How to choose a cloud provider for AI when AWS is spending big

If you need to choose a cloud provider for AI, Amazon’s latest public push matters. According to TechCrunch, AWS CEO Andy Jassy said Amazon expects about $220 billion in cash capex in 2026 (the majority for AWS) and plans to spend meaningfully more in 2027 as it builds AI infrastructure. Jassy told investors he believes AWS can grow rapidly — AWS was at a $184 billion annualized revenue run-rate and grew 39% year-over-year — and he forecasted roughly 40% growth in each of 2027 and 2028 (TechCrunch).

Investors showed concern: TechCrunch reports shares fell about 6% after-hours, and analysts pressed Jassy on returns and margins. The article also noted that other cloud providers are similarly investing heavily (TechCrunch).

What this shift means for SMBs choosing a cloud provider for AI

AWS’s willingness to spend heavily on capacity and AI tooling changes the market dynamics SMBs face. More capacity and new managed AI functionality can lower the technical barrier to using AI: expect more pre-built models, embeddings, fine-tuning options, and orchestration services from major clouds. That reduces upfront engineering needed to get value from AI, but it also accelerates vendor-specific feature sets that increase vendor lock-in.

At the same time, heavy capital investment doesn’t erase commercial questions. Providers can invest to gain scale and then offer competitive pricing or bundled services — or they can prioritize recouping costs through premium managed offerings. TechCrunch reports AWS is banking on growth to justify the spending, but investors are watching margins closely (TechCrunch).

Who is most affected and the main trade-offs

• Fast-growing SMBs and product teams that want to add AI features quickly will benefit from richer managed services and lower time-to-market. Those managed services can replace months of engineering work.
• Cost-sensitive SMBs and tightly budgeted IT teams will need to watch variable consumption costs, long-term commitments, and potential price volatility as providers compete or seek returns.
• Businesses with strict data residency, compliance, or portability needs face trade-offs: relying on cloud-native AI services simplifies operations but makes future migrations harder.

Key trade-offs you should weigh: speed vs. portability, simplicity vs. long-term cost predictability, and single-vendor convenience vs. multi-cloud resilience.

Practical checklist for evaluating cloud partners or a hybrid approach

• Define outcomes first: prioritize which AI use cases deliver measurable business value (automation, recommendations, document automation, etc.).
• Map expected usage: estimate training, inference, storage, and egress needs; model both typical and peak workloads.
• Cost testing: run a small POC to measure real consumption and model 12–24 month costs under different growth scenarios.
• Managed features: inventory managed AI services (model hosting, fine-tuning, vector DBs, MLOps) and estimate the engineering hours they save.
• Portability plan: require clear export formats, containerized inference, and avoid provider-only APIs for core business logic to reduce lock-in risk.
• SLA and support: validate SLAs, support tiers, and marketplace integrations that affect operations and downtime risk.
• Negotiate capacity and pricing: consider reserved capacity, committed use discounts, and enterprise support terms to stabilize costs.
• Hybrid readiness: if you need on-prem or edge, check the provider’s hybrid tooling and your ability to run inference locally or in colocation.

How to decide between single-cloud, multi-cloud, or hybrid

• Single-cloud makes sense when speed to market and integration with provider tools matter more than portability.
• Multi-cloud suits risk-averse teams that can afford extra complexity for redundancy or to avoid dependency on a single vendor.
• Hybrid is a pragmatic middle path when latency, data residency, or cost control require local execution and cloud bursting.

For most SMBs, a staged approach works best: prove value with managed services, measure costs and lock-in risk, then factor portability into the architecture before scale. If you want to offload implementation risk, combine provider services with an engineering partner that can keep your systems portable and secure.

If you’re evaluating managed AI integration or need production-ready hosting and maintenance, consider pairing AI features with solid DevOps and support to keep costs predictable and uptime high — we build AI assistants and automation that plug into existing business systems (AI & automation services) and offer cloud hosting, monitoring and long-term maintenance (DevOps & support).

Talk to the team if you want help scoping a POC, modelling cloud costs for AI workloads, or designing a hybrid architecture that balances speed, cost and control. Book a call at /contact.