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HoneyHive is our best fit for most teams looking for AI tracing and quality monitoring, while OpenLIT is a stronger match for teams already self-hosting and wanting OpenTelemetry export. Helicone combines tracing with a gateway, but its pricing and development outlook merit a closer look. Coval focuses on voice-agent QA, and Agenta is for building and running AI coworkers—not replacing an observability stack. Free plans are available from several vendors; listed paid starting prices range from $29 to $80, while some pricing remains unpublished.
Quick picksThe shortlist
01Compared at a glance
02The picks, in detail
- Best overall
HoneyHiveHoneyHiveHoneyHive offers the clearest fit for teams that need both trace and quality monitoring, with evaluation coverage for teams assessing agents before release. It also has a free plan, though the event cap is worth checking before you commit. Enterprise pricing is not listed here, so get a quote before comparing its total cost with other platforms.
What works
- Developer plan costs $0 per month
- Includes 10,000 events per month
- Supports trace replay and production monitoring
What doesn't
- Developer plan is capped at five users
- Developer plan includes one workspace
- Developer data retention is 30 days
- Best for self-hosters
OpenLITOpenLITOpenLIT suits teams already running their own observability infrastructure, especially AI teams that want OpenTelemetry export or operate GPU-backed LLMs. Its free plan makes the starting price attractive, but self-hosting is the main trade-off. Hosted pricing is not published in the available details, so teams that do not want to manage deployment should confirm the hosted option and its cost before switching.
What works
- OSS is free under Apache 2.0
- Unlimited self-hosted usage, with no per-trace fee
- Exports telemetry to other OpenTelemetry backends
What doesn't
- Cloud is coming soon; pricing is not published
- Enterprise pricing is contact-only
- Paid-plan billing terms are not published
- Best gateway and tracing
HeliconeHeliconeHelicone combines an LLM gateway with tracing, prompt inspection and agent inspection, giving teams more than a standalone monitoring fit. It has a free plan and a listed starting price of $79. Pricing uncertainty and user reports of slower development are reasons to check the current product direction and plan terms before moving a production stack.
What works
- Routes requests to over 100 models through one API key
- Gateway includes failover and zero percent markup
- Tracks latency, tokens, errors and costs
What doesn't
- Usage rates above plan allowances are not published
- Annual billing prices are not published
- Pro limits ingestion to 1,000 logs per minute
- Best for voice QA
CovalCovalCoval is the focused choice for teams testing voice agents before launch, tracking agent quality in production or comparing voice AI vendors. Its listed starting price is $80. The simulation limits deserve scrutiny, and the available details do not specify those caps, so confirm what the plan includes and whether it fits your evaluation volume before buying.
What works
- Simulates callers, policies, noisy audio, and edge cases
- Evaluates live conversations for failures and quality drift
- Human QA judgments can inform evaluations
What doesn't
- Starter includes only 100 simulation minutes monthly
- Starter upload allowance is 3,000 minutes monthly
- Starter overages cost $0.40 per simulation minute
- Best for AI coworkers
AgentaAgentaAgenta is aimed at teams building Slack or Telegram agents and automating scheduled or triggered tasks. It offers a free plan and a listed starting price of $29, and self-hosters can choose its MIT-licensed option. That price may look appealing, but Agenta is for building and running AI coworkers—not a replacement for a dedicated AI observability stack.
What works
- Free Hobby plan includes 500 signup credits
- Open-source version supports self-hosting under MIT
- Open-source plan lists unlimited users and projects
What doesn't
- Hobby is limited to two concurrent tasks
- Starter costs $29 monthly; annual pricing is not published
- Credit allowances require usage planning
03How we chose
We weigh published pricing, plan limits, product documentation, and Reddit sentiment when comparing AI observability platforms. Those sources help show what a team can verify before switching: what is included, which limits may affect a production workload, and what users say about practical trade-offs. We do not treat vendor claims as proof of performance, and we do not claim hands-on testing. When a price, limit, or capability is not documented in the materials available for comparison, we leave it unconfirmed rather than fill the gap.
04Check the production workflow before you switch
Start with the signals your team needs to inspect, not a feature checklist. Map how requests move through your application, including model calls, retrieval, tools, and agent steps. Then confirm in the docs whether a platform can capture the inputs and outputs you need, connect them to a trace, and support the evaluation workflow your team expects.
Ask how data is collected and what you can control about sensitive fields, retention, and access. Those details affect whether a platform fits your production requirements; do not assume they are the same across vendors. Also check plan limits against expected usage, and confirm what happens when you exceed them. If a key capability or limit is not documented, ask the vendor for a clear answer before planning a migration.
05Compare total cost and switching effort
A low starting price is not enough to judge fit. Compare the published plan price with its included usage, feature restrictions, and any limits that matter to your workload. If pricing or a cap is not published, mark it as unknown and request a written quote or clarification rather than estimating.
Switching effort matters too. Identify what needs to move: instrumentation, existing traces, evaluation datasets, dashboards, and team workflows. Check the documentation for setup steps and supported integrations, then list any gaps your engineers would need to bridge. A platform that fits your evaluation process but requires a large instrumentation change may cost more in engineering time than its subscription suggests. Use the same checklist for OpenLIT, HoneyHive, Agenta, Helicone, and Coval, and compare only details each vendor documents.
Questions people ask
What does an AI observability platform do?
An AI observability platform helps teams inspect and evaluate an AI application after deployment. When comparing products, check which parts of your workflow they can capture, how they connect events into traces, and what evaluation tools are documented. Do not assume every platform covers every model or agent setup.
How should I choose an AI observability platform?
Start with your production workflow, data requirements, and evaluation process. Compare documented integrations, plan limits, privacy controls, and pricing against those needs. Confirm unclear capabilities with the vendor. A feature that appears in a product description is not enough; check whether its availability or usage is restricted by plan.
How much do AI observability platforms cost?
Pricing depends on each vendor's published plans and usage limits. We do not have verified price figures in the materials available for this comparison, so we cannot give a reliable range. Check current pricing pages and confirm how usage is counted, what each plan includes, and whether overages or custom pricing apply.
Can these platforms monitor AI agents?
Agent workflows can involve multiple model calls, tools, and intermediate steps, so verify exactly what each product records and evaluates. Check the documentation for trace coverage, supported integrations, and plan restrictions. Do not assume that a platform's general LLM observability features cover your agent framework or workflow.