Sold by: Portkey AI
Control Panel for AI Apps
4.5
Overview
Scaling LLM apps in production is hard. You need reliability, security & accuracy and a bunch of DevOps work (Fallbacks, A/B testing, Load Balancing b/w models, etc) to get confidence on production.
Portkey is your AI control panel, bringing full visibility and control over your AI apps. Integrating Portkey's AI Gateway is a 2 line code upgrade. Bringing in full observability, routing control, guardrails, prompt management and security policies, all managed through a single pane of glass.
Portkey is enterprise-ready and is ISO, SOC2, HIPAA & GDPR compliant.
Highlights
- AI Gateway & Observability
- Prompt Management & Security
- Guardrails & Fine-tuning
Details
New
Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on the duration and terms of your contract with the vendor. This entitles you to a specified quantity of use for the contract duration. If you choose not to renew or replace your contract before it ends, access to these entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/12 months |
|---|---|---|
PortkeyEnterprise Access | Enterprise Plan | $99,999.00 |
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Custom.
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Delivery details
Software as a Service (SaaS)
SaaS delivers cloud-based software applications directly to customers over the internet. You can access these applications through a subscription model. You will pay recurring monthly usage fees through your AWS bill, while AWS handles deployment and infrastructure management, ensuring scalability, reliability, and seamless integration with other AWS services.
Resources
Vendor resources
Support
Vendor support
Custom Enterprise Support.
support@portkey.ai
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Updated weekly
By Portkey AI
By Lunary AI
By Karini AI
Sentiment is AI generated from actual customer reviews on AWS and G2
Functionality
Ease of use
Customer service
Cost effectiveness
Positive reviews
Mixed reviews
Negative reviews
AI generated from product descriptions
AI Gateway and Request Routing
AI Gateway with load balancing capabilities between multiple language models and fallback mechanisms for production reliability
Observability and Monitoring
Full visibility and observability over AI applications with comprehensive logging and monitoring through a centralized dashboard
Prompt Management
Centralized prompt management system for versioning, organization, and control of prompts across AI applications
Security and Compliance
Enterprise-grade security with ISO, SOC2, HIPAA, and GDPR compliance certifications and security policy enforcement
Guardrails and A/B Testing
Built-in guardrails for output validation and A/B testing capabilities for comparing model performance and behavior
Observability and Monitoring
Track metrics including cost, token usage, and latency with detailed traces for logging and debugging LLM calls and agents.
Prompt Management and Versioning
Collaborate on prompts with versioning capabilities and perform A/B testing to improve LLM prompt performance.
Evaluation and Testing Integration
Test prompts and models for optimal performance with evaluation suite integration into CI pipeline.
Content Safety and Guardrails
Implement guardrails to prevent toxic content, detect prompt injections, and prevent PII data leakage in AI responses.
Security and Compliance Features
Support for on-premises deployment and GDPR-compliant cloud hosting with PII removal and anomaly detection capabilities.
No-Code Application Development
Visual interface with built-in connectors and large language models enabling generative AI application deployment without coding requirements.
Multi-Model Support and Comparison
Access to latest large language models with prompt playground functionality for model comparison and evaluation across different LLM options.
Enterprise Security and Governance
Secure credentials management, personally identifiable information masking, data encryption, and role-based access controls for enterprise-level compliance.
Observability and Cost Management
Operational dashboards providing visibility into model spending, performance metrics, usage patterns, and trends for cost tracking and optimization.
Trust and Safety Controls
Content filtering mechanisms to reduce noise, block harmful content, and include relevant citations with ground truth comparison capabilities using LLM as a judge.
Standard contract
Customer reviews
Support Pan
Prompt testing has become structured and flexible but trial access still needs improvement
Reviewed on May 31, 2026
Review provided by PeerSpot
What is our primary use case?
Our team is searching for an alternative to LiteLLM, which is another monitoring management platform, and we found that Portkey is an alternative solution that we decided to try out.
We want to conduct prompt template testing with Portkey by splitting our business logic along with the prompt, so we need to test that against different models and perform evaluation accordingly. This is one major thing we are trying. The product owner can separately conduct their test for the prompt template on the playground.
There could be some advanced usage with Portkey such as setting up the MCP and guard rail. We are thinking about having that part as well, but so far we haven't touched it yet.
What is most valuable?
Portkey provides the standard interfaces for the major features that interested the industry has. It has a very comprehensive feature support for the MCP agent and its playground, support for prompt templates, and allows the management of traceability analytics. It covers most agentic application usage very comprehensively.
Portkey definitely provides a solid alternative solution for the agent and large model hosting platforms, and it is very helpful for us to explore the possibilities across the industry rather than staying with a few mainstream options.
Portkey successfully provides a standard interface and large model based solutions, and we can see those features in many other platforms as well. This is solid, but so far I haven't found anything super exciting or very innovative on this platform. For our organization, it's a great alternative solution to consider.
What needs improvement?
One major problem I see with Portkey is that when I had not yet started any trial, it started to denote that I had exceeded the prompt limit. Users are usually expecting a trial stage with more tokens to at least work through a few days to understand the products inside out. That might be something that could be improved.
The onboarding process of Portkey is very good. However, it requests that there are no free tokens or trial tokens. That was the limitation.
For how long have I used the solution?
I have been using Portkey for a few weeks.
What do I think about the stability of the solution?
When I was doing a test with Portkey, it was stable enough.
What do I think about the scalability of the solution?
We have not reached the point for considering scalability with Portkey.
How are customer service and support?
We do not have a customer relationship with Portkey.
Which solution did I use previously and why did I switch?
We were using LiteLLM and sometimes Langfuse. Langfuse is also open source, so we can deploy that to cover many such features. We used them and felt that their price was a little bit higher than our budget. This was the reason why we were seeking alternative solutions and eventually found Portkey.
How was the initial setup?
I did not want to quickly dive into too much advanced features. Portkey provides a very quick hands-on experience.
What's my experience with pricing, setup cost, and licensing?
Portkey is requiring production to be $49, I guess that is US dollars per month for 100K logs. I would say it is manageable.
What other advice do I have?
It is definitely a private cloud for Portkey from their provider. Using their online services with Portkey is good. If you are seeking an alternative solution for the product, Portkey is definitely worth a look. My overall review rating for this product is 7.
Computer Software
Portkey Brings Observability, Control, and Cost Clarity to LLMOps
Reviewed on Mar 26, 2026
Review provided by G2
What do you like best about the product?
What I like best about Portkey is that it brings structure to what is otherwise a very chaotic part of building AI products. When you're working with multiple LLMs, APIs, and edge cases, things break silently—and debugging becomes painful. Portkey acts as a unified gateway that gives you visibility, control, and reliability out of the box.
The biggest win for me is observability + control. Having centralized logs, request tracking, cost insights, and performance metrics in one place makes a huge difference. Instead of guessing what went wrong, I can actually see how prompts behave, where latency spikes happen, and how much each request costs.
It also simplifies multi-model integration. Rather than managing different APIs and retry logic across providers, everything runs through a single layer with built-in fallbacks, routing, and caching. That alone removes a lot of engineering overhead and lets me focus more on building features instead of infra.
Another big plus is cost optimization. Features like caching, usage tracking, and model routing help avoid unnecessary LLM calls and keep spend predictable, which is critical when scaling.
The biggest win for me is observability + control. Having centralized logs, request tracking, cost insights, and performance metrics in one place makes a huge difference. Instead of guessing what went wrong, I can actually see how prompts behave, where latency spikes happen, and how much each request costs.
It also simplifies multi-model integration. Rather than managing different APIs and retry logic across providers, everything runs through a single layer with built-in fallbacks, routing, and caching. That alone removes a lot of engineering overhead and lets me focus more on building features instead of infra.
Another big plus is cost optimization. Features like caching, usage tracking, and model routing help avoid unnecessary LLM calls and keep spend predictable, which is critical when scaling.
What do you dislike about the product?
What I dislike is that the platform can feel a bit complex initially. There’s a learning curve, especially if you’re new to LLMOps, and some areas like advanced analytics and documentation could be more polished.
What problems is the product solving and how is that benefiting you?
Portkey solves the problem of managing and scaling LLM integrations reliably. When building AI products, handling multiple model providers, debugging failures, tracking costs, and maintaining performance quickly becomes messy and time-consuming.
The biggest issue it addresses is lack of observability. Without a proper layer, it’s hard to understand how prompts are performing, where latency is coming from, or why certain responses fail. Portkey gives structured logs, request tracing, and metrics, which makes debugging and optimization much faster.
It also solves fragmentation across LLM providers. Instead of writing custom logic for each API, retries, and fallbacks, Portkey provides a unified gateway. This reduces engineering overhead and makes it easier to switch or combine models without rewriting core logic.
Another major problem is cost unpredictability. With usage tracking, caching, and smarter routing, Portkey helps control and optimize LLM spend, which becomes critical as usage scales.
The biggest issue it addresses is lack of observability. Without a proper layer, it’s hard to understand how prompts are performing, where latency is coming from, or why certain responses fail. Portkey gives structured logs, request tracing, and metrics, which makes debugging and optimization much faster.
It also solves fragmentation across LLM providers. Instead of writing custom logic for each API, retries, and fallbacks, Portkey provides a unified gateway. This reduces engineering overhead and makes it easier to switch or combine models without rewriting core logic.
Another major problem is cost unpredictability. With usage tracking, caching, and smarter routing, Portkey helps control and optimize LLM spend, which becomes critical as usage scales.
Marketing and Advertising
Best way to manage and scale LLM apps without losing control
Reviewed on Mar 19, 2026
Review provided by G2
What do you like best about the product?
We started using Portkey when our AI usage began getting messy across multiple models and providers. What stood out immediately was how it brings everything into one place without adding complexity.
The biggest win for us has been visibility. You can actually see what’s happening with every request — cost, latency, failures — which makes debugging and optimization way faster. The observability layer alone is worth it, especially when you’re running multiple use cases in production.
The biggest win for us has been visibility. You can actually see what’s happening with every request — cost, latency, failures — which makes debugging and optimization way faster. The observability layer alone is worth it, especially when you’re running multiple use cases in production.
What do you dislike about the product?
The product is evolving quickly, which is great, but it also means some advanced analytics and customization options are still catching up. The UI can improve in a few areas.
What problems is the product solving and how is that benefiting you?
Another thing I really like is how easy it is to switch between models or add fallbacks without rewriting core logic. The unified API and routing save a lot of engineering time, and it removes the usual vendor lock-in headache.
Prompt management and guardrails are also well thought out. It’s not just a gateway — it actually helps you run AI systems more reliably with things like caching, retries, and policy controls built in.
Setup is straightforward, and the documentation is clear enough that you can get something running quickly.
Prompt management and guardrails are also well thought out. It’s not just a gateway — it actually helps you run AI systems more reliably with things like caching, retries, and policy controls built in.
Setup is straightforward, and the documentation is clear enough that you can get something running quickly.
Rakshit A.
Great Dashboard and Analytics, Easy Integration
Reviewed on Nov 11, 2025
Review provided by G2
What do you like best about the product?
The standout feature is the dashboard and analytics, which make observability and monitoring straightforward. Implementation is simple thanks to the available APIs, and there is a wide selection of LLM models and providers. It integrates smoothly into daily monitoring and troubleshooting routines.
What do you dislike about the product?
The documentation falls short and often requires users to figure things out on their own. Additionally, price tracking does not work universally across all models, and for air-gapped setups, pricing updates must be done manually, which can lead to delays in getting accurate information.
What problems is the product solving and how is that benefiting you?
This tool has been instrumental for my DevOps teams, providing deep observability and effective cost controls. It also strengthens reliability through features like routing, retries, and fallbacks, which together make our delivery process faster, more cost-efficient, and predictable for our product teams. Since adopting it, we have experienced quicker release cycles, a reduction in incidents, and more consistent spending across our RAG, ASR, and OCR services running on Kubernetes.
Dharmik V.
Powerful AI Platform with Easy Integration, But Needs Bug Fixes and Better Analytics
Reviewed on Oct 27, 2025
Review provided by G2
What do you like best about the product?
Portkey is a comprehensive platform for gen AI builders os its AI gateway is smooth integration with large language models and its guardrails have a proactive usage with its prompt management and resilience helps to track costs as per use case and save repeated llm calls which has minimum 3-5 usage per day and its implementation is super easy for any beguines and its customer support is too friendly and responses quickly and its integration is super easy and efficient. we usally have a AI integration in large scale and has the guardrails for a limited usage and this software helps me alot.
What do you dislike about the product?
the software has a lot of bugs and its complexcity for newcomers are too high with missing advanced analytics and its GUI documentation must be more flexible.
What problems is the product solving and how is that benefiting you?
it helps me in building and deploying generative AI applications for unified platform and managing models and security for its smooth operative performance.