Your CRM vendor just sent an amendment to your contract. Buried in the legal language are new metered charges for API calls your analytics tool makes to access customer data.
SaaS vendors are shifting from flat subscriptions to consumption-based models that meter API usage, data movement, and integration activity. Salesforce adjusted connector pricing in 2025 after nearly a decade of flat fees.
SAP faces federal antitrust litigation alleging restrictions on customer data access, with most claims allowed to proceed. Two-thirds of IT leaders report unexpected charges from these new pricing structures.
For mid-market companies, this is forcing architectural and vendor decisions that weren’t on anyone’s roadmap.
Here’s where the fees hit hardest and what you can do about it.
Where the fees materialize in your stack
Integration partners absorb new costs, then pass them through. Salesforce adjusted connector economics in early 2025 after nearly a decade of stability.
Partners reported pressure to operate through AppExchange, changing how data extraction gets priced. You see this as vendor price increases for tools you’ve used for years.
Small fees accumulate faster than budgets track. Mid-market audits from 2025 show companies paying for overlapping connector services and API overages that individually looked minor but collectively reached double-digit percentages of SaaS spend.
What you can do
Map API usage to actual business value. Audit endpoints and frequencies, then identify high-volume integrations with low operational impact.
Renegotiate data access terms explicitly, not as part of general renewals. Push for caps on annual increases tied to usage growth, with advance notice requirements for new connector charges.
Build direct connectors where vendors permit to reduce dependency on marketplace-controlled integration layers.
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AI workloads amplify the meter
AI initiatives continuously consume data, which changes SaaS integration economics. Teams budget for training costs, inference compute, and storage, but often miss the cost of feeding AI systems with fresh SaaS data.
In multiple mid-market deployments, API quotas assumed to be included were exhausted weeks into production. Zylo’s 2025 benchmark found that organizations’ spending on AI-native apps jumped 75% year-over-year, with two-thirds of IT leaders reporting unexpected charges due to consumption-based pricing.
The disconnect is structural. Subscriptions forecast as stable OPEX. API-driven AI workloads are variable and hard to cap. Unlike cloud infrastructure, you can rightsize; you can’t throttle an AI application that needs frequent data refreshes to function.
What you can do
Model data access costs per AI use case before deployment. Estimate API usage under different modes: real-time sync versus batch, full datasets versus deltas, and price each scenario with vendors upfront. Manufacturing deployments reduced integration costs by shifting to multi-hour batch windows without affecting model quality.
Insert caching layers between SaaS systems and AI workloads to avoid hitting vendor APIs repeatedly, giving you control over refresh cadence.
Negotiate AI-driven usage volumes early. Some vendors offer volume tiers or flat-rate pricing for predictable high-usage scenarios, but rarely after per-call pricing locks in.
You’re paying multiple tolls to move the same data
Cloud providers charge for data leaving their platforms. SaaS vendors increasingly charge for data leaving theirs. When both apply, the same dataset triggers multiple fees across a single pipeline.
This surfaces when moving operational data from SaaS into cloud analytics environments. Organizations report paying API charges to extract from SaaS, pipeline costs to process it, and, if native vendor integration is involved, additional licensing for ingestion. What budgets of $100,000 annually can reach $300,000 at production scale.
IDC warns that integration tooling and hidden SaaS fees significantly inflate total software spend, eroding savings from best-of-breed architectures.
What you can do
Map data flows to identify stacked tolls. Document every billing boundary SaaS to cloud, SaaS to SaaS, cloud to SaaS. Many organizations discover they’re paying multiple vendors for the same data movement.
Architect for data gravity where it makes sense. If analytics can run natively within a SaaS platform, it may be cheaper than continuous extraction.
Prefer integration paths with transparent, usage-based pricing over marketplace-controlled connectors that compound as volumes scale.
Your contracts don’t reflect these changes yet
Most SaaS contracts signed in 2022–2023 assumed API usage was bundled or governed by loose “fair use” terms. Those assumptions are being quietly unwound.
Pricing changes often arrive through partner program updates rather than customer contract renegotiations. When integration vendors adjust pricing or pass through new costs, the changes show up as budget variances.
CIO Dive reported that 56% of enterprises said unexpected data costs caused cloud project delays because unanticipated integration fees stalled approvals.
In several mid-market renewals, teams discovered amendments introducing per-call fees for integrations they’d been using for years. Once at steady state, integration spend ran materially over budget without delivering incremental value.
The leverage window exists before renewal, but finance and engineering teams often don’t compare notes until the surprise bill arrives.
What you can do
Review contracts for data-access amendment clauses allowing vendors to introduce pricing for previously unmetered capabilities.
Negotiate grandfathering or volume caps before changes take effect. Vendors are more flexible before new pricing is enforced than after.
Maintain architectural optionality to avoid pricing pressure eroding negotiating leverage. That doesn’t mean migrating everything, it means designing integrations with enough flexibility that switching remains credible.
The shift from subscription-only to usage-based SaaS pricing is permanent. The organizations handling this best treat data access as an architectural and financial concern.
They map data flows before signing contracts, model API costs per use case, and build integration layers they control.
If connector fees are creeping into your SaaS bills, audit usage patterns now before next year’s budget assumes costs that no longer exist.
