Best Portkey Alternative in 2026

Portkey works well for teams in early production stages. Its breadth of model support, config-based routing, and managed observability lower the barrier to multi-provider AI integration. But as organizations scale, several architectural and commercial constraints become blockers.

Log-Based Pricing Creates Cost Unpredictability

Portkey's pricing model charges based on recorded logs rather than flat infrastructure costs. The Pro tier caps at 3 million logs per month, and exceeding that threshold means logs simply stop being recorded — critical observability data is silently lost during the highest-traffic periods when visibility matters most. Enterprise plans that unlock higher limits start at $5,000–$10,000 per month, a steep jump for teams that need comprehensive logging without artificial caps.

For high-throughput AI applications processing thousands of requests per second, this pricing model introduces cost unpredictability that makes infrastructure budgeting difficult.

30-Day Log Retention Falls Short for Regulated Industries

Portkey's Pro tier retains logs for only 30 days before automatic deletion. While this may suffice for early-stage applications, it creates compliance gaps for teams operating in regulated environments. Financial services under SOX require 7+ years for transaction-related logs, healthcare applications under HIPAA mandate 6+ years for medical records, and government contracts typically require a minimum of 3 years. Extended retention is available only on Enterprise plans at premium pricing, gating a fundamental compliance requirement behind the highest pricing tier.

Performance Overhead at Scale

Independent benchmarks reveal meaningful performance differences between AI gateways. Kong's published benchmark data shows that Portkey exhibited 65% higher latency compared to Kong's gateway under equivalent conditions. For production systems where gateway overhead compounds across chained agent calls, tool invocations, and multi-step reasoning workflows, this latency differential directly impacts user experience and infrastructure costs.

Limited MCP Gateway Support

As agentic AI workflows become mainstream in 2026, the Model Context Protocol (MCP) has emerged as critical infrastructure for enabling AI agents to use external tools, access databases, and interact with third-party systems safely. Portkey's MCP support remains limited — teams building agentic applications that require centralized tool governance, authentication management, and policy enforcement at the MCP layer need to supplement Portkey with additional tooling, creating workflow fragmentation.

Isolated Gateway Without End-to-End Platform Integration

Portkey operates as a standalone gateway and LLMOps layer. Teams that need pre-release testing, simulation across user personas, automated evaluation pipelines, and production quality monitoring must stitch together separate tools for each capability. This fragmentation creates data silos — gateway cost data lives in one platform while quality metrics, evaluation results, and experimentation data live in others. Correlating cost with output quality requires manual effort across disconnected systems.

Why Bifrost by Maxim AI Is the Best Portkey Alternative

Bifrost is an open-source, high-performance AI gateway built in Go by Maxim AI. It addresses every production limitation outlined above while delivering measurably superior performance, unlimited observability, and native integration with Maxim's end-to-end AI quality platform.

50x Faster Performance with Sub-Microsecond Overhead

Bifrost adds less than 11 microseconds of overhead per request at sustained 5,000 RPS, a 50x performance advantage over Python-based alternatives. Built in Go and optimized for concurrency, Bifrost treats performance as a first-class architectural concern rather than an afterthought.

For agentic AI workflows where a single user interaction can trigger 5–10 sequential LLM calls with tool invocations between each step, gateway overhead compounds multiplicatively. Bifrost's sub-100µs overhead ensures the gateway never becomes the bottleneck in multi-step reasoning chains.

Unlimited Logging with Native Observability

Unlike Portkey's log-based pricing that caps visibility at plan thresholds, Bifrost provides unlimited logging with native Prometheus metrics, distributed tracing, and OpenTelemetry support out of the box. Teams never lose visibility during peak traffic, and because Bifrost is self-hosted, log retention is controlled entirely by the team's own infrastructure, there are no artificial retention limits or premium tiers gating compliance requirements.

Full MCP Gateway for Agentic AI

Bifrost includes a built-in MCP gateway that centralizes all Model Context Protocol tool connections with governance, security, and authentication controls. Teams can manage tool access policies, enforce authorization at the gateway layer, and maintain a unified audit trail across all agent-tool interactions. For organizations building production agentic systems that need centralized policy enforcement over tool usage, this is a critical capability that Portkey currently lacks.

Self-Hosted Deployment in Under 60 Seconds

Bifrost deploys within your own infrastructure with zero configuration:

npx -y @maximhq/bifrost

Or via Docker:

docker run -p 8080:8080 maximhq/bifrost

All prompts and responses stay within your controlled environment. For teams operating under GDPR, HIPAA, SOX, or internal data residency policies, self-hosted deployment eliminates the compliance questions that any third-party managed proxy introduces. Portkey offers self-hosting options, but Bifrost's zero-config approach gets teams operational in seconds rather than requiring extensive setup.

Hierarchical Budget Management

Bifrost provides cascading budget controls at the organization, team, project, and virtual key level. Teams can set spending limits like $10K at the org level, $2K per team, and $500 per individual key — with real-time tracking of both token usage and dollar spend across all providers. This hierarchical approach to cost governance is essential for large organizations where multiple teams consume LLM resources independently.

Semantic Caching for Intelligent Cost Reduction

Where traditional caching relies on exact string matching, Bifrost's semantic caching understands when different queries carry similar meaning and returns cached responses accordingly. This approach achieves significantly higher cache hit rates — teams report 30–50% cost reductions compared to 15–20% with exact-match caching. For applications with repetitive query patterns like customer support, compliance workflows, and knowledge retrieval, semantic caching delivers material savings.

Drop-In Replacement with Zero Code Changes

Bifrost supports drop-in SDK replacement for OpenAI, Anthropic, Google GenAI, LangChain, and LiteLLM clients. Migration from Portkey requires changing a single line (the base URL) with no application logic changes:

# Before (Portkey)
- base_url = "<https://api.portkey.ai/v1>"
# After (Bifrost)
+ base_url = "<http://localhost:8080/v1>"

This migration path means teams can evaluate Bifrost in parallel with existing Portkey deployments before committing to a full switch.

Bifrost + Maxim: The Full-Stack Advantage

The most significant differentiator is not Bifrost in isolation, it is Bifrost as part of Maxim AI's end-to-end platform. While Portkey operates as a standalone gateway, Bifrost connects natively to Maxim's simulation, evaluation, and observability infrastructure.

• Pre-release simulation: Test agents across hundreds of scenarios and user personas before deployment, measuring quality with configurable evaluators at the session, trace, or span level.
• Automated evaluation pipelines: Run bulk evaluations using off-the-shelf or custom evaluators (deterministic, statistical, and LLM-as-a-judge) to quantify improvements or regressions across prompt versions.
• Production observability: Monitor real-time production logs and run them through periodic quality checks with automated alerts, so teams catch degradation before users are impacted.
• Unified cost and quality correlation: Gateway-level cost data from Bifrost flows directly into Maxim's dashboards, allowing teams to see whether cost optimizations (like model switching or caching) affect output quality, all without switching between tools.

This full-stack integration eliminates the workflow fragmentation that Portkey users face when stitching together separate tools for gateway management, testing, evaluation, and production monitoring.

See more: Maxim AI Platform | Agent Observability | Experimentation

Bifrost vs Portkey: Feature Comparison

Who Should Switch from Portkey to Bifrost

Bifrost is the right choice for teams that have outgrown Portkey's managed model and need production infrastructure they can fully control, monitor, and govern:

• Enterprise teams with compliance requirements that cannot accept 30-day log retention or plan-based log caps in regulated environments
• Platform engineers building internal AI infrastructure who need self-hosted, high-performance gateway capabilities without per-log pricing
• Teams building agentic AI systems that require centralized MCP governance, tool authentication, and policy enforcement at the gateway layer
• Cost-conscious teams at scale processing high request volumes who need semantic caching and zero-markup provider pricing
• AI engineering teams using Maxim for evaluation and observability who want a unified infrastructure layer connecting gateway metrics to quality monitoring

Get Started with Bifrost

Bifrost is open-source under Apache 2.0 and production-ready today. Start in 30 seconds:

npx -y @maximhq/bifrost

Explore the GitHub repository, read the documentation, or book a demo to see how Bifrost and Maxim AI work together for end-to-end AI quality management. Ready to get started?