Portkey is an artificial intelligence (AI) infrastructure provider that has developed an end-to-end production stack for builders of Generative Artificial Intelligence (GenAI), this includes an AI Gateway; a platform for observability, guardrails, governance, and prompt management; all in one location. Portkey provides enterprise AI teams with a centralized place to provide access to their models and tools as well as provides reliability, safety, and compliance to large-scale production AI applications. Founded in 2023, Portkey primarily services developers and companies that are creating production-grade AI applications.
Founded by Ayush Garg and Rohit Agarwal to unify fragmented AI models into a single gateway.
In March 2023, launched its first product, the AI Gateway, which currently supports over 1600 models, and provides both observability and governance capabilities.
Raised a $3 million Seed Venture Capital (VC) round to create production AI infrastructure.
Developed its platform to include MCP Gateway, which allows users to deploy AI agents, tools, and workflows directly on the platform.
Announced a strategic partnership with F5 to provide secure enterprise AI applications using Portkey.
Distributed team with engineering in India, enterprise-grade cloud infrastructure
LLMOps platform with routing optimization, caching, fallback strategies, and production observability for 1600+ models including agents and tool calling
Based on official website and product descriptions
| Service | Cost | Details | Source |
|---|---|---|---|
| Developer | $0 forever | 10K recorded logs/month, no overages allowed, community support, basic features like universal AI gateway, automatic fallbacks, load balancing | — |
| Production | $49/month | 100K recorded logs/month, +$9 per additional 100K logs, 30 days log retention/90 days metrics, prompt management (3 templates), simple caching, deterministic guardrails, AI gateway features. Not for custom security/data residency | Official pricing page |
| Enterprise | Custom pricing | Custom log limits, advanced features, complex compliance needs, dedicated support | Official pricing page |
| Feature | Portkey | LiteLLM | AI Gateway | ResultantAI |
|---|---|---|---|---|
| Core Functionality (AI Gateway) | Yes | Yes | Yes | Yes |
| Usage & Audit Logging | Yes (100K logs/mo) | Basic | Yes | Advanced analytics |
| Automatic Fallbacks/Load Balancing | Yes | Yes | Yes | Intelligent routing |
| Prompt Management | Yes (3 templates Pro) | No | Yes | Yes |
| Free Tier | 10K logs/month | Yes | $5K budget | Yes |
| Pricing Model | Platform fee + usage | Usage-based | Budget-based | All-inclusive |
| Budget Caps | No | No | Yes | Yes |
| Enterprise SSO/Security | Custom | No | Enterprise | Enterprise |
| API Integrations | LangChain/CrewAI | Comprehensive | Standard | Standard |
| Starting Price | $49/mo | Free scaling | $99/mo | $49+tokens |
Portkey Provides Additional Production Ready Features Like Advanced Logging (100K Logs/Month) & Prompt Management vs LiteLLM’s Basic Proxy Functionality. Portkey is Focused on Enterprise Production & LiteLLM is Focused on Developer Simplicity.
Portkey Best for Production Observability & LiteLLM Best for Simple Proxy Needs.
AI Gateway Wins in Predictable Budget Pricing ($99 Includes $5K Usage) with Intelligent Routing While Portkey’s ($49+$0.09/1K Logs) Can Surprise Teams at Scale. Portkey Better Suited for Use by Observability Intensive Teams.
AI Gateway Best for Budget Controlling & Portkey Best for Detailed Logging/Analytics.
Portkey Provides a Full AI Gateway Solution Including Observability, Where as Langfuse is Tracing Only. Portkey May Be a More Comprehensive Solution But Could be More Expensive When Used for Logging Intensive Use Cases.
Portkey Best for Complete Gateway Solutions & Langfuse Best for Lightweight Tracing.
Portkey is a Hosted Solution & Phoenix is Self-Hosted. Portkey Offers Managed Scaling/SLAs; Phoenix is Free But Requires Infrastucture Management.
Portkey Best for Managed Service & Phoenix Best for Cost-Conscious DevOps Teams.
Portkey Is a Production Stack for Builders of GenAI Providing AI Gateway, Observability, Guardrails, Governance, and Prompt Management in One Platform. It Serves as a Unified Interface to 1,600+ LLMs That Allows Teams to Monitor, Optimize, and Scale Their AI Applications Reliably.
Portkey Offers Developers a Free Tier, Pro Plans Starting at Usage-Based Pricing, and Enterprise Custom Pricing. Advanced Guardrails and Dedicated Support Require Paid Tiers. Contact Sales for Exact Quotes.
Portkey is a complete LLMOps Stack, providing Gateway Routing to over 1600 Models; Intelligent Caching for Cost Savings; Production-Ready Agent Support, including Guardrails, Governance & Prompt Management for Enterprise Scale. Unlike pure Observability Tools, Portkey also includes these additional Enterprise-Scale Capabilities.
Yes, Portkey meets all of the typical Enterprise Security Standards (SOC 2 Compliance, Data Encryption, Key Management); Does Not Store Your LLM API Keys; Offers Detailed Access Controls, Audit Logs and More.
Portkey seamlessly Integrates with Langchain, Crew.ai, Autogen, and Other Agent Frameworks; Works as a Drop-In Gateway Compatible with OpenAI, Azure, and 250+ LLM Providers Without Any Code Changes.
Documentation and Community Slack are Available to All Users (Free Tiers); Email Support During Business Hours are Included in the Pro Tier; Priority SLAs, Dedicated Managers are Included in the Enterprise Tier; G2 Reviews Praise Responsive Developer Support.
Yes, Portkey Provides a Generous Free Tier that Allows Developers to Test Both the Gateway and Observability Features; The Free Tier Limits Usage to Test Advanced Features; Pro & Enterprise Tiers Provide Unlimited Production-Scale Usage, Caching, Advanced Guardrails.
The Free Tier Has Limits on the Number of Requests and Limitations on Advanced Features; Custom SLA's in Enterprise Require Sales Contact; High Volume Production May Need Custom Rate Limit Configurations.
Portkey Delivers an Essential, Full-Stack LLMOps Platform for Production AI Applications, Providing Gateway Access to Over 1600 Models, Robust Observability, Cost Optimization, and Governance; Its Full-Stack Approach and Leadership Position in G2 Make it Ideal for Scaling GenAI Reliably; Strong Backing and Fortune 500 Adoption Validate its Readiness for Enterprise Use Cases.
Portkey is the go-to LLMOps platform for all developer teams seeking to deploy and optimize their GenAI applications in a production environment.
Portkey is a leading full-stack LLMOps platform, which includes gateway to 1600 + models, tracks 40+ metrics for observability, uses intelligent caching for cost optimization, and provides production-ready agent support. Used by Fortune 500 companies and top rated on G2, it is accelerating the development of AI applications while providing governance of enterprise applications. Established in 2023, with significant investment backing and rapid validation from the market.
Good - comprehensive information from official website and docs.portkey.ai. Limited details on exact pricing/SLAs require sales contact. Customer quotes from site and G2 confirm strong reception.
Top rated LLMOps platform on G2 by developer teams developing production GenAI applications. Portkey excels in observability, gateway functionality and developer experience. Earned multiple "Momentum Leader" badges.
Trusted by Fortune 500 companies processing 30 million + policies monthly and startups such as QA.tech. Provides critical visibility into multi-LLM application usage, cost tracking per use case and prompt management at scale.
Tracks 2000 + models and processes billions of tokens each day for SME customers. Handles production workload with strict SLA's and real time observability dashboard.
Featured in analyst reports on emerging AI infrastructure. Recognized by industry leaders for establishing LLMOps standards. Funded by prominent AI investors.
Integrations directly with Langchain, Crew.ai and Autogen to make AI agents production-ready. Streamlines development, deployment and tracking of multi-agent workflows.
Logs every single call to the LLMs with all inputs, selected model, output, latency and cost of compute across 250+ models.
Logs all prompts template changes along with version history and who made each change and when they did so.
Tracks creation of API keys along with usage/access patterns of each key across multiple models/providers.
Logs all changes to guardrails such as configuration updates, policy updates, routing logic updates etc.
Audits all permissions changes, role assignment changes, and all changes to workspace level access controls.
Allows you to see the retry/fallback/model route decision process to show you which model was chosen and why.
Records when PII is detected/redacted and any time a guardrail violation occurs along with all context surrounding the event.
All users will be given full attribution for all their actions with complete tracking of user identity throughout all modules and workspaces.
| Framework | Supported | Key Capability | Support Level |
|---|---|---|---|
| EU AI Act | Yes | user ID, timestamp, action, resource, outcome | Full - Audit logs demonstrate compliance for regulated AI operations |
| SOC 2 | Yes | who, what, when, resource, outcome | Full - Generates compliance reports and comprehensive audit trails |
| Internal Compliance Policies | Yes | user attribution, timestamps, resource, action type | Full - Audit logs demonstrate compliance without stitching data from multiple systems |
The use of audit logs is restricted to organization owners and admins with granular permission management; access to the ability to view the audit logs will be based upon role-based access controls for the purpose of governing properly.
Audit logs are automatically collected across all workspaces with a view of the entire organization available to admins while ensuring that each workspace remains isolated from other workspaces.
Fast, flexible filtering options are available for viewing audit log data by specific resource(s), the critical nature of an action, the IP address, etc. Use 15 + filters to create custom views of your audit log data.
Access control management ensures that only authorized users will have the ability to view audit logs across the organization.
Every action taken by a user will be tracked back to the user who performed the action across modules, workspaces, and organizations with a time-stamp associated with the action.
Users will be able to search audit logs using a variety of filter options such as resource type, user, action type, time stamp range, etc. for targeted investigations of audit log data.
A capability to recreate the complete execution path (i.e., "trace") for any request made to the system including which model was used, what fallbacks occurred, why retries were initiated, and what metadata was included in the request will exist.
The capability to export logs for audit trail purposes, internal review, or compliance reporting purposes will exist to demonstrate compliance without having to stitch together data from multiple systems.
Real-time monitoring of usage, errors, caching behavior, feedback, and metadata will exist through a single, unified dashboard displaying over 40 data points per request.
A unified, chronological view of the request lifecycle with detailed tracing of all prompt changes, guardrail updates, and configuration modifications will exist.
Logs will display a cost breakdown per session, latency analysis, and performance metrics to enable identification of problems quickly.
| Integration Type | Target System | Supported | Notes |
|---|---|---|---|
| LLM Providers | 250+ LLMs | true | Support for GPT-4, Claude, and 248+ other models with unified audit logging across all providers |
| LLM Providers | Anthropic Computer Use | true | Comprehensive audit logging with code context, generation metrics, developer attribution, and cost breakdown |
| Export & Integration | Export Logs | true | Export audit logs for audit trails and compliance reporting to external systems |
Which model was selected, what fallbacks occurred, and why retries were triggered will be logged for each AI call.
Logging of all LLM calls will occur with the request context, response generation, latency, and compute costs across 250 + models
To be able to use a prompt template input (and output) as part of an audit trail or to track the behavior of users in the system.
All events related to detecting Personally Identifiable Information (PII), redacting it, and calling a model should be logged prior to making that call. These logs will contain information about every violation of a guardrail and/or policy violation.
The creation of an API key, usage patterns, and the access control associated with the use of the API key are to be tracked so that there is no unauthorized access to the models.
A complete and detailed breakdown of the costs of each session; latency analysis; and performance metrics for every interaction with an AI will be logged.
For each AI operation, a minimum of the following data should be logged by the application: User ID, Route, Model, Risk Level, and Fallback Path Metadata.
An audit trail of all guardrails configurations updated; routing logic changed; and policies modified should be complete.
Any AI request should allow you to reconstruct the entire execution flow which includes the routing decisions made; what fallback was used; and what the outcome of the request was.
The full detail of how requests are routed through the AI Gateway should be visible via detailed observability dashboard views.
