Practical notes from the engineers who build agents, copilots and AI products.
Recommendation engine development costs $21,000 to $70,000, or ₹13,60,000 to ₹46,40,000, at Eazyware's published rates. The spread is driven by how many surfaces you personalise, whether an event pipeline already exists, and how many catalogue and identity systems have to be integrated before ranking can begin.
Recommendation engine development runs in five phases: pick one surface and one objective, build the event backbone, ship a two-stage retrieve-and-rank pipeline with business rules, design cold start deliberately, then prove lift with a holdout before widening. The order matters more than the algorithm you choose.
The ROI of recommendation engine development is incremental margin measured against a holdout group, divided by build plus twelve months of running cost. Anything measured without a holdout is not ROI, it is a correlation. Most defensible cases pay back within nine to eighteen months on the published price range.
Recommendation engine development takes six to eight weeks for a first surface when a usable event stream exists, ten to fourteen weeks for a production programme, and fourteen to twenty for a multi-surface platform. The critical path is almost never the model; it is the event pipeline and the measurement window.
Recommendation engine development fails for five repeatable reasons: unreliable event data, no controlled test, an unsolved cold start, a latency budget nobody owned, and a model nobody retrained. All five are decided before any modelling starts, which is why they are preventable rather than unlucky.
A recommendation engine development RFP should specify the surfaces, the event data you already have, the metric and holdout design, the latency budget, the cold-start rule, retraining ownership and the data rules. Without those seven, bids price different projects and cannot be compared.
The hidden costs of recommendation engine development are event instrumentation and repair, embedding and inference spend, vector and feature storage, experimentation tooling, retraining effort, merchandising rule upkeep and support. None appear on a build quote, and all of them recur every month after launch.
Recommendation engine development for startups is constrained by data volume and runway, so the right build is one surface, simple models and a fast controlled test. Enterprise builds are constrained by integration surface and governance, so the work shifts towards approvals, isolation and change management.
Recommendation engine development in India costs $21,000 to $70,000, or ₹13,60,000 to ₹46,40,000, for a production system. Indian delivery gives you fixed-price scope and IST overlap; what it does not give you automatically is a clean event pipeline, a holdout test or a residency decision.