AgentPing vs Helicone.

Both tools track LLM usage and surface cost, with different architectural bets: Helicone is a proxy that intercepts API calls; AgentPing is an SDK that wraps the agent run. As of March 2026, Helicone is in maintenance mode under Mintlify; the comparison below covers what the product does today, but the active product development is over. For an honest walkthrough of every alternative, see our Moving off Helicone in 2026 guide.

What changed in March 2026.

On 3 March 2026, Mintlify acquired Helicone and the platform entered maintenance mode. Security patches and new model support continue; new feature development has stopped. Helicone's services remain live for the foreseeable future, but the active development is over. If you are reading this page in 2026 or later, that context changes the comparison meaningfully. We have written a full migration guide covering every viable alternative honestly: Moving off Helicone in 2026.

Easy proxy integration, cost dashboards, an open-source option.

Helicone's proxy model means integration is a URL swap. If you already have a single shared OpenAI or Anthropic client and you want a cost-and-latency dashboard without writing SDK glue, Helicone is the path of least resistance. The open-source option is also genuinely useful for teams with data-residency constraints.

Watches the agent, not just the API call. Three pillars on one event.

AgentPing's primitive is the agent run. One run may make zero LLM calls or many; the agent is still one record. From that record, cost attribution rolls up by agent / customer / feature. Schedule freshness fires when a scheduled run doesn't land. Quality scoring (deterministic checks plus LLM-as-judge with calibration anchors) catches drift before the customer does.

Side-by-side

capabilities honest read
Capability Helicone AgentPing
Integration model Proxy (URL swap) SDK (1-line init, non-blocking)
Unit of analysis API call Agent run
Cost attribution by customer / feature By user / property First-class, retroactive once tagged
Schedule freshness (missed cron alerts) Not a focus Per-agent cron + tolerance window
Quality scoring (deterministic + judge) Limited Two layers + drift detection on distribution
Anomaly detection on per-agent spend Not a focus 14-day baseline, alert routes per agent
Self-hosted / open-source option Yes Hosted SaaS only at launch
Network hop on hot path Yes (proxy) No (background thread)
Pricing model Usage-priced beyond free tier Named limits, no metered billing
Starting price Free tier, then usage-based $0 free, $99 Starter, $199 Team
Seats Plan-dependent Unlimited on every plan
Run history retention Plan-dependent A full year on Team and Business
Agent evaluations Scores via API Included, zero config

Different shapes, different jobs.

Helicone is a great cost-and-latency proxy for teams that just need an LLM call dashboard. AgentPing is the right tool when "which agent caused this" and "is this agent still working" are the questions.

If you are on Helicone today

If you are currently happy with Helicone, the immediate question is not whether to migrate but when. The product still works for what it shipped. The right reasons to stay today are: you have a working production deployment, you have no near-term compliance review, and you do not need any new gateway features. The right reasons to migrate are: you are scaling and need active feature development, you have a SOC 2 or similar audit in the next twelve months, or you depend on a feature that has unresolved bugs.

Pick AgentPing if

  • Agent identity matters: you need cost per agent, customer, and feature, not per key.
  • You run scheduled agents and need missed-run alerts within a grace window.
  • You want continuous quality scoring (deterministic checks + LLM-as-judge) on the production stream.
  • You'd rather not add a network proxy on the LLM hot path.
  • You want one tool for cost, monitoring, and quality with shared alert routing.

If you are actively planning to migrate, AgentPing fits best when you need cost attribution per customer and per feature as the default (not just custom tagging), schedule freshness for cron-driven agents, and quality scoring on the production stream. The architectural shift is from proxy-in-critical-path to SDK-alongside-code, which is a different reliability story; see the migration walkthrough in our full guide.

Next steps.

If you are evaluating alternatives to Helicone, we have written a longer guide that covers every viable replacement, not just ours: Moving off Helicone in 2026.

Start free, no card required → Read the pricing page →
Is Helicone shutting down?
No. Maintenance mode means services remain live with security patches and new model support, but no new feature development. There is no public shutdown date.
How urgent is migrating off Helicone?
Not panic-urgent. A realistic planning window is six to twelve months. The triggers that genuinely accelerate migration are an upcoming compliance review, a scaling event, or a dependency on a feature that has unresolved bugs.
Can I migrate trace history from Helicone to AgentPing?
Not natively. Most teams archive Helicone's trace history and start fresh on the new platform from the migration date. The instrumentation is straightforward enough that this is rarely the bottleneck.
Helicone is a proxy. Does AgentPing work the same way?
No. AgentPing is SDK-based, not proxy-based. A proxy intercepts every LLM call by sitting in the network path; an SDK wraps the agent run and the LLM clients you already use. The SDK approach avoids adding a network hop in your hot path and works the same whether you call Anthropic, OpenAI, or self-hosted models. The trade-off is one line of SDK initialisation versus a proxy URL swap.
What does AgentPing capture that a proxy can't?
A proxy sees individual API calls but doesn't know which agent issued them or whether the agent run succeeded. AgentPing wraps the agent run as the unit of analysis, so cost attribution, schedule freshness, and quality scoring all roll up to the agent (and optionally the customer or feature). A run might issue zero, one, or fifty LLM calls; the agent is still one record.
Does Helicone do quality scoring?
Helicone has prompt versioning and basic eval surfaces, but live drift detection on the production score distribution is not its primary focus. AgentPing's Verify side runs deterministic checks plus LLM-as-judge with calibration anchors, with a hard per-team spend cap on the judge calls and statistical drift detection on the rolling baseline.
How does pricing compare?
Helicone has a free tier with paid usage scaling. AgentPing is flat-tier (Starter $99, Team $199, Business $399) with named limits and no metered billing, and annual billing is 2 months free. The flat tier is predictable; the usage tier suits bursty workloads. Pick based on volume and budget shape.
Can I use both?
Technically yes; in practice the SDK + dashboard overlap means most teams pick one. If you already have Helicone for the proxy and just need per-agent attribution plus schedule monitoring, AgentPing can run alongside without conflict; events flow independently.

How AgentPing implements cost, monitoring, and quality.

Features → What is AI agent observability? → Docs →