Reducing AI Costs Across AWS, Azure, and GCP
AI infrastructure costs are one of the fastest-growing line items on enterprise cloud bills. With models getting more capable (and more expensive), optimizing spend without sacrificing quality is a critical skill for engineering teams.
Here are proven strategies for reducing AI costs across AWS, Azure, and GCP.
1. Right-Size Your AI Models
Not every request needs GPT-4 or Claude 3 Opus. Many workloads — classification, extraction, summarization — can be handled effectively by smaller, cheaper models.
Strategy: Implement a routing layer that directs requests to the most cost-effective model based on task complexity. Simple queries go to GPT-3.5 or Claude Haiku; complex reasoning goes to premium models. A platform like Bonito makes this routing automatic.
Typical savings: 40-60% reduction in per-request costs.
2. Use Provider-Specific Pricing Advantages
Each cloud provider has different pricing structures and advantages:
- AWS Bedrock offers provisioned throughput pricing that can be 30-50% cheaper for predictable workloads
- Azure OpenAI provides enterprise agreements with volume discounts
- GCP Vertex AI offers sustained use discounts and committed use contracts
Strategy: Route workloads to the provider with the best pricing for each specific use case. Multi-cloud routing is one of Bonito's core features.
3. Implement Semantic Caching
Many AI requests are repetitive. If you're generating the same embeddings or answering similar questions, caching responses can dramatically reduce costs.
Strategy: Deploy a semantic cache layer that identifies similar requests and returns cached responses when confidence is high.
Typical savings: 20-40% reduction in total API calls.
4. Set AI Spend Budgets and Alerts
It sounds obvious, but most teams don't have real-time visibility into AI spend. A runaway process or unexpected traffic spike can generate thousands in charges before anyone notices.
Strategy: Use a platform like Bonito to set budget thresholds per provider, per team, and per application. Get alerts before you exceed them — not after.
5. Optimize Token Usage for Cost Efficiency
Token costs add up fast. Prompt engineering isn't just about quality — it's about efficiency.
Strategies:
- Trim unnecessary context from prompts
- Use system messages efficiently
- Set appropriate max_tokens limits
- Use streaming to detect early when a response is going off-track
6. Negotiate Enterprise Agreements
If you're spending more than $10K/month with any single provider, you likely qualify for volume discounts. Most providers have enterprise tiers with:
- Lower per-token pricing
- Committed use discounts
- Priority access and higher rate limits
- Dedicated support
Putting It All Together
The most effective AI cost optimization strategy combines multiple approaches. Bonito's cost intelligence features help you identify opportunities across all your providers, track savings over time, and ensure you're always routing to the most cost-effective option.
Start with visibility (know what you're spending), then optimize routing, then negotiate. Most teams find 30-50% savings within the first month.
Read about why multi-cloud AI management matters for more context on building a resilient, cost-effective AI strategy.