HoneyHive

Editor’s note: This HoneyHive review is based on my hands-on test of HoneyHive’s Developer plan with the Python SDK 1.7.0, in October 2026. I weighed pricing and the event meter, tracing setup, evaluations and experiments, production monitoring and human review, and deployment options.

Quick verdict: I recommend HoneyHive for teams running multi-step agents that want evaluations, alerts and human review in the same tool as their traces, without paying to try them. The catch is 10,000 free events per month, counting both spans and metrics, followed by a sales call because there is no self-serve paid tier.

HoneyHive pairs OpenTelemetry tracing with online evals, CI regression gates and annotation queues, and puts all of them on the free plan. In this review, I’ll take a closer look at HoneyHive’s pricing, features, and setup, so you can see exactly whether its 10,000-event allowance and quote-only Enterprise step fit your agent stack.

Key Takeaways

• Developer costs $0 with no card: 10,000 events per month, up to 5 users and 30-day retention.
• Alerts and evaluations are free features, including drift detection, annotation queues, online evals with sampling and CI/CD integration.
• Events are spans plus metrics: a run with 10 spans and 3 evaluator metrics consumes 13 events, not 1.
• Enterprise is custom-priced, with no paid self-serve tier. Self-hosting, SAML SSO and Data Export require Enterprise.
• OpenTelemetry-native SDKs cover Python 1.7.0 and TypeScript, with integrations for the OpenAI Agents SDK, LangGraph, Google ADK, AWS Strands, CrewAI and more.

Pros and Cons

How Much Does HoneyHive Cost?

HoneyHive sells two plans: Developer at $0, with no credit card, and Enterprise at a custom price. The free feature list is generous, but no paid self-serve tier makes budgeting past the free allowance harder than with Langfuse or Arize AX.

• Developer ($0): for prototypes and small teams. It includes 10,000 events per month, 30-day retention and 1,000 requests per minute, with up to 5 users, 1 workspace, unlimited projects and community support, hosted as multi-tenant SaaS in AWS US-West-2.
• Enterprise (custom): for production teams that need negotiated capacity or deployment controls. It adds custom limits and retention, unlimited users and workspaces, SAML and custom SSO, an uptime SLA with service credits, a custom DPA and BAA, and a dedicated technical account manager with quarterly business reviews. Self-hosted, hybrid and single-tenant deployments are available.

Plan detail	Developer	Enterprise
Price	$0, no card	Custom
Events/month	10,000	Custom
Retention	30 days	Custom
Rate limit	1,000 requests/minute	Custom
Users	Up to 5	Unlimited
Workspaces	1	Unlimited
Hosting	Multi-tenant, AWS US-West-2	Multi-tenant, single-tenant, hybrid, self-hosted
Data Export	No	Yes
Custom roles	No	Yes
SSO	Social SSO	Social, SAML, custom SSO
Support	Community	Dedicated TAM, uptime and support SLA

How HoneyHive Counts Events

HoneyHive counts one trace span or one metric-label combination as an event. Total usage equals spans plus metrics, so the free allowance is not 10,000 agent conversations or model requests.

My arithmetic: an agent run with 10 spans and 3 evaluator metrics consumes 13 events, which fits about 770 such runs per month. Every extra span or evaluator on that run lowers the count.

Online eval sampling slows metric growth but does not shrink span counts. HoneyHive’s suggested sampling rate above 10,000 events per day is 5 to 25%, and that traffic level already exceeds Developer’s entire monthly allowance in one day.

Is HoneyHive Good Value for Money?

• For feature access, yes: Developer includes alerts, annotation queues and online evals for up to 5 users, while LangSmith’s free tier stops at 1 seat.
• For a predictable upgrade, Langfuse wins: Core costs $29/month and Pro $199/month. Arize AX Pro at $50/month is the other low published step.
• For per-seat or flat budgeting: LangSmith Plus costs $39 per seat/month, Helicone Pro $79/month and Braintrust Pro $249/month.
• For an existing Datadog estate: Datadog LLM Observability Pro starts at $160/month, and I pick it only when agent telemetry must sit beside existing Datadog monitoring.

HoneyHive offers a startup discount for companies with under $5M in total funding raised, through sales.

Reviewer’s Notes: I recommend Developer for prototypes and a single agent that stays within the event allowance. Request an Enterprise quote before crossing 10,000 monthly events, or immediately if you need Data Export, SAML SSO or self-hosting, and ask about the startup discount on that call.

My Experience With HoneyHive

My setup used two credentials: an ingestion key for traces and a project key for experiments and reading data back. That split was the only snag between signup and my first experiment.

Signing Up and Creating a Project

I signed up at app.us.honeyhive.ai without entering a credit card, clicked New Project and created my-first-project.

I then opened Settings > Project > API Keys. On the Ingestion tab I created the tracing key and stored it as HH_INGESTION_API_KEY; HoneyHive shows that key only once, so I copied it immediately.

On the Project tab I created the separate project key and stored it as HH_API_KEY. Experiments use the project key, not the ingestion key, so I kept the two variable names distinct.

Tracing My First Agent Call

I installed HoneyHive alongside OpenAI and the OpenInference OpenAI instrumentation package. The Python SDK requires Python 3.11 or newer, so check your service’s runtime before adding it.

pip install honeyhive openai openinference-instrumentation-openai
export HH_INGESTION_API_KEY="..."
export OPENAI_API_KEY="..."

I initialized tracing with HoneyHiveTracer.init(source='development', session_name='quickstart-session'), then connected OpenAI with OpenAIInstrumentor().instrument(tracer_provider=tracer.provider). Self-hosted and dedicated deployments also pass a server_url here.

After I made an OpenAI call and ran the script, the Traces page showed the full request and response. Each trace opens in Tree, Timeline (a Gantt chart of span timing), Graph, Trajectory and Thread views, plus a Conversation view for chat sessions.

Running My First Experiment

I imported evaluate from honeyhive and built the support-message intent classifier. My function took a datapoint, read datapoint['inputs'] and returned the predicted intent as output.

I added a custom client-side evaluator, ran evaluate() against a dataset with HH_API_KEY and OPENAI_API_KEY set, and opened the Experiments dashboard for aggregated results.

I skipped HoneyHiveTracer.init() for this run because evaluate() handles tracing itself. Keep that difference in mind when you merge the tracing and experiment code into one application.

My evaluator ran client-side. Server-side Python evaluators moved to Python 3.14 in October 2026, which removes ast.Num, ast.Str and pkgutil.find_loader, so port older evaluator code before uploading it.

Reviewer’s Notes: Create both keys up front: ingestion for traces, project for experiments and reading data. A third key type, fine-grained data plane keys, arrived in October 2026, so decide which key each service gets before wiring production. Helicone, by contrast, monitors through its proxy gateway.

Tracing and Integrations: OpenTelemetry Under the Hood

HoneyHive ingests OTLP or JSON, and its SDKs are OpenTelemetry-native, so any language that emits OTel traces can reach its collector. HoneyHive claims automatic instrumentation for 50+ libraries.

The MIT-licensed Python SDK is at 1.7.0 and needs Python 3.11+. The TypeScript API SDK is at 1.6.0 and the CLI at 1.8.0; the version numbers do not move in lockstep.

TypeScript users should install the TypeScript API SDK, not the older honeyhive npm package, which sits at 1.0.45 from July 2026 and moves slowly. A Control Plane SDK at 1.3.0 covers configuration work.

• Agent frameworks: OpenAI Agents SDK, LangChain, LangGraph, LlamaIndex, Google ADK, AWS Strands, CrewAI, DSPy, Pydantic AI, Claude Agent SDK, Microsoft AutoGen, Semantic Kernel, Databricks Agent Bricks, Copilot Studio, n8n and Salesforce Agentforce.
• Model providers: OpenAI, Anthropic, AWS Bedrock, Azure OpenAI and Gemini.
• Gateways: LiteLLM, Portkey, Cloudflare AI Gateway, TrueFoundry and Vercel AI Gateway.
• Coding agents: Claude Code, OpenAI Codex CLI, Devin and Cursor SDK.

Tracing covers distributed traces, MCP server and client calls, multi-modal inputs and user feedback capture. Sampling and span filtering are the levers that keep that coverage inside the 10,000-event budget, since every retained span is billable.

For a HoneyHive vs Langfuse switch, a migration path covers Langfuse Python SDK v4 tracing, dataset imports and prompt recreation.

The Python SDK is still classified Beta and ships often: versions 1.2.0 through 1.7.0 landed between June and October 2026. Strands model events used to keep only the first tool call in a turn; an October 14, 2026 fix now keeps all tool calls and results.

Validate framework-specific telemetry on one agent before a broad rollout. If your stack is all LangChain or LangGraph, LangSmith is the tighter fit.

Evaluations, Experiments and CI Regression Gates

HoneyHive ships 52 server-side evaluator templates in Python and LLM types, and evaluations are its strongest area because the same evaluators score production traffic and pre-release experiments.

• Output checks: JSON Schema Validation, SQL Parse Check and Response length.
• Answer quality: Answer Faithfulness, Answer Relevance and Context Relevance.
• Agent behavior: Tool Usage, Plan Coverage, Failure Recovery and Handoff Completeness.
• Safety and policy: Toxicity, Policy Compliance, Harm Avoidance and Scope Adherence.

Custom Python and LLM-as-judge evaluators extend those templates. Client-side evaluators score inside application code, and human evaluators cover judgments an automated score cannot make.

Evaluation mode	Where it fits	Main control
Online	Ingested production or experiment traces	Event filters and sampling
Offline	Dataset experiments	evaluate()
CI regression	Pull-request comparisons	Baseline and compare_runs()
Human	Annotation queues	Custom evaluation fields

Online Evaluations

Online evaluations run asynchronously on ingested traces that match an evaluator’s event filters, for both production and experiment traces. HoneyHive’s suggested sampling bands:

• Under 1,000 events/day: 100%.
• 1,000 to 10,000 events/day: 25 to 50%.
• Above 10,000 events/day: 5 to 25%.

Those bands control LLM-judge cost, not plan capacity. Every metric they produce also counts toward the event meter, and Developer’s 10,000-event limit is monthly.

Offline Experiments and CI

The offline workflow runs evaluate() against a dataset, as in my intent-classifier experiment. For CI, HoneyHive provides a GitHub Actions workflow built around a baseline:

1. Create a baseline run tied to a pipeline ID.
2. Call compare_runs() on each pull request.
3. Exit non-zero when a metric degrades beyond the threshold.

The CLI also manages evaluators, datasets, charts and alerts as code, so evaluation config can ship in the same pull request as the agent change it checks.

Braintrust offers unlimited experiments on every plan, but its first paid step is $249/month. HoneyHive puts the CI gate and production alerts on a $0 plan.

Reviewer’s Notes: For an evaluation-led workflow I rank HoneyHive above Helicone and level with Braintrust. Set the CI gate on day one with a dataset that reflects the agent’s real task, because 52 templates do not pick the failure threshold for you.

Production Monitoring, Human Review and Datasets

HoneyHive puts alerts, drift detection, annotation queues and custom dashboards on Developer, so the free plan covers the full production feedback loop, not only tracing.

Alerts and Drift Detection

Aggregate alerts fire on absolute thresholds, such as an average toxicity score above 30%. Drift alerts compare periods, such as this week’s latency running 20% worse than last week’s.

Alerts can watch performance, evaluator scores, cost and custom fields, and move through five states:

• Active
• Triggered
• Resolved
• Paused
• Muted

Pair an evaluator-score alert with a latency alert, because a fast agent can still return an unfaithful answer. Custom dashboards are free, so visualization alone never forces an Enterprise upgrade. Dashboard charts come in histogram, number, categorical bar, time series bar, doughnut and table types, with shareable links and a date range selector.

Annotation Queues

Annotation queues route events to human reviewers, with human evaluators defining the fields: ratings, categories and notes. Uses include QA, active learning, compliance review and training-data curation.

Route production edge cases through a queue before adding them to regression datasets, so each test case carries a human label.

Arize AX includes labeling queues in every tier, which makes it the stronger pick when human labeling is the core job and you want a published $50/month paid step.

Datasets

HoneyHive builds datasets from traces, uploads, external syncs and Hugging Face imports, and those datasets feed the experiments and CI comparisons above.

Dataset export is not Data Export: you can export datasets on Developer, but bulk platform Data Export is Enterprise-only.

Dataset and datapoint REST endpoints are being deprecated in favor of project-scoped endpoints, so check existing integrations at each SDK upgrade.

Deployment, Security and Enterprise Controls

Developer runs only in AWS US-West-2, which rules it out for EU hosting or a private data plane. HoneyHive offers four deployment models:

• Multi-tenant SaaS: Developer’s deployment, with logical data separation in AWS US-West-2.
• Single-tenant SaaS: Enterprise, dedicated hosting in any AWS region with custom data residency.
• Hybrid SaaS: Enterprise, a HoneyHive-managed control plane with a self-hosted data plane.
• Fully self-hosted: Enterprise, with both control plane and data plane on your infrastructure.

HoneyHive claims SOC 2 Type II, GDPR and HIPAA compliance, with KMS encryption at rest, TLS 1.2+ in transit, regular third-party penetration tests, VPC isolation and PrivateLink.

EU hosting requires Dedicated Cloud or Self-Hosted, so settle residency before sending sensitive production traces.

RBAC assigns roles at organization, workspace and project scope, combining membership with a role. Developer includes basic RBAC and social SSO; custom roles and SAML require Enterprise, alongside the custom DPA and BAA. Custom model providers, a custom data retention policy and a Slack or Teams connect channel are Enterprise-only too.

Commonwealth Bank of Australia runs HoneyHive across dozens of AI applications, serving 17M+ retail customers and 55K internal users.

Langfuse is the pick when free, MIT-licensed self-hosting matters most. At Arize, the free self-host route is the separate open-source Phoenix, not Arize AX.

How Does HoneyHive Compare to Competitors?

HoneyHive packs alerts, annotation queues and CI gates into a $0 plan, but its missing middle tier pushes teams that need predictable paid pricing or free self-hosting toward the alternatives.

Tool	Free tier	First paid step	Self-host	Best for
HoneyHive	10,000 events, 5 users	Custom Enterprise	Enterprise only	Evaluations and monitoring together
Langfuse	Hobby: 50k units	Core: $29/month	Free, MIT	Open-source self-hosting
LangSmith	5k traces, 1 seat	Plus: $39/seat/month	Enterprise	LangChain teams
Arize AX	25k spans	Pro: $50/month	AX Enterprise; separate Phoenix OSS	Low-cost paid monitoring
Helicone	Hobby: free	Pro: $79/month	Open source	Gateway monitoring and caching
Braintrust	Starter: free	Pro: $249/month	Enterprise	Eval-heavy teams, unlimited users

• Langfuse: my first alternative for free self-hosting or a small paid commitment. Core costs $29/month, and the MIT-licensed core removes HoneyHive’s Enterprise-only self-hosting requirement. Langfuse joined ClickHouse in January 2026 and its core stays MIT licensed. HoneyHive still wins when alerts and human review on a free plan matter more than owning the server.
• LangSmith: my pick for teams built on LangChain or LangGraph. Plus costs $39 per seat/month, and the free tier has 1 seat and 5k traces, so HoneyHive’s 5-user Developer plan suits a small mixed-framework team better. Per-seat billing also grows with every hire.
• Arize AI: the cheapest published paid step, with Arize AX Pro at $50/month and labeling queues in every tier. Users and evals are unlimited on every AX tier. Its free 25k spans are not directly comparable to HoneyHive’s 10,000 events, because HoneyHive also counts metrics.
• Helicone: the alternative when a gateway matters more than evaluations. Pro costs $79/month, and the proxy adds caching, rate limits and fallbacks. HoneyHive wins when dataset experiments and CI regression checks drive the purchase.
• Braintrust: the pick for evaluation-heavy collaboration, with unlimited users and experiments on every plan. Pro costs $249/month and meters processed data and scores, so the paid commitment is visible before any sales call. HoneyHive stays ahead for trying production monitoring and evaluation together at $0.

The free meters are not interchangeable: LangSmith counts traces, Langfuse units, Arize AX spans and HoneyHive spans plus metrics. Run each meter against one of your own agent runs before comparing headline numbers.

Get Enterprise pricing before production rollout, not after your agents depend on HoneyHive’s evaluation and monitoring workflows.

How I Reviewed HoneyHive

I signed up for the Developer plan, created both an ingestion key and a project key, traced an OpenAI call with Python SDK 1.7.0, and ran a dataset experiment through evaluate(). I inspected the results on the Traces page and in the Experiments dashboard.

I weighed HoneyHive on pricing and the event meter, tracing and integrations, evaluations and CI gates, monitoring and human review, and deployment and security. I then separated the free-plan features from the Enterprise-only deployment and access controls.

I compared HoneyHive with Langfuse, LangSmith, Arize AX, Helicone and Braintrust on pricing structure, free-tier meters, self-hosting and workflow fit. My recommendation favors free feature access for small evaluation projects and treats custom Enterprise pricing as an open budget question.

Prices current as of October 2026.

Should You Use HoneyHive for Your AI Agents?

HoneyHive gives teams online evaluations, alerts, annotation queues and CI gates without a subscription. Its OpenTelemetry integrations cover mixed agent stacks, but the 10,000-event allowance counts both spans and evaluation metrics.

I recommend it for a prototype or a single agent with controlled traffic, when the goal is an evaluation loop and a CI gate before any observability contract.

Skip HoneyHive if you need a published paid plan. Langfuse Core at $29/month, Arize AX Pro at $50/month and Braintrust Pro at $249/month give visible budget steps, and Langfuse also covers free self-hosting.

For regulated deployments, request the Enterprise quote before instrumenting production, because private hosting, EU residency and SAML are not Developer features. If your agents already report to Datadog APM, Datadog LLM Observability Pro from $160/month keeps agent traces beside your existing APM data instead.

Start on HoneyHive’s Developer plan, instrument one agent, set evaluation sampling and watch the combined span-and-metric count against 10,000 monthly events. That number tells you when the Enterprise call is due.

FAQs

Is HoneyHive free?

Yes, HoneyHive’s Developer plan costs $0 with no credit card. It includes 10,000 events per month, up to 5 users, 30-day retention, 1 workspace and unlimited projects, plus online evaluations, alerts and annotation queues. Past the allowance, the only next step is Enterprise, because there is no paid self-serve tier.

What counts as an event in HoneyHive?

One trace span or one metric-label combination counts as an event. Total usage equals spans plus metrics, not agent runs. By my arithmetic, a run with 10 spans and 3 evaluator metrics uses 13 events, so about 770 such runs fit in the 10,000-event monthly Developer allowance.

Can I self-host HoneyHive?

Yes, but self-hosting requires Enterprise. Options are fully self-hosted control and data planes, hybrid hosting with a HoneyHive-managed control plane and your own data plane, or single-tenant SaaS. Developer is multi-tenant SaaS in AWS US-West-2 only, so request a quote before planning a private deployment.

Is HoneyHive open source?

HoneyHive’s SDKs are MIT licensed, but the platform itself is closed source. The SDK code does not give you a free self-hosted HoneyHive server; that is an Enterprise feature. For an open-source, self-hosted platform, Langfuse (MIT) or Arize’s separate Phoenix project are the routes. Both run on your own servers at no license cost.

Does HoneyHive work with LangChain and the OpenAI Agents SDK?

Yes, HoneyHive integrates with both LangChain and the OpenAI Agents SDK through OpenTelemetry-native tracing, alongside LangGraph, Google ADK, AWS Strands, CrewAI and other frameworks. Test one representative multi-step run before rollout to confirm tool calls appear in full, since Strands traces dropped tool calls until an October 2026 fix.