Helicone vs Kelet: Which is Better in 2026?
A comprehensive comparison of Helicone and Kelet covering features, pricing, use cases, and which tool is the right choice for your needs.
⚡ Quick Verdict
Choose Helicone if:
- →You need request logging and replay or cost and latency tracking
Choose Kelet if:
- →You want more affordable paid plans (from $400/mo)
- →You need a broader feature set (6 features vs 5)
- →You need automatic root-cause analysis on production agent failures or generates a prompt patch rather than only surfacing traces
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Helicone vs Kelet: At a Glance
Pricing Comparison: Helicone vs Kelet
Understanding the pricing differences between Helicone and Kelet is crucial for making the right choice. Here's how their plans compare side by side.
Kelet Pricing
💡 Pricing takeaway: Both Helicone and Kelet offer free tiers, making it easy to try before you buy. Visit each tool's website for the latest pricing details.
Feature-by-Feature Comparison
Here's how every feature from Helicone and Kelet stacks up.
What Makes Each Tool Unique
🔵 Unique to Helicone
Features available in Helicone but not in Kelet:
- ✓Request logging and replay
- ✓Cost and latency tracking
- ✓Prompt versioning
- ✓A/B testing for prompts
- ✓User session tracking
🟣 Unique to Kelet
Features available in Kelet but not in Helicone:
- ✓Automatic root-cause analysis on production agent failures
- ✓Generates a prompt patch rather than only surfacing traces
- ✓OpenTelemetry and Langfuse ingestion
- ✓Integrations across LangChain, CrewAI, Mastra, Agno, Strands and Pydantic AI
- ✓Human signals and feedback collection free on every tier
- ✓Per-session billing tied to a unit of agent work
Use Case Recommendations
Best for: Helicone
Open-source LLM observability platform. Helicone logs all your LLM requests, tracks costs and latency, runs prompt experiments, and detects issues — with a single line of code added to your existing OpenAI or Anthropic calls.
Ideal use cases:
- •Teams or individuals who need request logging and replay
- •Teams or individuals who need cost and latency tracking
- •Teams or individuals who need prompt versioning
- •Teams or individuals who need a/b testing for prompts
- •Anyone focused on LLM observability workflows
- •Anyone focused on prompt monitoring workflows
Best for: Kelet
Kelet is root-cause analysis for LLM applications and AI agents in production. Observability tools of the tracing generation are good at showing you that a run failed and what the spans were; Kelet's premise is that the expensive part is the next step — reading a haystack of traces to work out why the agent went wrong and what to change. It ingests from the stack teams already have, with OpenTelemetry and Langfuse integrations plus support for OpenAI, Anthropic, LangChain, CrewAI, Mastra, Agno, Strands, Pydantic AI, Google ADK, the Vercel AI SDK, PostHog and Mixpanel, then continuously diagnoses failures and generates a prompt patch rather than a dashboard. Human signals and feedback collection are free on every tier, which is a deliberate choice: the labels that make failure classification useful are exactly the thing most vendors put behind the paywall, so gating them would break the product for the teams most likely to adopt it. Billing is per session, where a session is one unit of agent work — a conversation, a task run or a pipeline execution — and the free tier covers 500 a month with 15-day retention. The startup tier is $400/month but free during early access with 30 days' notice before pricing changes and grandfathering for early users, so the honest read is that its real economics are not yet proven in the wild.
Ideal use cases:
- •Teams or individuals who need automatic root-cause analysis on production agent failures
- •Teams or individuals who need generates a prompt patch rather than only surfacing traces
- •Teams or individuals who need opentelemetry and langfuse ingestion
- •Teams or individuals who need integrations across langchain, crewai, mastra, agno, strands and pydantic ai
- •Anyone focused on observability workflows
- •Anyone focused on agents workflows
🤖 Other AI Agent Infrastructure Tools to Consider
Helicone and Kelet aren't the only options. Here are other popular tools in the same space:
SuperAGI
Open-source autonomous AI agent framework with visual dashboard — 14K GitHub stars
MetaGPT
Multi-agent AI framework simulating software teams — 45K GitHub stars, builds full apps from prompts
Cerebras
Fastest LLM inference powered by the Wafer Scale Engine.
Scale AI
AI data platform for training data and model evaluation.
Roboflow
End-to-end computer vision platform for developers.
Labelbox
Enterprise data labeling platform for ML training datasets.
Is one of these your tool?
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Frequently Asked Questions
Is Helicone better than Kelet?
It depends on your needs. Helicone offers 5 key features including Request logging and replay and Cost and latency tracking, while Kelet provides 6 features including Automatic root-cause analysis on production agent failures and Generates a prompt patch rather than only surfacing traces. Helicone uses a freemium model with a free tier, while Kelet is freemium with free access available. Choose based on which features and pricing model align with your requirements.
Is Helicone cheaper than Kelet?
Helicone doesn't have standard paid plans, while Kelet starts at $400/month. Both tools offer free tiers, so you can try each before committing. Always check the official websites for the most current pricing.
Can I use Helicone and Kelet together?
Yes, many users combine Helicone and Kelet in their workflow. Helicone excels at request logging and replay, while Kelet shines with automatic root-cause analysis on production agent failures. Using both allows you to leverage the strengths of each tool, though this means managing two subscriptions — though free tiers can help manage costs.
What's the main difference between Helicone and Kelet?
While both are ai agent infrastructure tools, Helicone emphasizes request logging and replay, whereas Kelet is known for automatic root-cause analysis on production agent failures. The best choice depends on your specific workflow and feature priorities.
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