Build vs Buy a Recommendation Engine
We sell one side of this decision, so we start with the case against us. Prices are real and dated. The total cost of building is yours to work out, because it depends on your team.
Build a recommendation engine when recommendations are your product's edge and you have the interaction data and engineers to run it. Buy when personalization supports the product. TasteRay's API costs $0, $49 or $199 a month, with custom Enterprise terms, while vendors that train on interactions need that data first.
A published "total cost of ownership" figure describes someone else's company, so you will find none here. You get the conditions that decide it, the prices vendors publish, and the list of costs to fill in with your own numbers.
When to build it yourself
If most of these describe you, build. We would rather tell you now.
- It is your moat
- Viewers or shoppers choose you because of your recommendations, and a competitor buying the same engine would close the gap.
- You have the data
- You hold enough interaction history for collaborative filtering to beat a good editor, and you have the rights to use it.
- You need it instantly
- Every row on every screen has to answer in under a second, for every viewer, on every page view. A call that generates text will not do that.
- You have the people
- Engineers who can train, evaluate and retrain models are already on staff, and will still be there in two years to keep it running.
- Your data cannot leave
- Your policy rules out sending viewer context to a third party and its AI model providers.
When to buy
- Discovery supports the product
- People come for your catalog or your prices, and better recommendations help them without being the reason they came.
- You lack the history
- You are new, niche or moving into a new category, so a model trained on interactions has too little to learn from.
- You want to test demand first
- A separate tab with a bought engine tells you whether viewers use mood-based discovery before you staff a team for it.
- You want reasons, not only order
- A written reason per pick is a second system to build on top of a ranker. TasteRay returns it in the same response.
What vendors publish
These are list prices, read from each vendor's own page on 5 November 2026. They do not measure the same thing, so read the unit before you compare. A TasteRay request generates text for up to ten results. An Amazon Personalize request returns a ranked list and sits alongside separate training and ingestion charges. Prices for Vionlabs, ThinkAnalytics and XroadMedia are not listed on their sites (as of Nov 2026).
| Vendor | Published price | What it covers | Source |
|---|---|---|---|
| TasteRay API | Free; $49 a month; $199 a month; Enterprise custom | 1,000, 10,000 and 100,000 requests a month | api.tasteray.com |
| Recombee | Free; from $99 a month; from $1,699 a month; from $4,499 a month; Platinum custom | Free under 100,000 interactions and requests a month. Paid tiers scale with usage | recombee.com/pricing |
| Amazon Personalize | $0.15 per 1,000 real-time requests and $2.00 per million interactions trained on v2 recipes; $0.05 per GB ingested | Pay as you go, with a minimum 1 TPS charge. Free tier for the first two months | aws.amazon.com/personalize/pricing |
| Algolia Recommend | Free with 5,000 requests a month; Grow plans include 10,000, then $0.60 per 1,000; Elevate custom | Recommendation requests within an Algolia plan | algolia.com/pricing |
What each option needs before the first result
| Vendor | Needed to start | When to choose it | Source |
|---|---|---|---|
| TasteRay API | A sentence or a list of preferences, and optionally your items | You have little history, or you want a written reason with every pick | API docs |
| Recombee | A catalog. Interaction history is optional, and the docs call interactions the most important data | You have interaction data and a large catalog, and want updates within milliseconds | How it works |
| Amazon Personalize | At least 1,000 interaction records and 25 users with two interactions each. AWS recommends 50,000 from 1,000 or more users | You run on AWS with engineers for datasets and retraining | AWS FAQ |
| Algolia Recommend | From 250 to 10,000 events depending on the model | You already use Algolia search on a commerce catalog | Algolia docs |
The costs to count for yourself
Each line is a real cost that buyers tend to leave out. Put your own salaries and cloud bills next to them. A number written here would be a guess about your company.
- Engineers to build the ranker, and the share of their time it takes after launch.
- Training, inference and storage: the vector store, the GPUs if you need them, the evaluation runs.
- Instrumentation to collect interactions, plus the months before you have enough.
- An evaluation harness, and the on-call rota for when the model degrades quietly.
- Writing the reason for each pick, if you want one.
- On the buying side: the vendor bill at your real volume, your cache, your integration, and the work of leaving.
Leaving is part of the decision
If you buy from TasteRay and later build in-house, the response shape is the integration: your front end keeps rendering why_match and key_factors, and you swap what fills them. Keep your candidate selection, cache and rejection memory on your side, as the guide does, and most of your work survives the move.
Other vendors' prices and requirements are as their public pages stated them on 5 November 2026 and may have changed. Check their pages before you rely on them.
Sources and further reading
FAQs about Build vs Buy a Recommendation Engine
How much does it cost to build a recommendation engine?
No honest single figure exists, because the cost depends on your team, your data and how much you need it to do. Count engineers, training and inference, instrumentation, evaluation, on-call time and the months spent collecting interactions. Any published total was calculated for someone else's team, so use it as a list of questions.
Can I start with an API and build my own engine later?
Yes, and it is a sensible order. Keep candidate selection, caching and rejection memory on your side from the first day, and treat the response fields as the contract your front end renders. When you build in-house, you replace what fills those fields and keep the rest of your integration.
Do I need interaction data to buy a recommendation engine?
It depends on the vendor. Amazon Personalize documents a minimum of 1,000 interaction records, and Algolia sets minimums per model. Recombee treats history as optional but important. TasteRay reasons from the context in each request, so a sentence is enough to start, and Enterprise can train on your content later.