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Composo vs building it yourself
The honest version: you could build your own LLM evaluation pipeline. It would take your best engineer six months, they would hate every minute of it, and they would miss patterns Composo has already catalogued from 30+ deployments. Composo deploys in two weeks because we have done it before. For most teams, that trade is worth it.
Build if you have a 6-month runway, 2 senior engineers to spare on maintenance, and a domain expert who can commit weekly QA forever. Otherwise buy.
At a glance
| Dimension | Composo | building it yourself |
|---|---|---|
| Time to first value | 1 week (initial failure report) | 3 to 6 months (end-to-end pipeline) |
| Time to production | 2 to 4 weeks | 6 to 12 months including first production iteration |
| Upfront engineering cost | Contract fee, scoped per deployment | 1 senior engineer × 6 months = £90k-£120k fully-loaded |
| Ongoing maintenance | Included in licence | ~2 engineers ongoing to keep evals current as models and product evolve |
| Quality at launch | Ships with failure taxonomy from 30+ prior deployments | Ships with what you remembered to evaluate in week one |
| Model drift handling | Built-in; corrections generalise across traces | Manual - someone must re-tune scoring each model update |
| Domain-expert workload | ~10 hours over 4 weeks for calibration | Ongoing weekly review forever |
| Alignment with human experts | 90%+ in most production contexts (domain-calibrated) | ~70% (basic homegrown LLM-as-judge) |
Where building it yourself is strong
- Full control and customisation. When you build it, nothing is off-limits. You can optimise exactly for your workflow, your stack, and your team's preferences.
- IP and defensibility. If evaluation quality is core to your product (not just a supporting function), owning the stack might be strategically correct.
- No vendor dependency. You are not tied to anyone's pricing, roadmap, or uptime.
- Cheap to start. A senior engineer can vibe-code a basic LLM-as-judge setup in a day. Getting going is not the hard part.
- Internal capability building. Your team learns the failure modes of your own AI deeply - that is valuable regardless of whether the stack is built or bought.
Where Composo is different
- The first week is not the hard part. The next 12 months are. Building is cheap. Keeping an evaluation pipeline accurate as your product evolves, your models change, and your domain drifts is roughly two full-time engineers.
- We have already catalogued the failures you will hit. 30+ prior deployments means a real failure taxonomy. An internal build starts from zero and learns by missing things in production.
- Frontier-model calibration is its own specialism. Getting an LLM-as-judge above 75% accuracy on domain-specific tasks takes months of prompt iteration, criteria ensembling, and calibration. Composo does this as the core business.
- The quarterly manual QA audit is not a substitute. Sampling 100 outputs once a quarter catches what it catches. It misses everything between audits, does not compound, and cannot run at production scale.
- Domain experts are scarce. Clinicians, lawyers, and financial analysts do not want to be eval reviewers forever. Composo absorbs their knowledge once through calibration and scales it.
When to pick which
Pick building it yourself if
- · Evaluation quality IS your product (not a supporting function)
- · You have a 6+ month runway to build before needing production-grade evaluation
- · You have 2 senior engineers you can dedicate to maintenance long-term
- · You have a domain expert committed to weekly QA review indefinitely
- · You are a research organisation where the methodology is the output
Pick Composo if
- · You have AI in production now and silent failures are expensive
- · Your engineering team's time is better spent on your actual product
- · You are in a regulated vertical where mistakes have real consequences
- · You want production-grade evaluation in 2 to 4 weeks, not 6 to 12 months
- · Your domain experts are expensive and scarce
Frequently asked questions
How much does it actually cost to build an internal LLM evaluation pipeline?
A minimum viable setup: 1 senior ML engineer over 3 months (~£45-60k fully loaded) gets you a working LLM-as-judge integrated into CI/CD. A production-grade system calibrated to your domain: closer to 6 months, one full-time engineer plus a part-time domain expert. Ongoing maintenance: budget ~2 engineers once you are running at scale. The hidden cost is opportunity cost - those engineers are not shipping product.
Why does accuracy plateau when you build your own?
Most internal builds use LLM-as-judge with a single evaluator prompt. That typically reaches around 70% alignment with human domain experts. Getting to the 90%+ alignment bar needed for production gating requires techniques like criteria ensembling, variance-informed calibration, and domain-specific reward modelling. These are real research contributions, not prompt engineering, and they take months to develop internally.
What about using open-source evaluation frameworks?
Open-source options (Promptfoo, DeepEval, Phoenix, Ragas) get you started quickly. They are excellent for CI/CD-style offline evaluation. They do not solve the harder problems: catching domain-specific failures that generic evaluators miss, calibrating to your failure taxonomy, running guardrails at production scale, and handling drift. You can combine open source with Composo or build on top of them - we see both.
Is Composo locked in - can I export the evaluation logic if we part ways?
The evaluation criteria, failure taxonomy, and annotated calibration data are yours. The underlying scoring model is proprietary. Customers who leave Composo typically take the criteria and taxonomy and re-implement scoring in-house if they choose to. The lock-in is closer to 'losing institutional knowledge' than 'data hostage'.
Should a small team (1-3 engineers) ever build this internally?
Rarely. Small teams have the least slack to absorb a 6-month internal build plus ongoing maintenance. The explicit trade is: would you rather build a homegrown eval pipeline, or ship your actual product faster? For most small teams, Composo is the shortcut to a quality layer so the team stays focused on their real work.
See what Composo catches on your own AI.
A clinical-quality failure report on your production AI, delivered in under a week.