Production AI teams
Engineers who need request-level visibility into latency, cost, errors and model behavior.
Independent tool overview
Helicone at a glance
Helicone is an open-source LLM observability platform and AI gateway for logging model requests, tracking cost and latency, analyzing users and sessions, managing prompts, and routing traffic across providers.
Visit the official Helicone site ↗
- Product type
- LLM observability platform and AI gateway
- Integration
- OpenAI-compatible gateway, provider proxies and manual logging
- Model access
- 100+ models through the Helicone AI Gateway
- Deployment
- Managed cloud or open-source self-hosting
- Free allowance
- 10,000 requests per month and 1 GB storage
- Current owner
- Helicone has joined Mintlify
Overview
What Helicone is
Helicone sits between an AI application and its model providers, or accepts asynchronous logs, to give engineering teams one place to inspect requests, costs, latency, errors and usage patterns. Its OpenAI-compatible gateway also provides access to more than 100 models with routing, caching and automatic fallbacks.
The hosted product combines monitoring with prompt versioning, a playground, scores, datasets, webhooks, alerts and reports. Developers can attach user, session and custom-property metadata so a raw model call becomes traceable to a customer, workflow or release.
Helicone is also open source and publishes self-hosting options for teams that need greater data control. Self-hosting shifts deployment, upgrades, storage, security and reliability to the buyer, while the managed Team and Enterprise plans package higher limits, compliance features and support.
Use cases
Who Helicone is best for
The strongest fit depends on the job you need the product to complete, not the size of its feature list.
Multi-provider applications
Teams that want one OpenAI-compatible API with routing, caching and provider fallbacks.
Privacy-conscious deployments
Organizations willing to operate the open-source stack inside their own infrastructure.
Capabilities
Core Helicone features
Request observability
Inspect prompts, responses, token usage, model cost, latency, status and custom metadata in a searchable request log.
Sessions and users
Group multi-step calls into sessions and connect model activity to users for product-level analysis.
AI Gateway
Use an OpenAI-compatible endpoint to access more than 100 models with routing, automatic fallbacks, caching and rate limits.
Prompt management
Version prompts, use typed variables, test them in the playground and deploy prompt changes through an identifier.
Scores and datasets
Attach feedback or evaluator scores, then curate logged examples for evaluation, analysis or fine-tuning workflows.
Monitoring and reports
Paid plans add alerts, reports and Helicone Query Language for operational analysis.
Open-source self-hosting
Run the platform with Docker or Kubernetes when data sovereignty or infrastructure control outweighs managed convenience.
Process
How the Helicone workflow works
Step 1
Choose an integration path
Use the Helicone AI Gateway, a provider-specific proxy or a manual logger according to latency, provider and data-routing requirements.
Step 2
Add useful metadata
Tag requests with user IDs, session IDs, prompt IDs, environment and application properties so logs can answer product questions.
Step 3
Inspect production behavior
Filter requests and dashboards for failures, slow calls, costly models, outlier users and problematic prompt versions.
Step 4
Build quality loops
Collect feedback and scores, curate datasets, test prompt changes and compare outcomes before wider release.
Step 5
Control traffic and cost
Apply gateway caching, rate limits, routing and fallbacks, then monitor whether those policies improve reliability without masking quality problems.
Cost
Helicone pricing and free plan
Managed plans include a subscription tier plus usage-based charges where stated. All listed plans include 10,000 monthly requests; paid usage, storage and model-provider charges can increase the total. Self-hosting avoids a managed-plan dependency but creates infrastructure and operations costs.
Hobby
Free
A limited managed workspace for small projects.
- 10,000 requests per month
- 1 GB storage
- 1 seat and 1 organization
- 7-day data retention
- 10 logs per minute
Pro
$79 per month + usage
A managed plan for growing teams.
- Unlimited seats and 1 organization
- Alerts, reports and Helicone Query Language
- 1-month retention
- 1,000 logs per minute
- 7-day free trial listed
Team
$799 per month + usage
Higher limits, compliance and support for scaling companies.
- 5 organizations
- SOC 2 and HIPAA features
- Dedicated Slack channel
- 3-month retention
- 15,000 logs per minute
Enterprise
Contact sales
Custom commercial and deployment terms.
- Unlimited organizations
- Custom MSA and SAML SSO
- On-premises deployment
- Forever retention listed
- Bulk cloud discounts
Pricing checked . Check current pricing at the source ↗
Assessment
Helicone strengths and limitations
Where it stands out
- Combines request analytics, prompt operations and gateway controls in one developer platform.
- OpenAI-compatible integration can reduce the work required to add multi-provider routing.
- Open-source code and documented self-hosting reduce dependence on the managed cloud.
- User, session and custom-property tracking make logs more useful for product and customer analysis.
- The free managed tier is large enough to evaluate the core workflow on a small application.
What to consider
- Proxy-based integrations put another service in the request path, so teams must evaluate latency, availability and failure behavior.
- Prompt and response logs can contain personal, proprietary or regulated data; configure redaction, access, retention and regional requirements deliberately.
- Paid managed plans also use metered pricing, so the subscription price alone is not the complete monthly cost.
- The Hobby tier is limited to one seat, seven days of retention and 10 ingested logs per minute.
- Self-hosting requires operating multiple storage and application components, performing upgrades and securing the deployment.
- Helicone's former Experiments feature was deprecated; current evaluation workflows rely on prompts, playgrounds, scores and datasets instead.
- Gateway caching and fallbacks can change cost and behavior, so they need application-specific testing rather than blanket enablement.
Compare
Helicone alternatives
The right alternative depends on the specific output, workflow, controls and budget your project requires.
Miscellaneous
Mistral Studio
Choose Mistral Studio when managed model development, evaluation and deployment within Mistral's platform are the priority.
Explore Mistral Studio →Coding
Together AI
Choose Together AI when hosted inference and model customization matter more than provider-neutral observability.
Explore Together AI →Coding
Google AI Studio
Choose Google AI Studio for a simpler Gemini-focused prototyping environment rather than a production-wide observability layer.
Explore Google AI Studio →Questions
Helicone FAQs
What does Helicone monitor?
Helicone records LLM request and response data plus metrics such as tokens, cost, latency, status, model, users, sessions and custom properties. What is visible depends on the integration and logging configuration.
Does Helicone replace an LLM provider?
Not necessarily. You can use its gateway to access supported models or route calls through provider-specific proxies while retaining your provider relationship. Model usage charges remain separate.
Is Helicone open source?
Yes. Helicone publishes its platform code and documents Docker, Kubernetes, manual and cloud self-hosting options.
Can Helicone keep prompts out of its logs?
Helicone documents controls such as omitting logs and a key vault, but teams should test the exact behavior and design their own redaction, retention and access policies before sending sensitive production traffic.
What is included in Helicone's free plan?
The current Hobby plan lists 10,000 requests per month, 1 GB of storage, one seat, one organization, seven days of retention and an ingestion limit of 10 logs per minute.
Does Helicone charge for model tokens?
The Helicone subscription and observability usage are separate from the underlying model cost. Gateway or provider billing arrangements determine how model usage itself is paid.
Bottom line
Our Helicone verdict
Helicone is a strong fit for teams that want fast, provider-flexible LLM monitoring and gateway controls without giving up a self-hosting path. The biggest decision is architectural: determine whether to put the managed gateway in the critical path or log asynchronously, then model the metered cost and treat prompt data as production-sensitive telemetry.
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