Tools
Helicone vs MLflow
Helicone or MLflow? Their plans monthly and yearly, the capabilities their makers state, security and latest updates, side by side — read from the makers' own pages.
In short
- Both: Self-hosted, API, Official SDKs, Traces and evaluates
- Only Helicone states: Choice of models
- Only MLflow states: Command line, Builds agents and workflows
Helicone
Helicone routes, monitors, debugs, and analyzes AI applications through an open-source gateway and observability platform.
Plans
Hobby
Free
- 10,000 free requests
- 1 GB storage
- 1 seat, 1 organization
Pro
- $79 per month
- Everything in Hobby
- Unlimited seats
- Alerts & reports
- HQL (Query Language)
Team
- $799 per month
- Everything in Pro
- 5 organizations
- SOC-2 & HIPAA compliance
- Dedicated Slack channel
Enterprise
Price on request
- Everything in Team
- Custom MSA
- SAML SSO
- On-prem deployment
- Bulk cloud discounts
Prices checked 2026-09-25 on the maker’s page.
Capabilities
- Choice of models — “Request Routing : Route by model, cost, or custom rules” source
- Self-hosted — “Now you can deploy our powerful observability platform directly within your own infrastructure with a single Docker command.” source
- API — “With this setup, any calls to the OpenAI Responses API will be automatically logged and monitored by Helicone.” source
- Official SDKs — “We’re thrilled to announce that we now have a Go SDK for Helicone’s Helpers Package .” source
- Traces and evaluates — “Built-in Observability : Full integration with Helicone’s analytics” source
Latest updates
- Claude Sonnet 4 and Sonnet 4.5 now support 1M context window
Sonnet 4 and Sonnet 4.5 models on the AI Gateway now support a 1M token context window by default across Anthropic API, AWS Bedrock, and Google Vertex AI.
- Control Reasoning Effort in Playground and better feedback on thinking models
Added the reasoning effort parameter, a minimal option, existing reasoning levels, and visual reasoning display when available.
- OpenAI GPT-5 Models Pricing and Playground Support
Added pricing for GPT-5, GPT-5-Mini, GPT-5-Nano, and GPT-5-Chat-Latest models across multiple providers.
MLflow
MLflow is an open-source platform for tracking, evaluating, deploying, and monitoring machine learning models, LLM applications, and agents.
Plans
Not read from the maker’s page yet.
Prices checked 2026-09-25 on the maker’s page.
Capabilities
- Command line — “The easiest way to start MLflow server is to run the mlflow CLI command in your terminal.” source
- Self-hosted — “Thousands of users and organizations run their own MLflow instances to meet their specific needs.” source
- API — “This page hosts the API documentation for MLflow.” source
- Official SDKs — “Python API” source
- Builds agents and workflows — “Hands-on guides and code examples for building Agents and LLM applications with MLflow.” source
- Traces and evaluates — “Capture complete traces of your LLM applications and agents to get deep insights into their behavior.” source
Latest updates
- MLflow 3.16.0 Highlights: Build-Your-Own Trace Views, a Redesigned Trace Explorer, and Span Links (3.16.0)
Build trace views in plain English, use the trace explorer, and record links between spans.
- MLflow 3.15.0 Highlights: MCP Registry, a Smarter Assistant, and Multimodal Judges (3.15.0)
Register and share MCP servers, choose LLM providers in the Assistant, save and share Runs table views, transfer artifacts using presigned URLs, and evaluate image…
- MLflow 3.14.0 Highlights: One-Line Agent Onboarding, Review Queues, Pytest Integration, and the LLM Playground (3.14.0)
Onboard apps with mlflow agent setup, collect trace reviews, manage evaluation datasets in the UI, run pytest regression tests, and use an in-browser LLM Playground.