Phoenix vs Portkey: Observability, Prompts & Evals Comparison
Compare Phoenix and Portkey side by side. Both are tools in the Observability, Prompts & Evals category.
Updated March 10, 2026
The short answer
Choose Phoenix if open-source with active development by Arize.
Choose Portkey if enterprise-scale monitoring (10B requests/month).
Quick Comparison
| Category | Observability, Prompts & Evals | Observability, Prompts & Evals |
| Pricing | Open Source | — |
| Best For | Engineering teams building agent and RAG systems who want OpenTelemetry-native observability with both self-hosted and managed options | — |
| Website | phoenix.arize.com | portkey.ai |
| Key Features |
| — |
| Use Cases |
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Pros & Cons: Phoenix vs Portkey
- +Open-source with active development by Arize
- +OpenTelemetry-native (no proprietary trace format lock-in)
- +Strong evaluator library out of the box
- +Both self-hosted and managed cloud options available
- +Upgrade path to full Arize enterprise platform
- −Smaller community than Langfuse for open-source-first teams
- −Cloud version's enterprise pricing is contact-sales only
- −UI and feature set tilt toward ML engineers more than application developers
- +Enterprise-scale monitoring (10B requests/month)
- +Sub-40ms overhead
- +Comprehensive analytics and tracing
- +Strong security certifications
- −Professional tier expensive (USD 2-5k/mo)
- −Open-source has limited features
- −Complex for small teams
Pricing: Phoenix vs Portkey
- · Open-source
- · Basic observability
- · Self-managed
- · Community support
- · Managed service
- · Advanced analytics
- · Standard support
- · Team features
- · Custom limits
- · SOC2, ISO27001
- · Priority support
- · Private deployment
When to Choose Phoenix vs Portkey
- OpenTelemetry-native LLM observability
- Hallucination detection for RAG
- Experiment tracking and golden-set evaluation
About Phoenix
Phoenix is the open-source observability and evaluation platform built by Arize AI for LLM and agent applications. It is OpenTelemetry-native, which means traces written through Phoenix can flow into any OTel-compatible backend in addition to Phoenix's own UI. The platform includes built-in evaluators for hallucination detection, retrieval relevance, and QA correctness, plus dataset management and prompt playground features. Phoenix can be deployed via Docker for self-hosting or used in Arize's managed cloud. The open-source core makes it attractive to teams that want to inspect and customize the observability layer, while the integration with the full Arize platform provides an upgrade path for organizations that need enterprise features like RBAC, SSO, and SLA-backed support.
About Portkey
Portkey Observability is the monitoring and analytics component of the Portkey AI platform, providing comprehensive visibility into LLM applications. The platform tracks requests, costs, latency, errors, and user behavior across all LLM providers. Portkey Observability integrates seamlessly with the Portkey AI Gateway, offering unified monitoring for multi-provider AI applications. The platform provides real-time dashboards, alerting, and detailed trace analysis to help teams optimize AI performance and costs. Portkey processes over 10 billion requests monthly with sub-40ms overhead, providing enterprise-grade observability for production AI systems.
What is Observability, Prompts & Evals?
Tools for monitoring LLM applications in production, managing and versioning prompts, and evaluating model outputs. Includes tracing, logging, cost tracking, prompt engineering platforms, automated evaluation frameworks, and human annotation workflows.
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