Eppo vs PostHog Experiments: Comparing A/B Testing Platforms
This skill teaches you how to systematically evaluate eppo vs posthog experiments and other A/B testing platforms by scoring them across analysis methods, integrations, pricing, and team workflow fit so you pick the right tool for your experimentation program.
Build a comparison scorecard covering five dimensions: statistical analysis methods, data warehouse integration, feature flag capabilities, pricing model, and team workflow fit. Score each platform against your actual experiment volume, data infrastructure, and team size. PostHog bundles experimentation with product analytics. Eppo connects to your existing warehouse. LaunchDarkly leads in feature management. The right choice depends on your stack and experimentation maturity.
Outcome: You produce a weighted comparison scorecard that maps each platform's strengths and limitations to your specific requirements, giving your team a defensible recommendation instead of a gut-feel tool choice.
Prerequisites
- Basic understanding of A/B testing concepts (control vs variant, statistical significance, sample size)
- Familiarity with your team's current data infrastructure (warehouse, analytics tools, event tracking)
- Knowledge of your organization's experiment volume and growth plans
- Understanding of feature flags and how they relate to experiment assignment
Overview
Choosing an experimentation platform is one of the highest-leverage decisions a product or growth team makes, because the tool shapes how quickly you can launch tests, how rigorously you can analyze results, and how deeply experimentation culture embeds into your organization. The landscape spans bundled product analytics suites like PostHog, warehouse-native platforms like Eppo, feature management tools with experimentation add-ons like LaunchDarkly, and dedicated statistics engines like Statsig. Each platform makes different architectural bets, and each bet has consequences for your workflow. Evaluating eppo vs posthog experiments, or any cross-platform comparison, requires more than reading feature matrices. You need to map each tool's capabilities against your data stack, your team's statistical literacy, your experiment volume, and your budget.
This skill sits inside the PostHog Experiments Onboarding A/B Test Method as a decision checkpoint. Before you invest weeks building experiment infrastructure on any platform, you should confirm that the platform actually fits your requirements. Teams that skip this step often discover mismatches six months in, when they need warehouse-native analysis that their bundled tool does not support, or when they realize they are paying for feature flag infrastructure they already built in-house. The comparison scorecard you produce here prevents that costly rework.
The concrete artifact is a scored evaluation matrix. Each row is a platform (PostHog, Eppo, LaunchDarkly, Statsig, and optionally others). Each column is a weighted evaluation dimension: statistical analysis approach, data integration model, feature flag capabilities, pricing structure, and team workflow fit. You assign numerical scores, weight them by your priorities, and produce a ranked recommendation. The scorecard is shareable with engineering leads, data teams, and finance, giving each stakeholder a clear view of why one platform fits better than another. By the end, you will have a documented, repeatable framework you can revisit whenever your requirements change or a new platform enters the market.
How It Works
Platform comparison works because experimentation tools are not interchangeable. They differ along five dimensions that compound over time, and understanding these dimensions lets you predict how a tool will perform as your program scales.
The first dimension is statistical analysis method. PostHog offers both Bayesian and frequentist engines, letting you choose per experiment. Eppo leans into frequentist methods with sequential testing and CUPED variance reduction, which matters when you need to detect small effects or run experiments on limited traffic. Statsig provides a proprietary Bayesian engine with automatic sample size calculations. LaunchDarkly's experimentation module is thinner statistically, relying on simpler significance calculations. The analysis method determines how fast you can call experiments, how accurately you detect small lifts, and how much statistical expertise your team needs to interpret results correctly. If your team runs the sibling skill interpreting Bayesian and frequentist results, the analysis engine choice directly affects their daily work.
The second dimension is data integration model. This is where architectural philosophies diverge most sharply. PostHog captures and stores events in its own infrastructure, meaning your experiment data lives alongside your product analytics. Eppo takes a warehouse-native approach: it reads from your existing data warehouse (Snowflake, BigQuery, Redshift, Databricks) and writes results back, so your experiment data stays in your single source of truth. LaunchDarkly and Statsig fall between these poles, capturing their own event streams but offering warehouse export connectors. The integration model matters because it determines whether you get a unified view of experiment results alongside your other business data, or whether you maintain two parallel data pipelines.
The third dimension is feature flag capabilities. PostHog includes feature flags as a core product, tightly coupled with experiments. LaunchDarkly is the market leader in feature flag management, with sophisticated targeting rules, percentage rollouts, and flag lifecycle management. Eppo does not provide its own feature flags at all; it integrates with whatever flag system you already use (LaunchDarkly, Unleash, Flagsmith, or homegrown). Statsig bundles flags and experiments together similar to PostHog. If your team already has a mature flag system, a platform that layers on top (like Eppo) avoids duplication. If you are starting from scratch, a bundled platform reduces integration work.
The fourth dimension is pricing model. PostHog uses event-based pricing with a generous free tier (1 million events per month). Eppo prices by the number of experiments or seats depending on tier. LaunchDarkly charges per seat with feature-flag usage tiers, and experimentation is an add-on. Statsig uses event-based pricing similar to PostHog. The pricing model interacts with your growth trajectory: event-based pricing scales with traffic, seat-based pricing scales with team size. A 10-person team running high-traffic experiments faces very different economics than a 50-person team running low-traffic tests.
The fifth dimension is team workflow fit. This is the hardest to evaluate from a feature page. It includes how experiments are created (UI vs. code), how results are shared (dashboards, Slack integrations, email digests), how experiments connect to deployment pipelines, and how non-technical stakeholders access insights. The only reliable way to score this dimension is to run a pilot experiment on each shortlisted platform, which the step-by-step process below walks you through.
Step-by-Step
Step 1: Document your experimentation requirements
Before looking at any platform, write down what your team actually needs. List your current monthly event volume and projected volume 12 months out. Count how many experiments you expect to run per month. Identify your data warehouse (if any) and the analytics tools already in your stack.
Note whether you have an existing feature flag system. Record who will create experiments (engineers only, or also PMs and designers), because this affects UI requirements. Finally, list any hard constraints: compliance requirements, data residency rules, or budget ceilings. This requirements document becomes the weighting input for your scorecard.
Tip: Interview at least one person from engineering, product, and data science. Each group has different priorities, and a tool that delights PMs but frustrates data scientists will not stick.
Step 2: Build the evaluation scorecard template
Create a spreadsheet or table with platforms as rows and evaluation dimensions as columns. Use these five columns: Statistical Analysis (weight: 25%), Data Integration (weight: 25%), Feature Flags (weight: 20%), Pricing (weight: 15%), and Team Workflow (weight: 15%). Adjust weights based on your requirements document. If your team already has a warehouse and wants experiments to query alongside revenue data, increase the Data Integration weight.
If you are budget-constrained, increase the Pricing weight. Each cell will hold a score from 1 to 5, and the weighted total determines your ranking. Include a notes column for each cell to capture the reasoning behind each score.
Tip: Do not assign equal weights to all dimensions. Equal weighting is a decision to not decide. Force-rank your priorities by asking: if two platforms tied on everything else, which single dimension would break the tie?
Step 3: Research each platform's statistical capabilities
For each platform on your shortlist, document the analysis engine (Bayesian, frequentist, or both), whether sequential testing is supported (letting you peek at results without inflating false positive rates), whether variance reduction methods like CUPED or CUPAC are available, how the platform handles multiple metrics per experiment, and what guardrail metric support looks like. Check whether the platform calculates required sample sizes automatically or requires manual calculation. Score PostHog, Eppo, LaunchDarkly, Statsig, and any other candidates on a 1-5 scale for this dimension. A platform that offers both Bayesian and frequentist engines with sequential testing and variance reduction scores highest.
A platform with only basic significance testing scores lowest.
Tip: Variance reduction (CUPED) is a sleeper feature. It can cut required experiment runtime by 30-50% by controlling for pre-experiment user behavior. If your traffic is limited, this single feature can be the difference between calling experiments in two weeks vs. six weeks.
Step 4: Map data integration architecture
For each platform, diagram how experiment data flows. Identify where events are captured (client SDK, server SDK, warehouse import), where they are stored (platform's own database vs. your warehouse), and how results are accessed (platform UI, API, SQL queries in your warehouse). Check whether the platform supports your specific warehouse (Snowflake, BigQuery, Redshift, Databricks, ClickHouse).
For Eppo specifically, verify that your warehouse schema matches their expected format, since Eppo reads directly from your tables. For PostHog, check whether you can export experiment results back to your warehouse for deeper analysis. Score each platform based on how well it fits your existing data infrastructure without requiring new pipelines.
Tip: Ask yourself: where do I want experiment results to live in two years? If the answer is 'in our warehouse alongside everything else,' warehouse-native tools like Eppo score higher even if they require more setup today.
Step 5: Evaluate feature flag integration
Document each platform's feature flag capabilities. For PostHog, check the flag types supported (boolean, multivariate, JSON payloads), targeting rules (user properties, percentage rollout, cohort-based), and how flags connect to experiments. For LaunchDarkly, review the same plus flag lifecycle management, audit logs, and approval workflows. For Eppo, identify which third-party flag tools it integrates with and how seamless the connection is.
For Statsig, check the same bundled capabilities. If you already run a feature flag system, score platforms that integrate with it higher than those requiring migration. If you have no flag system, score bundled offerings higher for reduced integration effort.
Tip: If you use LaunchDarkly for flags and are considering Eppo for experiments, test the integration during your pilot. The two tools complement each other well: LaunchDarkly manages assignment, Eppo handles analysis. But verify that the flag-to-experiment mapping works smoothly with your specific flag configuration.
Step 6: Calculate total cost of ownership
Pricing pages show sticker prices, but total cost of ownership includes engineering time for integration, ongoing maintenance, and the cost of vendor lock-in. For each platform, calculate the monthly platform fee at your current volume and at projected 12-month volume. Add estimated engineering hours for initial setup (SDK integration, warehouse connections, flag migration if needed). Factor in ongoing maintenance: how many engineer-hours per month does each platform require for upkeep?
Include the cost of any required add-ons (LaunchDarkly charges extra for experimentation on top of flag management). Finally, assess switching cost: if you need to leave this platform in 18 months, how much data and workflow would you lose? Score each platform on value relative to your budget, not just on raw price.
Tip: PostHog's free tier (1M events/month) is generous for small teams, but event-based pricing can spike unexpectedly as you scale. Model costs at 3x your current volume before committing. Eppo's pricing is less transparent on their public page, so request a quote with your specific experiment count.
Step 7: Run a pilot experiment on each finalist
Narrow your list to two or three platforms and run a real (small) experiment on each one. Use the same experiment hypothesis and the same metrics so you can compare the experience directly. Set up a simple A/B test on a low-risk feature, such as a button color or copy change on a settings page. ' Note every friction point: confusing UI, unclear documentation, SDK bugs, slow result computation, or missing integrations.
Have at least two team members (one technical, one non-technical if possible) use each platform independently and compare notes. This pilot is the only reliable way to score the Team Workflow dimension, because feature pages do not reveal daily-use friction.
Tip: Allocate one week per platform for the pilot. Run the experiment for at least three days to see how results update, how alerting works, and how the platform handles the 'waiting for significance' phase. The experience of checking results daily reveals workflow fit that no demo can show.
Step 8: Score, weight, and rank
Fill in every cell of your scorecard with the 1-5 scores gathered from your research and pilots. Multiply each score by its dimension weight. Sum the weighted scores for each platform to get a total. Rank the platforms by total score.
Review the results with your team and check for any dimension where the winning platform scored below 3. A platform that wins overall but scores a 2 on Data Integration may cause problems in six months. 5 points of each other, recheck your weights and consider whether the pilot experience should tip the balance. Document your reasoning in the notes column so the decision is auditable later.
Tip: Share the completed scorecard with stakeholders before announcing a decision. Give engineering, product, and data teams a chance to challenge scores. This builds buy-in and sometimes surfaces information you missed, like a data engineer who already evaluated one of the platforms at a previous company.
Step 9: Document the decision and plan migration
Write a one-page decision document that states which platform you chose, the top three reasons, the main tradeoffs you accepted, and the timeline for full adoption. If you are already using PostHog as part of the PostHog Experiments Onboarding A/B Test Method and decide to stay, document what you evaluated and why you chose not to switch, so the question does not resurface in six months. If you decide to migrate to a different platform, create a migration checklist: SDK swaps, flag migration, historical data export, team training, and a parallel-run period where both platforms run simultaneously to validate that the new tool produces consistent results. Set a review date 6 months out to revisit the decision.
Tip: Even if PostHog wins your evaluation, the scorecard is valuable. It gives you a documented baseline to revisit when your experiment volume doubles, your team grows, or a competing platform ships a major feature update.
Examples
Example: Early-stage SaaS with 5-person product team and no warehouse
A seed-stage B2B SaaS company with 15,000 monthly active users wants to start running A/B tests on their onboarding flow. They have no data warehouse, use Mixpanel for analytics, and their engineering team of three has no prior experimentation experience. Budget is tight: under $500/month for tooling. They expect to run 2-3 experiments per month.
The team builds the evaluation scorecard and immediately notices that warehouse-native tools like Eppo score low on Data Integration because they have no warehouse to connect to. Setting up Snowflake or BigQuery just for experimentation would add months of work and ongoing cost. PostHog scores highest on the bundled approach: experiments, feature flags, and analytics in one platform with a free tier covering their event volume (roughly 200,000 events/month). LaunchDarkly's experimentation add-on costs more than their total tooling budget when combined with the base seat licensing.
Statsig is competitive on price but adds another analytics pipeline alongside Mixpanel. The team runs a pilot on PostHog and Statsig. PostHog's integration takes one afternoon because they can replace Mixpanel simultaneously. 1.
They choose PostHog and plan to revisit in 12 months when they expect to have a warehouse in place. The decision document notes that Eppo should be re-evaluated once their data infrastructure matures.
Example: Growth-stage fintech with a mature data warehouse and 50-person engineering org
A Series C fintech company processing 2 million events per day runs experiments on payment flows, pricing pages, and onboarding. They have a well-maintained Snowflake warehouse, a 4-person data science team, and currently use an in-house experimentation system built on top of LaunchDarkly flags. The homegrown system lacks sequential testing and variance reduction, causing experiments to run 3-4 weeks when they should take 10 days. The team evaluates whether to improve their custom system, adopt PostHog, move to Eppo, or switch to Statsig.
The requirements document reveals that Data Integration is the highest priority (30% weight) because all reporting runs on Snowflake. Statistical Analysis is next (25%) because they need CUPED and sequential testing to cut experiment runtime. Feature Flags get 20% weight since they already use LaunchDarkly and do not want to migrate flags. Pricing gets 15%, and Workflow gets 10% since the team is technically sophisticated.
Eppo scores a 5 on Data Integration (reads directly from Snowflake, results stay in the warehouse), a 5 on Statistical Analysis (sequential testing, CUPED, metric-level power analysis), and a 5 on Feature Flags (integrates with their existing LaunchDarkly setup). PostHog scores a 3 on Data Integration (separate event pipeline, would need export to Snowflake) and a 4 on Statistical Analysis (Bayesian and frequentist, but no CUPED at the time of evaluation). 4. The pilot confirms that Eppo's Snowflake integration takes 2 days to configure and experiment results appear as queryable tables alongside their existing metrics.
The team adopts Eppo for experimentation while keeping LaunchDarkly for flag management.
Example: E-commerce company prioritizing speed of setup over analytical depth
A DTC e-commerce brand with 500,000 monthly visitors and a 3-person growth team wants to test product page layouts, checkout flows, and promotional banners. They use Shopify, have basic Google Analytics, no warehouse, and need to start testing within one week. They plan to run 5-8 experiments per month and do not have a data scientist on staff. The growth lead can interpret basic conversion rate differences but has never configured a statistical test.
Speed of setup and ease of use dominate the requirements. The team weights Team Workflow at 30%, Feature Flags at 25% (they need flags for Shopify integration), Data Integration at 15%, Statistical Analysis at 15%, and Pricing at 15%. PostHog's JavaScript snippet installs in 30 minutes and provides flags, experiments, and analytics immediately. The growth lead finds PostHog's experiment creation UI intuitive, with guided steps for hypothesis, metric selection, and minimum sample size.
Statsig offers a similar bundled experience but requires slightly more configuration for Shopify. Eppo is eliminated early because the team has no warehouse and no plans to build one. LaunchDarkly is eliminated because the experimentation add-on is too expensive for their volume. The pilot on PostHog takes one day.
The growth lead creates an experiment, assigns 50/50 traffic to two product page variants, and sees results within 48 hours. 8. They choose PostHog and use the PostHog Experiments Onboarding A/B Test Method as their playbook for the first three months.
Example: Enterprise SaaS evaluating compliance and governance requirements
A healthcare SaaS company subject to HIPAA runs experiments on clinical workflow features. They need SOC 2 compliance, data residency in the US, audit logs for every flag change, role-based access control for experiment management, and the ability to restrict experiment results to authorized personnel. Their data warehouse is BigQuery, and they have a 6-person platform engineering team. Budget is not the primary constraint, but predictability of costs matters.
Compliance and governance requirements reshape the scorecard. The team adds a sixth dimension: Security and Compliance (weighted 25%), reducing other weights proportionally. LaunchDarkly scores highest on governance: SOC 2 Type II certified, role-based access, approval workflows for flag changes, and detailed audit logs. However, its experimentation module scores lower on Statistical Analysis (basic significance testing, no CUPED).
Eppo scores well on Data Integration (BigQuery native) and Statistical Analysis, but the team needs to verify its compliance certifications and data handling practices. PostHog offers self-hosted deployment (keeping all data on the company's own infrastructure), which solves data residency but adds operational burden. The team scores LaunchDarkly for flags plus Eppo for experiments as the combination that best satisfies compliance, analytical rigor, and data integration. The pilot validates this split: LaunchDarkly handles flag governance while Eppo reads experiment results from BigQuery with no PHI leaving the warehouse.
Best Practices
Weight your scorecard dimensions before researching any platform. Setting weights after you have already formed opinions creates confirmation bias. You will unconsciously inflate the weight of whatever dimension your preferred platform wins. Write down weights based solely on your requirements document, then lock them in.
Evaluate at your projected scale, not your current scale. A platform that works beautifully for 5 experiments per month may buckle at 50. Ask each vendor what their largest customer runs, how result computation time scales with event volume, and whether pricing tiers change at higher volumes. Teams that evaluate at current scale often outgrow their tool within a year.
Include non-technical stakeholders in the pilot. If product managers and designers cannot independently check experiment results, you create a bottleneck where data scientists become translators. Score the Team Workflow dimension partly based on whether a PM can understand results without asking an analyst to explain the dashboard.
Test the documentation, not just the product. When your on-call engineer needs to debug a flag assignment issue at 11 PM, the quality of troubleshooting docs matters more than the quality of the marketing site. During your pilot, intentionally break something (misconfigure a flag, send malformed events) and see how quickly the docs help you fix it.
Separate the feature flag decision from the experimentation decision. Some teams conflate these, but they are distinct capabilities. You can use LaunchDarkly for flags and Eppo for experiment analysis. You can use PostHog for both.
Evaluating them as a bundle when your needs are separable hides important tradeoffs. Score each capability independently, then consider the integration cost of split vs. bundled solutions.
Record your scoring rationale in writing, not just the numbers. Six months from now, you will not remember why you gave Statsig a 3 on Data Integration. A sentence of reasoning per cell transforms the scorecard from a one-time artifact into a living reference that supports future re-evaluation.
Check the platform's update velocity and roadmap transparency. Experimentation tools are evolving rapidly. A platform that ships meaningful improvements monthly and publishes a public changelog signals active development. A platform with a stale changelog may be deprioritizing experimentation in favor of other product lines. Review the last 6 months of release notes for each finalist.
Common Mistakes
Choosing based on a feature checklist instead of workflow fit
Correction
Feature checklists create a false sense of completeness. A platform might technically support sequential testing, but if the UI buries the setting three clicks deep and defaults to fixed-horizon, your team will never use it. The pilot experiment in Step 7 exists specifically to catch these gaps. If you skip the pilot and choose based on a checklist, you will discover workflow friction only after committing engineering time to a full integration.
Always run at least one real experiment on each finalist before deciding.
Comparing sticker price without modeling total cost of ownership
Correction
A free-tier platform with 40 hours of integration work costs more than a paid platform with 4 hours of setup. Teams frequently choose the cheapest platform on paper, then spend weeks on custom integrations, data pipeline adjustments, and workarounds for missing features. Model total cost over 12 months including engineering time, add-on costs, and maintenance hours. The cheapest-looking platform often turns out to be the most expensive when you include labor.
Evaluating only the experimentation feature while ignoring the rest of the platform bundle
Correction
PostHog includes analytics, session replay, and feature flags alongside experiments. If you are already paying for separate analytics and session replay tools, PostHog might replace three subscriptions with one. If you are already happy with your analytics stack and just need better experimentation, Eppo's warehouse-native approach avoids duplicating your analytics infrastructure. Evaluate the full stack overlap, not just the experimentation module.
Failing to do so leads to redundant tooling and fragmented data.
Assuming warehouse-native always beats bundled analytics
Correction
Warehouse-native tools like Eppo are powerful when your data team maintains a clean, well-modeled warehouse. But if your warehouse is messy, poorly documented, or maintained by one person who might leave, warehouse-native experimentation inherits all that fragility. A bundled tool like PostHog that manages its own data pipeline insulates your experimentation program from warehouse instability. Assess your warehouse maturity honestly before scoring the Data Integration dimension.
A warehouse-native tool on a shaky warehouse is worse than a bundled tool with its own pipeline.
Letting one team member make the decision alone
Correction
Platform choice affects engineers (SDK integration, flag management), data scientists (analysis methods, result interpretation), product managers (experiment creation, result sharing), and finance (budgeting). A decision made by engineering alone often overlooks PM workflow needs. A decision made by a PM alone often underestimates integration complexity. Require at least three stakeholders from different functions to review and validate the scorecard before finalizing.
Single-stakeholder decisions get challenged and relitigated within months.
Not setting a review date for the decision
Correction
The experimentation platform market changes rapidly. Eppo, Statsig, and PostHog all ship major features quarterly. A decision that was correct 12 months ago may be wrong today. Set a calendar reminder to revisit your scorecard every 6 months.
Update the scores based on new features, changed pricing, and your own evolving requirements. Without a review date, teams stay on suboptimal platforms for years out of inertia.
Other Skills in This Method
Running A/B Tests in the PostHog Experiments Tab
Step-by-step walkthrough of creating, launching, and monitoring an A/B test using PostHog's Experiments UI, including variant allocation and goal setup.
Setting Up PostHog Feature Flags for Experiment Variants
How to create and configure feature flags in PostHog to assign users to control and test variants in an A/B experiment.
Shipping the Winning Variant and Cleaning Up Feature Flags
How to roll out the winning experiment variant to 100% of users, remove the losing variant's code, and archive feature flags to keep your codebase clean after an experiment concludes.
Designing Experiment Hypotheses and Success Metrics for Onboarding
How to formulate a clear hypothesis, choose primary and secondary conversion metrics, and define what winning looks like before launching an onboarding A/B test.
Segmenting New User Cohorts for Onboarding Experiments
How to target experiments specifically to new users or sign-up cohorts using PostHog's person properties and cohort filters to avoid contaminating results with existing users.
Interpreting Bayesian and Frequentist Results in PostHog
How to read PostHog's experiment results dashboard, understand credible intervals vs p-values, and decide when an experiment has reached statistical significance.
Integrating PostHog A/B Tests with Webflow and Marketing Pages
How to implement PostHog experiments on no-code or marketing landing pages using the JavaScript snippet, Webflow custom code, and anti-flicker techniques.
Frequently Asked Questions
How long should the full evaluation of eppo vs posthog experiments take?
Plan for 2-4 hours of research and scorecard building, plus one week per finalist platform for the pilot experiment. A typical evaluation with two finalists takes about three weeks from start to decision document. Shortcutting the pilot is tempting but risky, since workflow friction only surfaces during hands-on use. If you are under time pressure, run pilots in parallel rather than sequentially.
Should I evaluate platforms before or after running my first experiment?
Run at least one experiment first using whatever tool you already have access to, even if it is a simple feature flag with manual metric tracking. Having firsthand experiment experience helps you evaluate platforms with informed priorities. The [PostHog Experiments Onboarding A/B Test Method](/methods/posthog-experiments-onboarding-a-b-test-method) is a good starting workflow for your first test. After that experience, you will know which parts of the process felt painful, and those pain points should heavily influence your scorecard weights.
Can I use PostHog for feature flags and Eppo for experiment analysis?
Yes, but verify the integration path. Eppo is designed to work with multiple flag systems, including PostHog's feature flags. The pattern is: PostHog assigns users to variants via its flag SDK, your application logs events to your warehouse, and Eppo reads those events for analysis. The main consideration is ensuring that the flag assignment data (which user saw which variant) reaches your warehouse in a format Eppo can query. Test this data flow during your pilot before committing.
What if my top two platforms score within half a point of each other?
When scores are that close, the tiebreaker should be the pilot experience. Which platform felt more natural during daily use? Which documentation was more helpful when something broke? Also consider the trajectory: check each platform's last six months of release notes. A platform with a strong recent improvement trajectory may surpass its competitor within a quarter. If scores are still tied after considering these factors, choose the platform with the lower switching cost, so you can change your mind with less pain.
How do I evaluate platforms my company is not willing to pay for yet?
Most platforms offer free tiers or trials. PostHog's free tier includes 1 million events per month with experiments. Statsig offers a free tier with limited feature flags and experiments. Eppo typically offers a trial period upon request. LaunchDarkly has a 14-day trial. Use these free options for your pilot experiments. If a platform has no free option and your company will not approve a trial budget, contact their sales team and explain you are evaluating alternatives. Most will offer a time-limited free evaluation, especially for teams with growth potential.
Why does my scorecard keep favoring the platform I already use?
This is status quo bias combined with familiarity advantage. Your current platform scores higher on Team Workflow because your team already knows it, and it scores higher on Data Integration because your pipelines already connect to it. To correct for this, score each platform as if your team were starting from scratch. What would you choose if you had no existing tool? Then add a separate 'migration cost' line item to the non-current platforms. This separates the platform's intrinsic quality from the switching cost, letting you see whether the current tool is actually best or just most familiar.
How often should I re-run this evaluation?
Set a formal review every 6 months and an ad-hoc review whenever a triggering event occurs. Triggering events include: your experiment volume doubles, your data infrastructure changes significantly (e.g., adopting a warehouse for the first time), a platform you evaluated ships a major feature you were missing, your team size crosses a pricing threshold, or your current platform announces a pricing change. Keep your scorecard in a shared document so re-evaluation is quick. Most re-evaluations take 1-2 hours if the original scorecard is well-documented.