Does Fluence Really Have Unlimited Bandwidth with No Egress Fees?

Cloud cost management is a nuanced challenge—especially when evaluating claims like “unlimited bandwidth with no egress fees”. Fluence, a decentralized cloud platform, markets itself with offers that catch attention, promising to eliminate some of the most common cost drivers on traditional public clouds.

But as a 12-year cloud infrastructure and SRE practitioner who’s run cost reviews across AWS, Azure, and Google Cloud, I’d caution you to dig deeper before embracing those statements at face value.

Understanding What “No Egress Fees” and “Unlimited Bandwidth” Really Mean

On the surface, “no egress fees” suggests you won’t pay for data leaving the Fluence network. “Unlimited bandwidth” sounds like you can push infinite data volumes without degradation or incremental cost.

While these claims may be accurate within Fluence’s ecosystem boundaries—e.g., inside their decentralized network—they typically don’t account for:

Data ingress and egress costs from/to public clouds or the internet
Underlying compute and storage consumption required to handle network traffic
Performance constraints imposed by hardware, virtualization, and network topology

In other words, the promise of “no egress fees” can be true if you’re operating purely inside Fluence’s environment, but don’t assume it means your overall bandwidth needs vanish or become costless.

Why Always-On Small Services Often Hide Cloud Waste

From my experience auditing cloud costs, many organizations run a fleet of “small” or low-CPU services that operate 24/7. These always-on lightweight processes often fly under the radar but cumulatively rack up substantial charges.

The problem is that “small” in cloud terms is subjective—running many small instances that don’t fully utilize their CPUs still leads to paying full instance-hour costs, and egress traffic charges continue per gigabyte.

Plus, cloud providers charge for storage attached to these services and any data that crosses network boundaries. When your architecture is spread over multiple regions or clouds, egress charges can multiply, even if Fluence claims “no egress fees” within its scope.

Shared CPU Definitions Differ by Cloud Provider

One area that trips up many engineers evaluating performance and cost is the assumption that the number of vCPUs is a direct proxy for computation power and uptime guarantees. It’s not.

Cloud Provider Shared CPU Definition Performance Implications AWS “T-family” instances share physical cores with burstable CPU credits CPU credits replenish over time but can burst temporarily; consistent peak usage may lead to throttling Azure B-series VMs use accrued credits and shared scheduler placement Similar credit system; performance can be unpredictable under sustained loads Fluence Shares compute resources among nodes in a decentralized network Resource availability decisions depend on peer load and network state

Thus, “unlimited bandwidth” on a shared rightsizing vs autoscaling CPU node might be limited by how credits are consumed or neighboring peers’ load, which impacts peak throughput and latency.

Measure Peaks with the Right Observation Window

During cost and performance reviews, I always ask: “What do the P95 and P99 of CPU and network usage look like?” Why? Because average utilization what is cloud waste values hide spikes and can mislead sizing decisions.

For example, an instance might show a 30% average CPU usage, but if the spikes reach 95–99 percentile above 90%, that means the service experiences high transient loads that need headroom.

AWS tools like Compute Optimizer and Microsoft’s Azure Advisor incorporate percentile metrics in their recommendations, but almost no one outside those tools systematically reviews spike duration and peak percentiles before resizing.

If you’re evaluating Fluence’s resource model or AWS/Azure cloud sizing in the context of bandwidth and cost:

Use an observation window that captures traffic patterns over days or weeks (not just minutes)
Review P95, P99 latency and bandwidth percentiles instead of simple averages
Identify how long spikes last to understand whether short burst capacity suffices or sustained throughput is necessary

Use Percentiles and Spike Duration, Not Averages, to Spot Cost-Waste and Performance Bottlenecks

Averages smooth traffic and compute consumption, hiding intermittent peaks that cause throttling, request queuing, or egress traffic surges—and thus cost overruns.

Missed spike sizing can lead to:

Overprovisioning — wasting money on idle CPU and bandwidth for rare events (“just in case”)
Underprovisioning — causing latency spikes, failed requests, or repeated retries that increase total bandwidth usage upon retries

To optimize cost and performance, capture:

P95/P99 CPU and network bandwidth metrics to understand high-load scenarios
Spike duration to decide if capacity needs to be sustained or can be supported by burstable credits/temporary scaling
Data ingress vs. egress volume, especially if egress fees apply outside Fluence’s network

This approach aligns with recommendations from AWS Compute Optimizer and Azure Advisor, which guide customers to right-size based on peak loads, not averages.

Fluence Billing: What’s Behind “Unlimited Bandwidth”?

Fluence uses a decentralized approach distributing workloads across a network of peers. Their billing tends to be structured around compute and storage usage within the Fluence ecosystem. Here’s what you need to remember:

Data staying inside the Fluence network often avoids public cloud egress fees.
Bandwidth to and from external cloud regions or the public internet may still incur costs outside Fluence’s billing.
Compute and storage resources allocated to process that traffic still need to be paid for, so “unlimited bandwidth” is only within the context of those limits.
Always-on coordination and peering services can create hidden costs in the form of persistent connections and replicated data storage.

This means when assessing your true costs—including egress—you should not rely solely on headline Fluence marketing but instrument your workloads to:

Measure raw outbound bandwidth over time
Map which data crosses Fluence boundaries to public clouds or internet
Analyze cumulative network usage and associated charges on any underlying public cloud provider

Practical Steps: What Worked in My Experience Across AWS, Azure, and Decentralized Clouds

Instrument real usage for long observation periods. Use native cloud tools and third-party monitoring that capture P95, P99 metrics on CPU and network. For example, CloudWatch and Azure Monitor can output these percentiles, not just averages.
Understand the service’s traffic profile. Measure burst duration, typical peak hours, and idle time. This helped me propose switching from burstable CPU families (like AWS T-series or Azure B-series) to standard fixed CPU types during sustained workloads.
Review indirect egress costs. In multi-cloud or hybrid setups, identify ingress/egress patterns to avoid surprise bills. A “no egress fees” promise internal to Fluence doesn’t eliminate charges when data flows to external systems.
Write rollback criteria before any pilot migration. I always document acceptable P95 latency thresholds, bandwidth usage limits, and cost deviation limits. If exceeded during testing, revert the changes.
Use cloud-native recommendation tools cautiously. AWS Compute Optimizer and Azure Advisor provide great starting points but always validate recommendations against your real workload spike patterns and SLA needs.

Summary: Take the Marketing with a Grain of Salt and Measure Everything

The catchy promise of “unlimited bandwidth” and “no egress fees” by Fluence or other providers is enticing but can mislead if taken out of context. Real-world cloud infrastructure demands careful measurement of your workload’s peak demands, CPU resource sharing nuances, and egress traffic patterns.

Avoid cost surprises and performance issues by:

Measuring P95 and P99 CPU and bandwidth usage over weeks, not relying on averages
Understanding how shared CPU models differ between AWS, Azure, and decentralized clouds like Fluence
Recognizing always-on small services often mask cost inefficiencies
Factoring in all network charges including external cloud egress, not just internal network claims

Leveraging cloud tools like AWS Compute Optimizer and Azure Advisor with a focus on percentiles and spike duration, coupled with a solid rollback plan for pilots, will save you cost and headache in the long run.

In cloud infrastructure, measurement and clarity beat clever marketing every time.