MLflow vs Portkey
On the evidence we track, Portkey leads this comparison with a composite score of 79/100. Scores are only directly comparable because these tools share a category; the full breakdown and every source is below.
Capabilities
Feature-by-feature on the axes that matter for mlops & llmops tools. “-” means undocumented, not absent.
What each one is
The product in its own terms, so the numbers below have context.
MLflow
A comprehensive, open-source platform that provides experiment tracking, model registry, LLM tracing, prompt management, and deployment capabilities across the complete machine learning and AI lifecycle
Portkey
LeaderPortkey is an AI gateway platform that routes to 1,600+ language models while incorporating 50+ built-in content safety guardrails. It provides SDKs, CLI tools, and infrastructure components for deploying and managing generative AI applications.
Pricing
List pricing as published by each vendor, with the date we read it. Always verify at the source before you buy.
Portkey
LeaderFree tier with 10k logs/month. Production at $49/month with usage overages. Enterprise custom pricing for compliance-focused organizations.
- DeveloperFree
- Universal API & Key Management
- Routing, Fallbacks, Load Balancing, Retries
- 3 Prompt Templates with Playground, Versioning, Variables
- Logs, Traces, Feedback, Custom Metadata, Filters
- Simple Caching
- +3 more
- Production$49/month + $9 per 100k request overage
- Universal API, Fallbacks, Load Balancing, Retries
- Unlimited Prompt Templates with Playground, API Deployment, Versioning, Variables
- Logs, Traces, Feedback, Metadata, Filters, Alerts
- LLM & Partner Guardrails
- Role-Based Access Control, Service Account API Keys
- +3 more
- EnterpriseContact sales
- Custom retention periods for logs and metrics
- Advanced Evaluation Templates with custom guardrail hooks
- Role-Based Access Control, SSO (Okta), Granular Budget & Rate Limits
- Private Cloud Deployment, VPC Hosting, Data Export to Data Lakes
- SOC2 Type 2, GDPR, HIPAA compliance certifications
- +3 more
Platform & deployment
Where each product runs and how it can be hosted. A dash means undocumented, not unsupported.
Integrations
What each product connects to. Counts come from the vendor's own integration directory where one exists.
- OpenAI
- Claude Code
- Anthropic
MLflow
- Databricks
- AWS S3
- Google Cloud Storage
- Azure Storage
- AzureML
- Kubernetes
- LangChain
- Pydantic AI
- Anthropic Claude
- Google Gemini
- SAP AI Core
- JFrog
- Aliyun
- PostgreSQL
- MySQL
- MSSQL
- OpenAI Codex
- Gemini
- Ollama
- OpenClaw
- Qwen Code
- LiteLLM
- Amazon S3
- OpenTelemetry/OTLP
- +1 more
Portkey
Leader- Google Cloud (Vertex AI, Gemini)
- Azure
- AWS
- GCP
- Langgraph
- CrewAI
- Strands Agents
- Google ADK
- Claude
- Mistral
- MongoDB
- Vertex AI
MLflow or Portkey: which one depends on you
A composite score cannot know your constraints. Describe them and both get re-weighted against what you actually need, with the evidence behind every position.
Free to run, no account needed to start. How the evaluation works
Comparison generated from independently-sourced facts. Every value links to its source and retrieval date. See the method.