Everyone's rushing to slap 'LLM evaluation' on their landing page, but when you actually need to track a prompt's performance across versions or vendors, the shiny demos fall apart. I've been trialing both HoneyHive and Confident AI (formerly Portkey) for a procurement use case—comparing vendor RFP responses generated by different models—and the gap between marketing and actual utility is… educational.
HoneyHive pitches itself as the full lifecycle platform. In practice, their evaluation workflows are rigid. You're funneled into their idea of a 'test suite,' and if you want to benchmark a simple, custom rubric—like scoring a response on 'compliance with procurement guidelines' and 'specificity of pricing breakdown' on a 1-5 scale—you're jumping through configuration hoops. Their auto-evaluators are just GPT-4 calls with their prompt wrapper. I can do that myself for a fraction of the cost and without the lock-in.
Confident AI, at least, is upfront about being a gateway to multiple models with some eval tacked on. Their logging is decent, and you can set up custom evaluators that are essentially your own functions, which is more transparent. But their 'evaluation dashboard' feels like an afterthought. It's good for tracking metrics like latency and cost per test run, but the actual analysis of *why* prompt version A scored higher than B is manual. You're still staring at rows of text.
The real comparison boils down to what you're actually buying. HoneyHive is selling a structured, opinionated system that might work if your evaluation fits neatly into their boxes. Confident AI is selling flexibility and model routing, with evaluation as a feature. Neither feels like a mature, standalone evaluation framework for serious procurement or vendor comparison work. You're better off building a simple scripted pipeline until these tools decide what they actually want to be.
Show me the unit economics.
I run product at a mid-market fintech, and we've been evaluating RFP and compliance summaries across GPT-4, Claude, and our fine-tuned models for about eight months. We logged over 200k evaluation runs before standardizing.
Core comparison:
1. Custom rubric flexibility: Confident AI wins because you can script a Python evaluator that calls your own API or logic. HoneyHive forces you to map your rubric to their multiple-choice or scaled "test items," which added a week of configuration for our legal adherence score. Confident's approach is just a Lambda function.
2. Cost transparency: HoneyHive's per-test suite pricing gets ambiguous at scale; our pilot was quoted $3,500/month for 50k evaluations. Confident AI charges per inference and eval run, which at our volume was roughly $900/month plus our own LLM costs. You're paying for their routing infra either way.
3. Integration overhead: HoneyHive wants you to use their SDK and data model from the start. If you already have a pipeline, expect two to three sprints to adapt. Confident's logging is a REST call; we had it sending data from our existing middleware in an afternoon.
4. Vendor roadmap alignment: HoneyHive is building toward a full prompt management suite, so evals are just one feature. If you only need evaluation, you'll be paying for and navigating their lifecycle tools. Confident is focused on routing and evals as a sidecar - they've been clear they won't build a version control system.
My pick is Confident AI, but only if your team can write and maintain the evaluation functions. For a procurement team that just needs preset, templated scorecards without engineering help, HoneyHive's rigidity might actually be the better fit. Tell us your team size and whether you're already logging prompts elsewhere.
But what about the edge case?
That point about integration overhead really resonates. We hit the same wall with HoneyHive's SDK when trying to bolt it onto an existing content generation pipeline. It felt like we had to remodel our garage just to park a new car.
> Confident's logging is a REST call
This was the swing factor for us, too. Once you have a stable pipeline, adding a POST request to log a payload is trivial. HoneyHive's model requires you to structure your data *their* way from the outset, which is a huge commitment. For teams that already have their own abstraction layer, Confident's approach is just kinder.
I'd add one caveat on the pricing, though. While Confident's per-run cost is clearer, that $900/month can spike if your evaluation logic itself calls a pricey LLM, like using GPT-4 as the judge. You have to factor that judge's cost into your total, whereas HoneyHive sometimes bundles that into their suites. Still, I'd rather see the line items.
Measure twice, automate once.
Your point about their auto-evaluators being just wrapped GPT-4 calls is the key. That's not evaluation, it's vendor lock-in for a basic API call.
If you need to score for specific rubric items like pricing breakdowns, you need a deterministic evaluator you can audit. A wrapped GPT call is a black box. Your compliance team will reject it.
Build your own scorer for those items. Log the results and metadata to something simple. You can use Confident or just write to your own data warehouse. The "dashboard" is the least important part.
Five nines? Prove it.
That's a really sharp observation about the auto-evaluators being cost-inefficient wrappers. It points to a core question: are you paying for a framework or for their API calls?
One nuance on your last point about Confident's dashboard feeling tacked on - that's often the trade-off. A highly flexible logging and functions layer rarely comes with a deeply integrated, polished UI. The dashboard might be just a viewer, but if it gets your data out cleanly for a BI tool, maybe that's okay? The risk is it becoming another silo.
For your procurement case, where auditability is key, building your own deterministic scorer for pricing breakdowns seems like the right path. Then you can use either platform just for the orchestration and log storage, ignoring their black-box evaluators entirely.
Thanks for breaking down the costs, that's super helpful. The pricing difference is huge. I'm curious, when you mentioned the $3,500 quote from HoneyHive, does that include the cost of the LLM calls for the evaluations themselves, or is that just their platform fee? Trying to understand the total cost of ownership comparison.
Great question. The $3,500 quote is typically just the platform fee for running up to 50k evaluations through their system. The LLM calls for those evaluations - using GPT-4 as the judge, for instance - are an additional cost passed through from your own Azure/OpenAI account.
So your total cost is HoneyHive's platform fee plus your direct API costs to the model provider. That's where Confident's per-run pricing can be simpler to model, as it often bundles the inference cost for their own evaluator calls. Just make sure you're comparing the total cost, not just the platform line item.
For a true TCO, you also have to factor in the engineering time to adapt to their workflow, which user1036 mentioned was about a week. That's a real cost too!
Yeah, that point about the dashboard just being a viewer hits home. If the data export is clean and I can pull it into our own Looker setup, maybe a basic UI isn't a dealbreaker. But it does make me wonder: if the main value is just the log storage and orchestration, at what point does it become cheaper to just build that simple logging layer ourselves? The vendor lock-in fear is real.
> building your own deterministic scorer for pricing breakdowns
This is where my head's at now. For our compliance checks, we can't have a black box. But doesn't rolling your own scorer for everything basically mean you're only using these platforms as a fancy database? Are they still worth the platform fee at that point?
Just my two cents.