS, Portkey with enterprise-grade observability and governance features, OpenRouter providing managed multi-model access, Helicone offering production-grade observability, and LangServe for LangChain-native deployments. Whether you're optimizing for speed, enterprise controls, or developer experience, this guide helps you choose the right LLM gateway for your production AI infrastructure.

The LLM Gateway Landscape in 2025

The AI infrastructure market has matured significantly over the past year. With Anthropic capturing 32% market share and enterprise spending on foundation model APIs more than doubling, organizations now juggle multiple LLM providers including OpenAI, Anthropic, Google Gemini, Cohere, and Mistral. Each provider offers different pricing models, API formats, rate limits, and performance characteristics.

This complexity makes LLM gateways essential infrastructure for production AI applications. These gateways act as a unified control plane between applications and model providers, abstracting provider-specific differences while adding intelligent routing, automatic failovers, and real-time observability.

However, not all gateways are built for production scale. As teams move from prototyping to production workloads handling thousands of requests per second, the gateway layer itself can become the bottleneck that slows down the entire application.

Why Look for a LiteLLM Alternative?

LiteLLM gained popularity as a Python-based abstraction layer for working with multiple LLM providers. While it simplified initial development, production teams consistently report several critical issues that warrant exploring alternatives.

Performance Degradation at Scale

GitHub issues reveal that LiteLLM experiences gradual performance degradation over time, even after disabling router features and Redis. Teams report needing to periodically restart services to maintain acceptable performance levels. According to LiteLLM's own production documentation, the platform requires worker recycling after a fixed number of requests to mitigate memory leaks, with configuration options like max_requests_before_restart=10000 becoming necessary operational overhead.

High Latency Overhead

One of the most cited concerns with LiteLLM is the significant latency overhead it introduces. With mean overhead around 500µs per request, this delay compounds in agent loops where multiple LLM calls are chained together. For real-time applications like chat agents, voice assistants, and AI-powered customer support, this latency overhead becomes a critical bottleneck that directly impacts user experience.

Database Performance Challenges

Teams using LiteLLM at scale face database-related challenges. According to user reports, when there are 1M+ logs in the database, it significantly slows down LLM API requests. Daily request volumes of 100,000+ mean hitting this threshold within just 10 days, forcing teams into complex workarounds involving cloud blob storage and multiple callback configurations.

Complex Configuration Requirements

LiteLLM's production best practices require extensive tuning: matching Uvicorn workers to CPU count, configuring worker recycling, setting database connection pool limits, and implementing separate health check applications. The platform warns against using usage-based routing in production due to performance impacts, limiting routing flexibility for cost optimization.

Memory Leak Management

Despite recent fixes addressing 90% of memory leaks, production deployments still require careful memory management strategies. The Python-based architecture contributes to higher memory footprints, with reported usage around 372MB under moderate load compared to more efficient alternatives.

If you're hitting these limitations, here are the top 5 LiteLLM alternatives that address these pain points while offering production-grade features for scaling AI applications in 2025.

Top 5 LiteLLM Alternatives

1. Bifrost by Maxim AI

The Fastest LLM Gateway Built for Production Scale

Bifrost is a production-grade LLM gateway built in Go by Maxim AI, designed specifically to address the performance and reliability challenges teams face at scale. Rather than treating the gateway as an afterthought, Bifrost positions itself as core infrastructure with minimal overhead, high throughput, and enterprise-grade features out of the box.

Why Bifrost Stands Out

Unmatched Performance:

Bifrost delivers exceptional performance that sets it apart from alternatives. Comprehensive benchmarks on identical hardware reveal dramatic differences:

Metric LiteLLM Bifrost Improvement
p99 Latency 90.72s 1.68s ~54x faster
Throughput 44.84 req/sec 424 req/sec ~9.4x higher
Memory Usage 372MB 120MB ~3x lighter
Mean Overhead ~500µs 11µs @ 5K RPS ~45x lower

The 11µs mean overhead at 5K RPS is particularly significant. This is the time Bifrost adds to each request for routing, load balancing, logging, and observability. At this level, the gateway effectively disappears from your latency budget, making it ideal for latency-sensitive applications where every millisecond counts.

Built in Go for Performance:

Unlike Python-based alternatives, Bifrost leverages Go's compiled nature, efficient memory management, and native concurrency support. This architectural choice provides:

  • Ultra-low latency: Compiled language with minimal garbage collection overhead
  • Horizontal scalability: Lightweight goroutines handle concurrent requests efficiently
  • Minimal memory footprint: Significantly lower resource usage compared to Python-based solutions
  • Built-in concurrency: Native support for high-throughput workloads without complex threading

Key Features

Unified Multi-Provider Access:

Bifrost provides a single OpenAI-compatible API for all providers, supporting OpenAI, Anthropic, AWS Bedrock, Google Vertex, Azure OpenAI, Cohere, Mistral, Ollama, Groq, and more. Access 12+ providers and 250+ models without managing different SDKs or authentication mechanisms.

Adaptive Load Balancing:

Unlike simple round-robin approaches, Bifrost intelligently distributes requests across providers and API keys based on:

  • Real-time latency measurements
  • Error rates and success patterns
  • Throughput limits and rate limiting
  • Provider health status

This ensures optimal resource utilization and cost efficiency without manual tuning, automatically adapting to provider performance and availability.

Semantic Caching:

Semantic caching goes beyond simple response caching by identifying semantically similar requests. This reduces repeated inference costs significantly, particularly valuable for applications with common query patterns like customer support or documentation queries.

Model Context Protocol (MCP) Support:

Bifrost includes native support for Model Context Protocol, enabling AI models to use external tools like filesystems, web search, and databases. This makes it ideal for building complex agentic applications that require tool use and external data access.

Enterprise Governance:

Governance features include:

  • Budget Management: Hierarchical cost control with virtual keys, teams, and customer budgets
  • SSO Integration: Google and GitHub authentication support
  • Usage Tracking: Monitor usage at customer, team, and user levels
  • Role-Based Access Control: Granular permissions for different user roles
  • Vault Support: Secure API key management with HashiCorp Vault integration

Comprehensive Observability:

Built-in observability features include:

  • Native Prometheus metrics for performance monitoring at /metrics endpoint
  • OpenTelemetry support for distributed tracing
  • Comprehensive structured logging
  • Real-time dashboard for quick insights without complex setup

This integrates seamlessly with Maxim's AI observability platform for end-to-end visibility into AI application behavior, enabling teams to track production quality, debug issues, and optimize performance.

Migration Simplicity

One of Bifrost's key advantages is migration simplicity. As a drop-in replacement, you can switch from LiteLLM with minimal code changes.

From LiteLLM SDK:

from litellm import completion

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

# After - just add base_url
response = completion(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Hello!"}],
    base_url="http://localhost:8080/litellm"
)
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Setup in 30 Seconds:

# Using Docker
docker run -p 8080:8080 \
  -e OPENAI_API_KEY=your-key \
  -e ANTHROPIC_API_KEY=your-key \
  maximhq/bifrost

# Or using npx
npx @maximhq/bifrost start
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Visit http://localhost:8080 to access the web dashboard and start routing requests immediately.

Integration with Maxim's Platform

Bifrost works seamlessly with Maxim's comprehensive AI platform, providing end-to-end visibility and control:

Pre-Production Quality Assurance:

Production Monitoring:

This integrated approach addresses the full spectrum of AI agent quality evaluation needs, from development through production.

Best For:

  • Teams requiring ultra-low latency (under 15µs overhead)
  • High-throughput production applications (5K+ RPS)
  • Organizations prioritizing performance and cost optimization
  • Teams building complex multi-agent AI systems
  • Enterprises needing governance and budget controls

Pricing:

Open-source and free to self-host. Enterprise features with additional support available through Maxim AI.

Learn More:

2. Portkey

Enterprise-Grade AI Gateway with Advanced Observability

Portkey is a comprehensive AI gateway designed to streamline and enhance AI integration for developers and organizations. Built as a full-stack platform, it serves as a unified interface for interacting with over 250 AI models, offering advanced tools for control, visibility, and security in Generative AI applications.

Key Strengths

Extensive Provider Support:

Portkey connects to 1600+ LLMs and providers across different modalities through a single API. This includes all major providers like OpenAI, Anthropic, Google Vertex, AWS Bedrock, Azure OpenAI, Cohere, and Mistral, as well as emerging providers and open-source models.

Advanced Observability:

Portkey excels in observability, capturing every request and tracing its complete journey. Features include:

  • Unified traces: Connect LLM calls to downstream actions, errors, and latencies
  • Real-time monitoring: Track API requests, costs, and guardrail violations in real-time
  • Export capabilities: Send logs to your reporting tools for further analysis
  • Deep debugging: Trace failures back through complex agent workflows

Prompt Management:

Comprehensive prompt lifecycle management with versioning, testing, and change tracking built in. Create, manage, and version prompt templates collaboratively through a universal prompt playground.

Smart Routing and Fallbacks:

Dynamically switch between models, distribute workloads, and ensure failover with configurable rules. Portkey supports:

  • Conditional routing based on cost, latency, and accuracy
  • Automatic fallbacks during provider failures or errors
  • Load balancing across multiple providers and API keys
  • Provider optimization to switch to most cost-effective options

Built-in Guardrails:

Choose from 50+ pre-built guardrails to ensure compliance with security and accuracy standards. Verify LLM inputs and outputs to adhere to specified checks, or bring your own custom guardrails.

Enterprise Security:

  • SOC 2, HIPAA, GDPR, and CCPA compliant
  • Secure key management with virtual keys
  • Role-based access control for granular permissions
  • SSO support via SAML

Semantic Caching:

Intelligent caching that identifies semantically similar requests, reducing costs and improving latency for repeated queries.

Use Cases

Portkey is particularly well-suited for:

  • Enterprises requiring comprehensive governance and compliance features
  • Teams managing 25+ GenAI use cases across the organization
  • Organizations needing detailed cost tracking per use case or team
  • Companies with strict security and compliance requirements
  • Teams building with popular frameworks like LangChain, CrewAI, and AutoGen

Limitations

  • Pricing: Starts at $49/month for managed service, which may be higher than self-hosted alternatives
  • Complexity: Rich feature set may be overkill for simple use cases
  • Smaller community: Newer compared to LiteLLM, with a smaller open-source community

Best For:

  • Enterprise organizations with complex governance requirements
  • Teams needing comprehensive observability and prompt management
  • Companies requiring compliance certifications (SOC 2, HIPAA, GDPR)
  • Organizations managing multiple AI use cases across departments

Learn More:

3. OpenRouter

Managed Multi-Model Gateway with Simplicity First

OpenRouter is a managed service that provides a unified API to access hundreds of AI models through a single endpoint. It emphasizes simplicity and ease of use, making it ideal for teams wanting immediate multi-model access without infrastructure overhead.

Key Strengths

Extensive Model Catalog:

OpenRouter provides access to 500+ models from multiple providers through a single API. This includes frontier models from OpenAI, Anthropic, Google, and Meta, as well as open-source alternatives and specialized models.

Zero Infrastructure Management:

As a fully managed service, OpenRouter handles all the operational complexity:

  • No servers to deploy or maintain
  • Automatic scaling to handle traffic spikes
  • Built-in high availability and redundancy
  • Regular updates with new model releases

Quick Setup:

Get started in under 5 minutes by simply changing your API base URL. OpenRouter provides an OpenAI-compatible interface, making migration from OpenAI straightforward.

Automatic Fallbacks:

Seamlessly switches between providers during outages, ensuring high availability for production applications.

Bring Your Own Keys (BYOK):

Option to use your own provider API keys while still benefiting from OpenRouter's routing and management capabilities.

Pass-Through Billing:

Centralized billing across all providers with transparent pricing. OpenRouter adds a small markup (typically 5%) on requests for the management layer.

Web Interface:

Direct model access through a web UI, allowing non-technical stakeholders to test and compare models without writing code.

Privacy and Security

OpenRouter takes a privacy-first approach:

  • Zero Data Retention (ZDR): Configure requests to only route to providers that don't store prompts
  • No logging by default: Prompts and responses aren't stored unless you opt in
  • SOC 2 Type I compliant: As of 2025, with ongoing security certifications

Limitations

  • Limited customization: Less control over routing logic compared to self-hosted solutions
  • Basic access controls: Authentication features are more limited than enterprise alternatives
  • Markup pricing: 5% fee on all requests adds to overall costs
  • Latency overhead: Adds approximately 25-40ms of overhead under typical conditions

Best For:

  • Teams wanting immediate multi-model access without setup
  • Rapid prototyping and experimentation across models
  • Non-technical stakeholders needing direct model access
  • Small to medium-sized teams prioritizing speed over customization

Learn More:

4. Helicone

Production-Grade Gateway with Built-In Observability

Helicone is an OpenAI-compatible proxy that positions itself as a comprehensive observability and routing solution. It offers both cloud-hosted and self-hosted deployment options, making it flexible for different organizational requirements.

Key Strengths

Low Latency Overhead:

Helicone emphasizes minimal performance impact with ultra-low overhead and optimized request handling. The gateway is designed to add minimal latency to requests while providing comprehensive features.

Built-In Observability:

Rich logging and analytics tools provide deep insights into LLM usage:

  • Request volume and latency tracking
  • Cost analysis and budget monitoring
  • Error rate tracking and debugging
  • User and session-level analytics

Health-Aware Routing:

Uses circuit breaking to detect provider failures and automatically route to healthy providers, ensuring high availability without manual intervention.

Flexible Deployment:

Offers both managed cloud service and self-hosted options, allowing teams to choose based on their security and compliance requirements.

Caching Capabilities:

Smart caching to reduce redundant requests and lower costs, particularly effective for applications with repeated queries.

Developer-Friendly:

Quick integration with existing OpenAI-compatible code, requiring minimal changes to get started.

Limitations

  • Limited enterprise features: Lacks advanced role-based access controls and audit logging compared to enterprise-focused alternatives
  • Narrower model support: Primarily optimized for OpenAI-compatible APIs, with less comprehensive support for other providers
  • Basic governance: Limited policy enforcement and compliance features for regulated environments

Best For:

  • Teams prioritizing observability and monitoring
  • Organizations needing both cloud and self-hosted options
  • Development teams wanting rich analytics without complexity
  • Applications requiring smart caching and cost optimization

Learn More:

5. LangServe

Native Framework for LangChain Deployments

LangServe is a wrapper that exposes LangChain agents and workflows as RESTful APIs. While not a full LLM gateway in the traditional sense, it's commonly used as one by teams already invested in the LangChain ecosystem.

Key Strengths

LangChain-Native:

Seamless integration with LangChain applications, allowing teams to deploy their existing LangChain chains and agents as production APIs without rewriting code.

Highly Flexible Architecture:

Complete control over how your LangChain workflows are exposed and deployed, with the ability to customize every aspect of the API.

Framework Integration:

Works naturally with the entire LangChain ecosystem, including:

  • LangGraph for building stateful agent systems
  • LangSmith for tracing and debugging
  • Vector stores and retrieval systems
  • Custom tools and function calling

RESTful API Generation:

Automatically generates OpenAPI-compatible REST endpoints from LangChain chains, making integration with existing systems straightforward.

Limitations

  • No built-in access control: Security and authentication must be implemented separately
  • Manual observability setup: Requires integration with external monitoring tools
  • No multi-provider routing: Focuses on deploying chains rather than routing across providers
  • Limited production features: Missing features like automatic failover, load balancing, and semantic caching
  • Operational overhead: Teams must handle scaling, monitoring, and reliability themselves

Deployment Considerations

LangServe requires teams to build their own infrastructure around it:

  • Set up authentication and authorization
  • Implement monitoring and logging
  • Configure scaling and load balancing
  • Build retry logic and error handling

For teams already using LangChain extensively, this trade-off may be acceptable. However, organizations seeking a production-ready gateway with minimal operational overhead should consider alternatives like Bifrost or Portkey.

Best For:

  • Teams heavily invested in the LangChain ecosystem
  • Organizations with existing DevOps infrastructure for API deployment
  • Projects requiring complete control over deployment architecture
  • Development teams comfortable building custom observability and security layers

Learn More:

Feature Comparison Table

Feature Bifrost Portkey OpenRouter Helicone LangServe
Deployment Self-hosted Self/Managed Managed Self/Managed Self-hosted
Open Source ✅ ✅ ❌ ✅ ✅
Multi-Provider ✅ 12+ ✅ 250+ ✅ 500+ ✅ Limited ⚠️ Custom
Performance (Overhead) 11µs @ 5K RPS ~25-50ms ~25-40ms Low Variable
Automatic Failover ✅ ✅ ✅ ✅ ❌
Load Balancing ✅ Adaptive ✅ ✅ ✅ ❌
Semantic Caching ✅ ✅ ❌ ✅ ❌
Observability ✅ Native ✅ Rich ⚠️ Basic ✅ Strong ❌
Governance/RBAC ✅ ✅ ⚠️ Basic ⚠️ Limited ❌
Budget Management ✅ ✅ ✅ ✅ ❌
MCP Support ✅ ✅ ❌ ❌ ⚠️ Custom
SSO Integration ✅ ✅ ❌ ❌ ❌
Compliance SOC 2 ready SOC 2, HIPAA, GDPR SOC 2 Type I Varies Manual
Setup Time 30 seconds 5 minutes 5 minutes 5 minutes 15-30 minutes
Pricing Free (OSS) $49+/month Pass-through + 5% Free (OSS) Free (OSS)

How to Choose the Right LLM Gateway

Selecting the right LLM gateway depends on your specific requirements, scale, and organizational constraints. Here's a decision framework to guide your choice:

Choose Bifrost When:

  • Performance is critical: You need ultra-low latency (under 15µs overhead) for real-time applications
  • High throughput: Your application handles 5K+ requests per second
  • Cost optimization: You want adaptive load balancing and semantic caching to reduce costs
  • Enterprise governance: You need budget management, SSO, and hierarchical access controls
  • Production reliability: You require 99.99% uptime with automatic failover
  • Comprehensive platform: You want integration with end-to-end AI evaluation and observability

Choose Portkey When:

  • Enterprise requirements: You need comprehensive governance and compliance (SOC 2, HIPAA, GDPR)
  • Prompt management: Managing and versioning prompts is critical to your workflow
  • Multiple use cases: You're managing 25+ AI use cases across your organization
  • Advanced observability: You need unified traces across complex agent workflows
  • Framework integration: You're building with LangChain, CrewAI, or AutoGen

Choose OpenRouter When:

  • Quick setup: You need immediate multi-model access without infrastructure
  • Non-technical access: Product managers or other stakeholders need direct model testing
  • Rapid prototyping: You're experimenting across many models and want simplicity
  • Managed service: You prefer not to manage infrastructure yourself

Choose Helicone When:

  • Observability focus: Rich analytics and monitoring are your primary needs
  • Flexible deployment: You want options for both cloud and self-hosted deployments
  • Cost tracking: Detailed usage analytics and cost monitoring are priorities
  • OpenAI optimization: Your application primarily uses OpenAI-compatible models

Choose LangServe When:

  • LangChain ecosystem: You're heavily invested in LangChain and LangGraph
  • Custom control: You need complete control over deployment architecture
  • Existing infrastructure: You have robust DevOps practices for API deployment
  • Framework coupling: Your application logic is tightly coupled to LangChain

Migration Strategies

Migrating from LiteLLM to Bifrost

Step 1: Install and Configure Bifrost

# Using Docker
docker run -p 8080:8080 \
  -e OPENAI_API_KEY=$OPENAI_API_KEY \
  -e ANTHROPIC_API_KEY=$ANTHROPIC_API_KEY \
  maximhq/bifrost
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Step 2: Update Your Application Code

from litellm import completion

# Simply add base_url parameter
response = completion(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Hello!"}],
    base_url="http://localhost:8080/litellm"  # Point to Bifrost
)
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Step 3: Test and Validate

Run your existing test suite to ensure compatibility. Bifrost is designed as a drop-in replacement, so most applications work without code changes.

Step 4: Monitor Performance

Access the Bifrost dashboard at http://localhost:8080 to monitor:

  • Request latency and throughput
  • Provider health and failover events
  • Cost tracking per model and provider
  • Error rates and retry patterns

Step 5: Integrate with Maxim AI (Optional)

For comprehensive AI application observability, integrate Bifrost with Maxim's platform:

from bifrost.plugins import maxim

maxim_plugin = maxim.NewMaximLoggerPlugin(
    os.getenv("MAXIM_API_KEY"),
    os.getenv("MAXIM_LOG_REPO_ID")
)
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This enables end-to-end tracing, production quality monitoring, and automatic dataset curation for continuous improvement.

Real-World Use Cases

High-Throughput Chat Applications

For conversational AI applications handling thousands of concurrent users, Bifrost's 11µs overhead and automatic failover ensure consistent user experience even during provider outages. Companies like Comm100 rely on Maxim's platform for production AI quality in customer support scenarios.

Multi-Agent Enterprise Systems

Complex multi-agent AI systems generate high request volumes with diverse routing needs. Bifrost's adaptive load balancing and semantic caching reduce costs while maintaining performance across agent interactions. Organizations like Atomicwork use Maxim's integrated platform to scale enterprise support with AI agents.

Financial Services and Regulated Industries

Organizations in regulated industries require governance, compliance, and audit trails. Portkey's SOC 2, HIPAA, and GDPR compliance combined with comprehensive logging meets these requirements. Teams can enforce guardrails for content filtering and PII protection while maintaining detailed audit logs.

Rapid Prototyping and Research

OpenRouter excels for research teams and startups experimenting across many models. The managed service eliminates infrastructure overhead, allowing teams to focus on model selection and prompt engineering rather than operations.

LangChain-Native Applications

Teams building complex agent workflows with LangChain and LangGraph benefit from LangServe's native integration. While it requires more operational overhead, the tight coupling with the LangChain ecosystem simplifies development for framework-dependent applications.

The Role of Observability in Production AI

Regardless of which gateway you choose, comprehensive observability is critical for production AI applications. The gateway handles routing and load balancing, but understanding quality, debugging failures, and optimizing performance requires deeper visibility.

Maxim's AI observability platform provides:

  • Real-time monitoring: Track production quality with distributed tracing across your entire AI stack
  • Quality evaluation: Run automated evaluations on production traffic to catch regressions
  • Cost optimization: Identify expensive patterns and optimize model selection
  • Dataset curation: Automatically build evaluation datasets from production logs
  • Root cause analysis: Debug failures by tracing through complex agent workflows

This level of observability is essential for building reliable AI systems that users can trust.

Conclusion

As AI applications move from prototypes to production at scale, the infrastructure layer becomes critical. LiteLLM served the market well in the early days of multi-provider LLM integration, but production teams need gateways that treat performance, reliability, and observability as first-class concerns.

Bifrost by Maxim AI delivers on these requirements with 54x faster p99 latency, 9.4x higher throughput, and 45x lower overhead compared to LiteLLM. The combination of ultra-low latency, automatic failover, semantic caching, and enterprise governance makes it the definitive choice for teams building production-grade AI applications.

For organizations requiring enterprise-grade governance and comprehensive prompt management, Portkey provides a robust platform with deep observability features. Teams prioritizing simplicity and managed services benefit from OpenRouter's extensive model catalog and zero infrastructure overhead. Helicone offers strong observability features with flexible deployment options, while LangServe serves teams deeply integrated with the LangChain ecosystem.

The migration path to Bifrost is straightforward: one line of code to point your existing LiteLLM SDK to Bifrost's endpoint. Setup takes 30 seconds with Docker or npx, and you get production-grade infrastructure immediately.

Moreover, Bifrost's integration with Maxim's comprehensive platform for AI simulation, evaluation, and observability provides end-to-end visibility and control throughout the AI development lifecycle.

Your AI applications deserve infrastructure that scales with your ambitions, not bottlenecks that slow you down.

Ready to Upgrade Your LLM Gateway?

Make the switch to Bifrost today and experience the difference that production-grade infrastructure makes for your AI applications.

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