How one AI platform reduced inference costs by 40% by moving beyond OpenAI

Challenge: AI operational costs were growing faster than revenue

A mid-stage SaaS company that provides AI powered customer service automation faced an increasingly common challenge.

Their platform relied heavily on proprietary large language model APIs to power several core features, including:

• intent classification

• sentiment analysis

• automated response generation

• knowledge base summarization
  
  

Initially, the architecture worked well. The team integrated OpenAI models quickly and launched their product without needing to manage infrastructure or model training.

However, as customer adoption increased, the economics of the system began to change.

The company was processing several million AI requests per month across customer support workflows. Token based pricing meant their inference costs grew directly with usage. Within a year, monthly model costs had become one of the largest operational expenses for the platform.

According to the company’s CTO:

“Our AI operational costs were growing at an unsustainable rate relative to revenue. The product was working, customers loved it, but the economics became difficult to scale.”

The engineering team began evaluating alternatives that could reduce costs without compromising reliability or model performance.

Evaluation: exploring open-weight model infrastructure

The team explored a range of options, including optimising their existing API usage, switching to alternative managed model providers, and deploying open-weight models.

Open-weight models provide access to trained model parameters, allowing organisations to run them on their own infrastructure or through specialised inference platforms. Unlike proprietary APIs, this approach gives teams more control over deployment and cost structure.

The evaluation focused on four criteria:

1. Cost efficiency for high volume inference workloads

2. Model performance for customer support use cases

3. Migration effort for existing production systems

4. Operational complexity of running models in production
   
   

After testing several model families, including recent open-weight models such as Llama based chat models, the team determined that performance for their use cases was comparable to the proprietary models they were currently using.

The remaining question was infrastructure.

Running models internally would require significant GPU investment and operational overhead. Instead, the company evaluated AI infrastructure platforms that host open-weight models while providing developer friendly APIs.

These platforms typically expose endpoints that follow the same structure as OpenAI’s API, which can reduce integration work for existing applications.

Implementation: migrating a production workload

The engineering team chose to migrate one component of their system first, the automated response generation service used by their chatbot.

The migration process involved several steps.

First, the team benchmarked different models using historical customer support conversations. They evaluated response quality, latency, and consistency.

Second, they implemented a parallel inference pipeline that allowed both the existing API and the open-weight model endpoint to run simultaneously for testing.

Third, they updated their API configuration to route requests through the new endpoint. This required minimal code changes, primarily updating API endpoints and model parameters.

The CTO explained:

“We expected a major refactoring effort. In practice the code changes were relatively small because the new endpoint followed the same API structure we were already using. Most of the work went into testing and prompt tuning.”

The testing phase lasted several weeks while the team monitored response quality, latency, and operational stability.

Results: lower costs and greater infrastructure flexibility

After deploying the new architecture, the company gradually expanded the use of open-weight models across several services.

Over time they observed several benefits.

Lower inference costs

Based on their usage patterns of several million requests per month, the company reduced their AI inference costs by approximately 40 percent.

This reduction came primarily from running high volume inference workloads on dedicated infrastructure rather than token priced APIs.

The exact savings depended on workload characteristics and would likely differ for other organisations.

Comparable model performance

For their customer service use cases, the selected open-weight models produced responses comparable in quality to the proprietary models previously used.

Some prompts required tuning to match previous behaviour, but the engineering team considered the overall performance acceptable for production use.

Greater control over infrastructure

Running models through a specialized inference platform allowed the team to control where workloads were executed and how models were configured.

This flexibility also allowed them to experiment with fine tuning models on anonymized customer support data.

Gradual migration across services

Following the successful rollout, the company migrated several additional features to the same infrastructure while keeping some workloads on proprietary APIs.

This hybrid architecture allowed the team to balance cost, reliability, and performance.

Lessons learned from the migration

The team identified several important takeaways from the process.

First, model evaluation is essential. Even when models perform similarly in benchmarks, prompt behaviour and output style can differ.

Second, migration requires testing time. Although the API integration was relatively straightforward, quality evaluation and prompt adjustments required careful iteration.

Third, not every workload needs to move. Some specialised tasks continued to use proprietary APIs because they delivered better results.

Finally, infrastructure matters as much as model choice. GPU optimization, batching strategies, and inference frameworks significantly affect performance and cost.

What this means for other engineering teams

As AI workloads grow, many organisations are rethinking the infrastructure behind their applications.

Open-weight models and specialized inference platforms are expanding the options available to engineering teams. They provide greater control over deployment and cost structure, particularly for high volume workloads.

However, migration is rarely a simple technical change. It involves model benchmarking, infrastructure evaluation, and careful testing before production rollout.

Organisations facing similar AI cost challenges may find value in evaluating open-weight alternatives alongside proprietary APIs to determine which combination best fits their workloads.