AI Model Deployment Services
Introduction - AI model deployment
This is specifically about taking a model that already exists - one your team trained, or a vendor/open-source model - and getting it running reliably in production: a proper inference API, versioning so you can roll back a bad deployment, and monitoring so you know when it starts drifting or failing. It's not about building the model itself.
If you also need the training/pipeline work done, that's covered separately under our MLOps pipeline service - this page is for teams who already have a model and need it served properly.
Where deployment problems actually happen
Most model failures we get called in for aren't bad models - they're deployment problems: no versioning so a bad update can't be rolled back, no monitoring so degraded performance goes unnoticed for weeks, or an inference setup that can't handle real production load. We treat deployment as its own discipline, not an afterthought once training is "done."
We're honest about infrastructure costs too - GPU inference isn't free, and we'll help you right-size hosting rather than over-provision for traffic you don't have yet.
What's included
- Production inference API with proper request/response handling
- Model versioning with rollback capability
- Performance and drift monitoring with alerting
- Load testing sized to your actual expected traffic
- Cost-optimized hosting recommendation (cloud GPU vs CPU vs managed inference)
- Documentation for your team to deploy future model updates themselves
Our process
1. Review your model & requirements
We assess your existing model, expected traffic, and latency requirements before recommending an inference architecture.
2. Build & load test on staging
The inference API, versioning and monitoring get built and tested under realistic load before touching production.
3. Deploy & monitor
Production rollout with monitoring active from day one, plus a handover so your team can deploy future model versions independently.
Pricing
Cost depends mainly on expected traffic and latency requirements, not model complexity itself. Range: Rs 60,000 - 400,000 - indicative, final quote after discovery. Request a quote or WhatsApp +92 324 2991303.
Industries we serve
Teams with an existing trained model (in-house data science, or a fine-tuned open-source model) needing reliable production serving without building MLOps infrastructure from scratch.