If you're building with large language models, you already know the challenge isn’t just about calling an API. It’s about managing performance, routing across providers, optimizing costs, and making sure your application remains reliable at scale. As LLM usage grows, teams need infrastructure that not only connects to models like GPT-4 or Claude but also adds transparency, control, and flexibility to how those models are used. That’s where tools like Portkey come into play.
Portkey acts as a control layer between your application and multiple LLM providers. It helps developers route requests, track token usage, handle timeouts, and monitor latency, all while offering features like caching, retries, and observability. For many teams, it’s a plug-and-play way to bring stability and insight into their GenAI workflows.
But as more products go multi-model or shift toward complex orchestration, prompt experimentation, or fine-grained analytics, it’s fair to ask, is Portkey the best fit for every use case?
What is Portkey?
Portkey is an infrastructure platform designed to help developers build and scale AI applications using large language models. At its core, Portkey acts as a middleware layer between your app and various LLM providers, like OpenAI, Anthropic, or Mistral, giving you better control, observability, and flexibility when making API calls to these models.
If you’ve ever tried to integrate multiple LLMs into a single application, you’ve likely run into challenges like handling provider-specific rate limits, managing latency spikes, or switching between providers for cost or performance reasons. Portkey was built to solve exactly those problems.
Portkey offers LLM routing, which means you can route user requests to the best-performing or most cost-effective model provider based on your logic. It also includes features like retry logic, caching, failover, timeouts, and fallbacks, so your application stays reliable even when a provider is experiencing downtime or latency issues.
Another key advantage is observability. Portkey gives developers detailed visibility into every single LLM call, tracking latency, token usage, cost, and model behavior. This is critical when you’re optimizing usage or trying to debug strange output from a model. It also supports prompt management, letting teams version, test, and evolve prompts without constantly redeploying code.
And yes, it’s developer-friendly. Portkey offers SDKs and APIs that are easy to integrate, so teams can plug it into their stack without overhauling their architecture.
In short, Portkey is like a smart control center for your LLM-powered app. It’s especially useful if you’re working with multiple models or providers and want a clean way to manage complexity while improving reliability and speed.
But as with any tool, it’s not the only option, and it might not fit every use case. In the next section, we’ll look at how Portkey works and then dive into why you might want to explore alternatives.
How Does Portkey Work?
Portkey works as a middleware platform that sits between your application and one or more large language model (LLM) providers like OpenAI, Anthropic, or Mistral. Instead of sending requests directly to an LLM API, your application communicates with Portkey. From there, Portkey takes care of routing, failover, observability, and more without requiring you to rewrite your core logic.
At the heart of Portkey is its LLM routing engine. This lets you create custom logic to decide where each request goes. For example, you might send critical user flows to GPT-4 for quality while routing background tasks to a more affordable model like Claude Instant. Routing can be based on cost, speed, model performance, or even fallback logic. This gives you the flexibility to optimize both quality and cost without embedding provider-specific code into your application.
Portkey also improves reliability by managing low-level failure handling behind the scenes. You don’t need to manually code for retries, timeouts, or fallback behavior. Instead, Portkey handles it automatically. If a provider fails or times out, it can retry with the same provider or route the request to an alternative.
One of the most practical features Portkey offers is caching. If the same input is sent repeatedly, Portkey can return a stored response instead of making another API call. This helps reduce latency, save tokens, and cut unnecessary costs.
Another core advantage is observability. Portkey gives you detailed visibility into every LLM request, including:
- Response time and latency
- Token usage per call
- Total cost per provider or prompt
- Success/failure rates
- Model performance comparisons
This data helps teams monitor behavior in real time and troubleshoot issues faster.
Portkey also supports prompt versioning, which is especially useful for teams that regularly experiment with prompt design. You can version and track prompts independently of your application code, making it easier to test and optimize performance without constant redeployments.
Integration is straightforward. Portkey provides REST APIs and SDKs in popular languages like Python and JavaScript. You simply change your request endpoint to Portkey, configure your routing logic, and you’re good to go.
Why Explore Portkey Alternatives?
Portkey is a reliable tool for managing LLM traffic, routing, and observability, but it isn’t always the best fit for every workflow. As teams scale and LLM use cases become more complex, some developers need more flexibility, deeper observability, or support for hybrid cloud deployments. Others might want better prompt versioning, more open infrastructure, or closer integration with their existing MLOps stack.
Exploring alternatives can unlock different strengths, whether you're optimizing for cost, speed, transparency, or long-term control over your AI infrastructure. Some tools offer stronger analytics, some are more developer-friendly, and others are designed with enterprise-scale workloads in mind.
Top 5 Portkey alternatives in 2025
- TrueFoundry
- Helicone
- LangFuse
- Vertex AI
- LLMonitor
Each of these brings something different to the table. We’ll explore what makes them great and when you might want to choose them over Portkey.
1. TrueFoundry
TrueFoundry is a full-stack, developer-first AI infrastructure platform that includes a powerful LLM Gateway designed to help teams build, deploy, and manage GenAI applications across open and closed-source models. It acts as a centralized layer for routing, observability, version control, and deployment of LLMs, offering everything Portkey does but with significantly more flexibility and control.
At the core of TrueFoundry is its LLM Gateway, which provides a unified API layer to interact with over 100+ LLMs from providers like OpenAI, Anthropic, Mistral and open-source models like LLaMA and Falcon. Teams can route traffic intelligently, enforce rate limits, cache responses, log requests, and track costs, all from one interface. It’s like having the best parts of Portkey but combined with the ability to self-host, fine-tune, and deploy models on your own infrastructure if needed.
TrueFoundry runs on your Kubernetes cluster, so you retain full data ownership, minimize latency, and avoid egress costs. It’s built to support both experimentation and production workloads, with seamless integrations across your software and MLOps stack.
Top Features:
- Unified AI Gateway to manage, route, and log across 100+ LLMs
- Fine-tune and deploy open-source LLMs with autoscaling and custom endpoints
- Full observability: latency, token usage, cost, and provider performance
- Prompt versioning, rollback, and multi-environment model promotion
- Self-hostable, cloud-agnostic, and no vendor lock-in (you get all Kubernetes manifests)
How TrueFoundry is Better Than Portkey:
While Portkey focuses on routing closed LLM APIs, TrueFoundry provides a production-grade AI Gateway that combines routing, caching, prompt management, and observability with full deployment control. You’re not limited to calling external APIs, you can fine-tune models, deploy them as scalable APIs, and manage everything in your environment.
TrueFoundry also supports agent workflows, RAG pipelines, and real-time inference, making it ideal for companies scaling serious GenAI products. And with complete control over infrastructure, model selection, and data privacy, it’s built to grow with your stack, not constrain it.
2. Helicone
Helicone is an open-source observability layer designed to help developers monitor and understand how their applications interact with large language models. It acts as a lightweight proxy between your app and LLM providers like OpenAI and Anthropic, capturing detailed logs of each request and response. For teams that need transparency and insight into prompt behavior, Helicone offers a focused, no-fuss solution.
Getting started is simple. You route your LLM API calls through Helicone’s endpoint instead of directly to the provider, and it automatically logs prompt inputs, responses, latency, token usage, and estimated costs. The visual dashboard makes it easy to debug slow requests, spot anomalies, or analyze how prompts perform over time.
It doesn’t try to do everything—there’s no routing or caching logic like you’d find in Portkey, but it does observability extremely well. That makes it a good fit for developers who already have their infrastructure in place but want more clarity into how their LLMs are behaving in production.
Top Features:
- Real-time logging of prompts, responses, and metadata
- Dashboards for latency, usage, and token cost tracking
- Response diffing and debugging tools
- Support for OpenAI, Anthropic, and other providers
- Self-hostable and open source, with privacy-first architecture
How Helicone Compares to Portkey:
Helicone doesn’t aim to replace Portkey’s routing or reliability logic. Instead, it focuses entirely on observability, offering a cleaner and often more detailed view into your LLM activity. If you're mainly looking for insight, debugging, and transparency, Helicone can be a strong companion or alternative to Portkey’s logging features.
It’s ideal for teams that want to keep their infrastructure simple but still need visibility into how LLMs are performing across different prompts and users. While Portkey combines observability with control, Helicone focuses on visibility alone and does it with developer-friendly ease.
3. LangFuse
LangFuse is an open-source platform built for observing, evaluating, and improving LLM-based applications. It gives developers detailed visibility into how prompts are performing, how users interact with outputs, and where optimization opportunities exist. While it doesn’t focus on routing or fallback handling like Portkey, it fills a different need: making LLM apps smarter through better analytics and feedback loops.
At its core, LangFuse captures traces of each LLM call, including prompt inputs, model responses, user feedback, latency, and success rates. These traces can be visualized and filtered in its dashboard, helping teams understand not just what the model did but how well it aligned with user expectations or business goals.
LangFuse is especially useful for teams running A/B tests, prompt experiments, or building feedback-driven pipelines. It can also integrate with RAG pipelines and agent-based systems, where prompt complexity and flow matter just as much as model choice.
Top Features:
- Trace logging with full input/output context
- A/B testing and evaluation tools for prompt performance
- User feedback capture and quality scoring
- Integration with LangChain, OpenAI, Anthropic, and other providers
- Open source, self-hostable, and lightweight to deploy
How LangFuse Compares to Portkey:
LangFuse and Portkey serve different layers of the LLM stack. Portkey focuses on managing requests, routing, caching, and ensuring reliability. LangFuse focuses on evaluating what those requests actually produce and how well the output serves your product or user.
If you're running experiments, refining prompt quality, or trying to track user feedback to improve your LLM app’s effectiveness, LangFuse is a solid alternative to Portkey’s observability features. It’s not a control plane, but an insight layer, giving teams the data they need to iterate faster.
For teams prioritizing feedback, quality tuning, and analytics over routing logic, LangFuse is a strong, open-source option that complements or substitutes Portkey in a focused way.
4. Vertex AI
Vertex AI is Google Cloud’s fully managed machine learning and GenAI platform that brings together a suite of tools for building, deploying, and managing AI models at scale. It includes everything from model training and pipeline orchestration to prompt tuning and foundation model APIs. For organizations already invested in Google Cloud, Vertex AI can be a natural extension of their infrastructure when working with large language models.
Unlike Portkey, which focuses on LLM routing and observability, Vertex AI offers a broader platform with deep integration into the GCP ecosystem. It supports model tuning using Google’s foundation models (like PaLM), prompt management, and model evaluation. You also get centralized monitoring, security controls, and full access to other GCP services like BigQuery and Dataflow, making it appealing for enterprise teams building production-grade GenAI systems.
While Vertex AI may be more heavyweight than Portkey, it suits organizations looking for enterprise-scale orchestration and built-in model access under one roof.
Top Features:
- Access to Google’s foundation models (e.g., PaLM) with prompt tuning and evaluation
- Managed APIs for model serving, training, and batch inference
- Integration with BigQuery, Looker, and the broader GCP stack
- Model monitoring and explainability tools
- Role-based access, version control, and enterprise security
How Vertex AI Compares to Portkey:
Portkey is designed for fast, flexible LLM routing across multiple providers, while Vertex AI focuses on deep cloud-native AI integration. If your stack already runs on Google Cloud and you need prompt management, training capabilities, and access to proprietary models from Google, Vertex AI may serve as a broader but valid alternative.
It doesn’t offer provider-agnostic routing like Portkey or TrueFoundry, and it’s more opinionated in terms of tooling. However, for enterprise teams prioritizing governance, security, and vertical integration with Google tools, Vertex AI can replace Portkey in a more managed, cloud-native setup.
It’s best suited for larger orgs building AI workflows, not just API orchestration, but full-stack GenAI products.
5. LLMonitor (Lunary.ai)
LLMonitor is a lightweight, developer-friendly observability tool designed specifically for LLM-based applications. It focuses on giving you clear visibility into how your prompts perform in the real world, with minimal setup and strong support for privacy and security. While it doesn’t handle routing or model selection like Portkey, it offers a clean and reliable solution for teams that want to monitor, debug, and analyze LLM interactions in production environments.
With LLMonitor, you can log each request and response, track performance metrics, and view trends over time. It captures inputs, outputs, latency, token usage, and errors, helping developers trace problems and improve prompt quality. It also supports user-level insights, making it easier to identify bottlenecks or failure points in your GenAI-powered features.
LLMonitor is particularly helpful for small to mid-sized teams building LLM apps who don’t need a full control layer but want transparency, simplicity, and ownership over their logs.
Top Features:
- Logs every LLM call with input, output, latency, and errors
- Visual dashboards to monitor trends and usage
- Lightweight SDKs with easy integration for Python and JavaScript
- Supports multiple providers, including OpenAI and Anthropic
- Can be self-hosted for full data control and privacy
How LLMonitor Compares to Portkey:
LLMonitor is more focused than Portkey. While Portkey combines routing, retries, and observability into one platform, LLMonitor sticks to its core mission: tracking and analyzing LLM usage. It’s ideal if you already have your routing or gateway solution in place and need something to give you clarity into how your prompts are performing.
It doesn’t offer advanced routing, fallback logic, or caching, but for teams that value simplicity, speed, and clear insight, LLMonitor is a clean alternative. It’s often used alongside other tools or as a logging layer within custom LLM stacks.
If Portkey helps you control traffic, LLMonitor helps you understand the quality of that traffic and improve your application accordingly.
Conclusion
As GenAI applications grow more complex, so do the infrastructure demands behind them. Portkey offers a solid starting point for LLM routing and observability, but it may not meet every team’s long-term needs. For those looking for more flexibility and deeper control, TrueFoundry stands out as a powerful AI gateway that supports open-source LLM deployment, prompt versioning, cost tracking, and full-stack observability. Other tools like Helicone, LangFuse, Vertex AI, and LLMonitor also serve as strong alternatives based on specific needs. The right choice comes down to your stack, scale, and how fast you plan to grow.
