As large language models (LLMs) become more central to modern applications, developers are constantly looking for tools that simplify how they work with multiple model providers. Whether you're building with OpenAI, Anthropic, Cohere, or open-source models like LLaMA and Mistral, managing those connections in a clean and scalable way can quickly get complicated. You need routing, observability, token tracking, and failover strategies, all without cluttering your application code.
This is where LiteLLM has earned attention. It's a Python-based abstraction layer that offers a unified API across different LLM providers. It’s lightweight, easy to plug into your app, and helps you switch between models with minimal effort. For early-stage projects and small teams, it’s a practical starting point.
However, as applications mature and workloads increase, LiteLLM’s limitations can become more noticeable. Some teams outgrow its simplicity and start looking for platforms that offer deeper insights, better infrastructure control, and more advanced features.
One common concern we’ve consistently heard from developers is that LiteLLM introduces noticeable latency.You can see the benchmarking results here.
TrueFoundry AI Gateway delivers ~3–4 ms latency, handles 350+ RPS on 1 vCPU, scales horizontally with ease, and is production-ready, while LiteLLM suffers from high latency, struggles beyond moderate RPS, lacks built-in scaling, and is best for light or prototype workloads.
Built for Speed: ~10ms Latency, Even Under Load
- Handles 350+ RPS on just 1 vCPU — no tuning needed
- Production-ready with full enterprise support
LiteLLM is a great tool to get started with multi-model routing. It abstracts over different LLM providers like OpenAI, Anthropic, Cohere, and more — making it easier to prototype agent workflows with a single interface.
However, when moving beyond local development into enterprise-grade use cases, several critical challenges emerge -
In this article, we’ll break down what LiteLLM does well and where it might fall short. Then, we’ll explore five strong alternatives that offer broader capabilities. Whether you're looking for more control, deeper observability, or better scalability, these tools can help you find the right fit for your growing GenAI infrastructure needs.
What is LiteLLM?
LiteLLM is an open-source Python library that provides a simple, unified API for interacting with multiple large language model (LLM) providers. Its main goal is to abstract away the differences between providers like OpenAI, Anthropic, Cohere, Hugging Face, and others so developers can switch between them without rewriting code. With just a few configuration changes, you can test, compare, or switch models while keeping your application logic consistent.
It’s particularly useful for teams experimenting with different models or building LLM-backed apps that may need flexibility in routing requests across providers.
Key Features:
- Unified API for multiple LLMs using the OpenAI-compatible format
- Easy model switching through configuration
- Proxy server mode for logging, rate limiting, and basic caching
- Token usage tracking and support for API key management
- Open-source and simple to integrate into any Python backend
Pricing: LiteLLM itself is completely free and open source. Since it doesn't host or serve models directly, you only pay for the usage of the underlying LLM providers (like OpenAI or Anthropic). There’s no licensing fee to use LiteLLM.
Challenges: While LiteLLM is great for quick integrations and prototyping, it may fall short for production-grade applications. It lacks advanced observability, security controls, audit trails, and enterprise features like model performance tracking or fine-tuning support. There’s also limited built-in support for self-hosted or open-source model deployment, which some teams may need as they scale. It’s a powerful abstraction layer but not a full-fledged infrastructure platform.
1. High Latency Overhead
One of the most cited concerns with LiteLLM is the significant latency it introduces, especially when acting as a proxy for external LLM providers like OpenAI, Anthropic, or Cohere. In performance benchmarks, this latency overhead becomes a bottleneck for real-time applications such as chat agents, voice assistants, and AI-powered customer support tools. The additional delay often outweighs the benefits of its abstraction, especially when used in agent loops where multiple LLM calls are chained together.
2. Difficult to Deploy in Enterprise Environments
LiteLLM’s lightweight nature makes it appealing for simple use cases, but deploying it in enterprise-grade environments—such as on-premise servers, secure VPCs, or Kubernetes clusters—requires significant manual scaffolding. There’s no built-in support for platform-level concerns like service discovery, autoscaling, centralized logging, or secure configuration. As a result, teams in regulated industries or with strict compliance needs find it hard to adopt and operationalize LiteLLM in production.
3. Lacks Enterprise-Level Support and SLAs
LiteLLM is an open-source project with no formal commercial backing, which means there’s no enterprise support plan, no SLAs for uptime, and no dedicated escalation path. This makes it a risky dependency for mission-critical AI workloads where reliability, accountability, and proactive support are essential. Teams building production systems need guarantees and support structures that LiteLLM currently does not offer.
4. Bug-Prone at Scale
Due to its rapid development cycle and community-driven nature, LiteLLM can be unstable when used at scale. Users have reported frequent regressions between versions, edge-case bugs, and inconsistent behavior in concurrent or multi-tenant scenarios. Without rigorous testing pipelines or backward compatibility guarantees, deploying LiteLLM into high-scale systems often leads to unpredictable production issues.
5. Limited Functionality Beyond API Proxying
While LiteLLM simplifies the task of routing API calls across multiple LLM providers, it does little beyond that. It doesn’t support open-source model hosting, fine-tuning workflows, observability such as tracing of agents, multi-tenant governance, or agent tool integration—features often required by enterprises deploying LLMs at scale. Teams looking for a unified GenAI platform will find LiteLLM too narrow in scope, requiring them to build or bolt on these missing capabilities themselves.
6. Good for Prototyping, Not for Production
LiteLLM is well-suited for developers who need to quickly test different LLM APIs or prototype new ideas. However, the moment those prototypes need to scale into production—especially in terms of observability, security, and reliability—it starts to fall short. Managing API keys, usage quotas, latency metrics, and routing logic manually becomes a burden that doesn’t scale with growing workloads or team needs.
Built for Speed: ~10ms Latency, Even Under Load
- Handles 350+ RPS on just 1 vCPU — no tuning needed
- Production-ready with full enterprise support
How Does LiteLLM Work?
LiteLLM works by sitting between your application and multiple large language model (LLM) providers, acting as a lightweight abstraction layer. Instead of calling OpenAI, Anthropic, or other LLM APIs directly, you send your requests through LiteLLM, which then forwards them to the selected provider using a consistent API format. This design allows you to write your application once and swap out LLMs behind the scenes without making major changes to your codebase.
The library is built to mimic the popular OpenAI API format, so if your app already uses OpenAI’s chat/completions or completions endpoints, you can plug in LiteLLM with minimal refactoring. You can change providers simply by updating environment variables or configuration files, which makes it ideal for testing different models or balancing performance and cost.
In addition to its core abstraction layer, LiteLLM also supports a proxy mode. In this setup, LiteLLM runs as a local or hosted server that handles LLM API calls for your application. This proxy enables additional functionality, such as:
- Logging: Capturing and storing requests, responses, and metadata for debugging and analysis
- Rate limiting: Prevent overuse of tokens or hitting provider rate limits
- Basic caching: Avoid repeat calls by storing previous responses
- Token usage tracking: Monitor how many tokens each request consumes
- Provider fallback: Set up simple logic to fall back to another model if one fails
LiteLLM’s proxy mode is especially useful in development and staging environments where teams need visibility into how models behave without adding heavy infrastructure.
Behind the scenes, LiteLLM uses Python’s requests library to send and receive API calls. It supports both synchronous and asynchronous calls and includes hooks for custom logging, key rotation, and request handling. The architecture is intentionally lightweight, with minimal dependencies and a clear focus on developer experience.
While LiteLLM is not designed to manage complex model routing at scale, it gives teams an easy on-ramp to working with multiple providers and reduces integration time significantly. For many early-stage applications or experiments, it removes the friction that typically comes with managing different LLM APIs.
Top 5 LiteLLM Alternatives of 2025
While LiteLLM is a helpful abstraction layer for working with multiple LLM providers, it may not offer everything teams need as they move into production or handle more complex workloads. If you're looking for greater observability, model orchestration, traffic control, or API management, other platforms provide more robust functionality. These alternatives can better support scaling, customization, and long-term reliability in GenAI applications.
Here are five top alternatives to consider in 2025:
- TrueFoundry
- Helicone
- Portkey
- Eden AI
- Kong AI
1. TrueFoundry
TrueFoundry is a powerful alternative to LiteLLM for teams that need more than just model abstraction. While LiteLLM is excellent for unifying APIs across LLM providers, TrueFoundry is built for teams who want to run LLMs in production—backed by robust infrastructure, observability, and full control over how models are deployed and scaled.
TrueFoundry includes a built-in LLM Gateway, but it doesn’t stop at routing. You can host, fine-tune, and serve open-source models like Mistral or LLaMA on your own cloud or on-premises setup. This gives teams more flexibility and data control than LiteLLM, which relies entirely on third-party APIs.
In contrast to LiteLLM’s lightweight proxy, TrueFoundry offers a fully managed system with traffic routing, fallback handling, prompt versioning, cost analytics, and observability built in. It works across providers like OpenAI, Anthropic, and Hugging Face but also supports self-hosted models using vLLM and TGI. That means you can start with API-based models and gradually move to hosting your own—without changing your integration.
Because it runs on your Kubernetes infrastructure, TrueFoundry also offers a level of security and compliance that LiteLLM simply isn’t designed for. You avoid egress costs, retain full data ownership, and can enforce internal governance policies with ease.
Top Features:
- Production-ready LLM Gateway with support for hosted and self-hosted models.
- Full prompt versioning, rollback, and performance testing tools.
- Multi-cloud and on-prem support with full Kubernetes integration.
- Fine-tuning workflows for open-source models.
- Token usage, latency, and cost monitoring at the request level.
Why it’s a best LiteLLM alternative:
LiteLLM simplifies development, but TrueFoundry enables scale. It’s ideal for teams moving beyond experimentation and into production, especially those who want to maintain flexibility over where and how their models run. If you're ready to build serious GenAI systems with observability, deployment control, and performance optimization, TrueFoundry offers what LiteLLM lacks out of the box.
For more details, check out our documentation.
Built for Speed and Enterprise workloads: ~10ms Latency, Even Under Load
- Handles 350+ RPS on just 1 vCPU — no tuning needed
- Production-ready with full enterprise support
2. Helicone
Helicone is an open-source observability layer purpose-built for teams working with large language models. While LiteLLM focuses on routing and unifying access to multiple providers, Helicone solves a different but equally important challenge: visibility. It allows developers to track every LLM request in detail so they can understand, debug, and optimize model usage as applications scale.
Helicone works by sitting between your application and your LLM provider. Instead of calling OpenAI or Anthropic directly, you send your API calls through Helicone’s proxy. From there, it captures rich metadata about each request, including latency, prompt input, response output, token usage, error rates, and estimated cost. This data is then displayed in a clean, developer-friendly dashboard.
Unlike LiteLLM, which abstracts away model differences and makes switching providers easier, Helicone is ideal for teams who are already locked into one or more providers but want more transparency. It’s especially valuable when prompt quality, user behavior, and performance consistency matter.
Helicone also supports self-hosting, which gives teams full control over logs and data retention. It integrates easily into most Python-based GenAI stacks and adds minimal overhead to setup.
Top Features:
- Real-time logging of prompt, response, and token-level metrics
- Built-in dashboards for cost, latency, and error tracking
- Easy integration with OpenAI, Anthropic, and other APIs
- Privacy-first, self-hostable architecture
- Lightweight and dev-friendly to set up
Why it’s a LiteLLM alternative:
Helicone doesn’t replace LiteLLM’s routing logic, but it can act as a strong companion—or an alternative if your priority shifts from model abstraction to monitoring. If you’re using one or two primary models and need deeper insight into how they behave in production, Helicone offers visibility that LiteLLM currently lacks. It’s a focused tool that adds real value to teams aiming to debug and refine their LLM usage at scale.
3. Portkey
Portkey is an LLM infrastructure layer designed to help developers manage API calls across multiple language model providers with greater reliability. Like LiteLLM, it offers a unified interface to connect with models from OpenAI, Anthropic, Mistral, and others. But where LiteLLM focuses on simplicity, Portkey is built for production environments that require higher resilience and control.
It introduces features such as automatic retries, caching, request timeouts, and fallback routing. This makes it easier to keep GenAI applications stable, even when providers are experiencing latency or downtime. Portkey also supports cost and token tracking per request, helping teams optimize usage more effectively than LiteLLM’s minimal tracking.
Portkey can be deployed in the cloud or self-hosted and works well for teams who want a lightweight reliability layer without building their own retry and routing logic from scratch.
Top Features:
- Multi-provider routing with fallback and retry logic
- Caching, timeouts, and rate limiting
- Real-time cost and token usage tracking
- OpenAI-compatible proxy endpoint
- Self-hostable or managed deployment
Why it’s a LiteLLM alternative:
Portkey is a good step up when your LLM calls need more than simple abstraction. It adds robustness and basic observability, making it suitable for teams moving from experimentation into production where uptime and cost efficiency start to matter.
4. Eden AI
Eden AI is an API marketplace that allows developers to access multiple AI services—like language models, OCR, translation, and speech-to-text, through a single unified API. While LiteLLM focuses exclusively on abstracting LLM providers, Eden AI takes a broader approach, making it easy to mix and match services from different vendors without managing separate integrations.
For LLMs, it supports providers like OpenAI, Cohere, and DeepAI and allows routing based on pricing, speed, or availability. It’s especially useful for teams building multi-modal AI applications who want a plug-and-play solution with minimal setup.
Top Features:
- Unified API for multiple AI providers across modalities
- Supports LLMs, text-to-speech, translation, image analysis, and more
- Provider benchmarking for performance and pricing
- Real-time usage and billing analytics
- No-Code interface for testing and evaluating APIs
Why it’s a LiteLLM alternative:
If you’re looking for an easy way to connect to LLMs and other AI services without managing multiple APIs, Eden AI is a practical option. While not as developer-centric as LiteLLM, it’s ideal for teams who want a broader range of AI tools through one interface.
5. Kong AI
Kong AI is an extension of the popular Kong Gateway, built to support API management for AI workloads, including large language models. While LiteLLM focuses on abstracting LLM APIs at the application level, Kong AI brings in enterprise-grade API gateway capabilities like traffic control, authentication, rate limiting, and observability—tailored for AI services.
Kong AI enables organizations to manage access to multiple LLM providers securely and reliably. It doesn’t provide unified LLM syntax like LiteLLM, but it does help teams enforce governance, monitor traffic, and integrate LLM calls into larger API ecosystems. For companies already using Kong for traditional APIs, extending it to cover LLMs can be a natural fit.
Kong also supports plugins and integrations with tools like Prometheus and OpenTelemetry, giving teams more insight into request-level behavior and system performance.
Top Features:
- AI-specific extensions for the Kong Gateway.
- Request authentication, rate limiting, and API key management.
- Traffic shaping, retries, and circuit breaking.
- Integration with observability tools like Grafana and Prometheus.
- Works with both cloud-based and self-hosted LLM APIs.
Why it’s a LiteLLM alternative:
Kong AI is best for teams focused on security, scalability, and governance. It’s not a model abstraction layer but a powerful infrastructure option for managing LLM traffic in production environments.
Conclusion
LiteLLM is a great starting point for developers who want a simple way to integrate multiple LLMs, but as projects grow, infrastructure needs become more complex. Whether it’s better observability, production-level routing, or tighter control over traffic and usage, alternatives like TrueFoundry, Helicone, Portkey, Eden AI, and Kong AI offer more tailored solutions for scaling GenAI applications. The right choice depends on your goals—whether you're optimizing for flexibility, reliability, or enterprise-grade security. As the GenAI ecosystem matures, it's worth evaluating platforms that align with how you build, monitor, and grow your LLM-powered products.
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- © 2022 ENSEMBLE Technologies
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