AI · head to head
Helicone vs MLflow
Helicone
AI
Open-source LLM observability and gateway platform for AI applications
- From
- Free
- Rated
- -
MLflow
Machine Learning
Open source platform for managing the ML lifecycle
- From
- Free
- Rated
- -
The short version
- Each has a real cost: Helicone the free Hobby plan is capped at 10,000 requests per month, which teams with production traffic can exceed quickly.; MLflow requires infrastructure setup: teams must manage MLflow tracking server, database, and artifact storage themselves
- They diverge on capability: Helicone covers Request dashboard and tracking, MLflow covers Experiment tracking.
Where they differ
Only the attributes on which Helicone and MLflow actually diverge.
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated).
What each one covers
Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.
Only in Helicone
- Request dashboard and tracking
- Sessions and segments
- Helicone Query Language (HQL)
- Prompt datasets and improvement
- Playground
- Rate limits and alerts
Only in MLflow
- Experiment tracking
- Model registry
- Model packaging
- Deployment
- Project organization
- TensorFlow
- PyTorch
- scikit-learn
What people use each for
The jobs each tool is most often brought in to do.
Helicone
- Monitoring cost and latency of production LLM applicationsnot MLflow
- Debugging multi-step agent sessionsnot MLflow
- Managing and iterating on prompts across a teamnot MLflow
- Routing requests across multiple LLM providersnot MLflow
MLflow
- Machine learningnot Helicone
- Data analysisnot Helicone
- Model trainingnot Helicone
- Predictive analyticsnot Helicone
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Helicone
- The free Hobby plan is capped at 10,000 requests per month, which teams with production traffic can exceed quickly.
- Advanced compliance features like SOC 2 and HIPAA are only available starting at the $799/month Team plan.
- Usage beyond the free tier is billed on top of the base subscription, adding cost unpredictability at scale.
- On-premises deployment is restricted to the custom Enterprise tier.
MLflow
- Requires infrastructure setup: teams must manage MLflow tracking server, database, and artifact storage themselves
- Basic UI and visualization: lacks rich interactive dashboards and real-time monitoring compared to commercial platforms
- Limited collaboration: no built-in role-based access control or multi-user management features
- Production monitoring gaps: drift detection, explainability, and alerting require separate dedicated tools
Pricing, plan by plan
Helicone
Free- HobbyFree
- 10,000 free requests
- 1 GB storage
- 1 seat
- Pro$79/month
- 10K free requests included, usage-based beyond
- 7-day free trial
- Unlimited playgrounds and workspaces
- Team$799/month
- 5 organizations
- SOC 2 and HIPAA compliance
- Dedicated Slack channel access
- Enterprise$undefined/mo
- Custom MSAs and SAML SSO
- On-premises deployment
- Bulk cloud discounts
MLflow
Free- Open SourceFree
- Experiment tracking
- Model registry
- Deployment tools
Which should you pick?
Choose Helicone if
- You need request dashboard and tracking.
- You want to start without paying.
- You work on web, api.
- You also want sessions and segments.
Choose MLflow if
- You need experiment tracking.
- You want to start without paying.
- You work on Web, Python API, REST API.
- You also want model registry.
Questions people ask
- Is Helicone or MLflow better?
- Neither clearly leads. Helicone starts at Free and MLflow at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Helicone or MLflow?
- Helicone starts at Free and MLflow at Free.
- Does Helicone or MLflow run on more platforms?
- Helicone runs on web, api. MLflow runs on Web, Python API, REST API.
- Can I use Helicone for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Helicone best used for?
- Helicone is most often used for monitoring cost and latency of production llm applications, debugging multi-step agent sessions, managing and iterating on prompts across a team, routing requests across multiple llm providers. Of those, monitoring cost and latency of production llm applications and debugging multi-step agent sessions are not what MLflow is typically brought in for.
- What can Helicone do that MLflow cannot?
- Helicone covers Request dashboard and tracking, Sessions and segments, Helicone Query Language (HQL), Prompt datasets and improvement. MLflow covers Experiment tracking, Model registry, Model packaging, Deployment.
Answered from the vendors’ own pages
Helicone: What does Helicone cost?
Helicone offers a free Hobby plan, a Pro plan at $79/month, a Team plan at $799/month, and custom Enterprise pricing, with usage-based charges applying beyond included request limits.
SourceMLflow: Is MLflow free to use?
Yes, MLflow is completely open-source and free. However, teams typically incur infrastructure costs for hosting and maintaining the MLflow tracking server. Databricks offers Managed MLflow as a commercial option for cloud deployment.
SourceHelicone: Is there a free plan, and what are its limits?
The free Hobby plan includes 10,000 requests per month, 1 GB of storage, 1 seat, and 1 organization, aimed at kickstarting AI projects.
SourceMLflow: Can MLflow track experiments for different ML frameworks?
Yes, MLflow is framework-agnostic and works with TensorFlow, PyTorch, scikit-learn, XGBoost, and any other ML framework. This flexibility is a core design principle allowing teams to use diverse tools.
SourceHelicone: Are there discounts available?
Helicone offers 50% off the first year for qualifying startups, discounts for non-profits, a $100 annual credit for open-source projects, and free access for students.
SourceMLflow: Does MLflow include a model registry?
Yes, MLflow Model Registry (added in 2018) provides a central model store with versioning, stage transitions, and deployment tracking. This enables production model governance and lineage tracking.
SourceMLflow: What are MLflow's main limitations?
MLflow requires significant infrastructure setup and maintenance. The UI is basic compared to commercial tools, collaboration is limited without third-party RBAC solutions, and production monitoring requires separate tools for drift detection and alerting.
SourceMLflow: Can MLflow handle LLM and agent tracing?
MLflow added LLM and agent tracing capabilities in recent versions, though the native support is limited compared to specialized LLM observability platforms that replaced weak LLM tracing.
SourceRelated pages
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