AWS Graviton vs AMD vs Intel: Price-Performance, Workload Fit, and EDP Leverage

EC2 instance pricing in 2026 reflects three distinct processor lineages — AWS-designed Graviton (Arm), AMD EPYC (x86), and Intel Xeon (x86) — each available in general-purpose, compute-optimized, memory-optimized, and storage-optimized families. The right processor choice is a much bigger cost lever than most enterprises treat it as. For an EC2-heavy estate, moving 50% of suitable workload from Intel m5 / m6i to Graviton m7g typically reduces compute spend on those workloads by 20% to 40%, before any commitment discount. Stack that on Savings Plans and EDP and processor strategy becomes one of the cleanest cost-reduction levers in the AWS portfolio.

The three processor lineages on EC2

AWS Graviton (Arm)

Graviton is AWS's in-house silicon program, built on Arm architecture. Current generations of relevance:

• Graviton2 (used in m6g, c6g, r6g, t4g families) — first widely-deployed generation, still in service for low-utilisation workloads.
• Graviton3 (m7g, c7g, r7g) — typical workhorse choice, materially better than Graviton2 across most benchmarks.
• Graviton4 (r8g, with broader family rollout continuing) — newest generation with the strongest per-vCPU performance and highest density.

Graviton's commercial value comes from AWS's silicon margin: AWS designs the chip, fabricates it through TSMC, and captures the margin that Intel and AMD would otherwise capture as silicon vendors. AWS passes a portion of that margin to customers as list-price reduction, which is why Graviton list prices sit meaningfully below comparable Intel and AMD instances.

AMD EPYC (x86)

AMD EPYC instances on EC2 (the *a suffix — m5a, m6a, m7a, c6a, c7a, r6a, r7a) sit between Graviton and Intel on price. AMD's competitive posture against Intel has improved significantly across the last two EPYC generations, and AWS prices AMD instances roughly 10% below comparable Intel instances at list. AMD instances run pure x86, so the workload-compatibility surface area is identical to Intel — there is no migration friction within x86.

Intel Xeon (x86)

Intel Xeon is the historical default on EC2. The m5, m6i, m7i families have been the workhorse for most enterprises since EC2 launched. Intel still leads in raw single-thread performance for some workloads, has the broadest ISA support (including AVX-512 in newer generations), and is the default for many legacy workload patterns where binary compatibility, BIOS-level features, or vendor support matrices favour Intel.

List-price comparison by family

Approximate list pricing for general-purpose 4xlarge instances (16 vCPU, on-demand Linux, US East 1) as of 2026:

The price geometry alone tells most of the story: Graviton sits 15% to 20% below equivalent Intel, AMD usually 10% below current-generation Intel, and newer AMD families (m7a) carry a price premium that reflects their absolute performance leadership in some benchmarks. But list price is only half the equation — what matters is price-performance, and that's where Graviton's advantage compounds.

Price-performance, not just price

Per-vCPU performance varies by workload. Generalised benchmark patterns we see in practice across web tier, application tier, batch compute, and database workloads:

• Graviton3 vs m6i: typically 25% to 40% better price-performance for ARM-native workloads (Linux web/app tiers, Java, Go, Python, Node, modern PHP, Rust).
• Graviton3 vs m6i for memory-bound workloads: closer to 20% advantage, sometimes neutral when memory-bandwidth-bound.
• AMD EPYC (Genoa) vs Intel (Sapphire Rapids): AMD ~10% better price-performance for most general-purpose workloads, occasionally tied or behind for AVX-512-heavy workloads.
• Intel Sapphire Rapids vs older Intel: AVX-512 and AMX make Intel categorically faster for specific AI inference, encoding, and crypto workloads.

The takeaway is that Graviton's price-performance advantage is largest for the workloads most enterprises are running — Linux web tier and application tier services — and shrinks for specialised workloads. For a generic three-tier web app, Graviton is almost always the right answer. For an AVX-512-optimised inference service, Intel may still be the right answer.

Workload fit map

Workloads that move cleanly to Graviton

• Linux web tier — Nginx, Apache, modern Node, Python, Go.
• Containerized microservices on EKS or ECS where images are multi-arch or rebuildable.
• Java workloads — JVM Arm support has been mature for years.
• Open-source databases — Postgres, MySQL, Cassandra, Redis, MongoDB all run well on Graviton.
• Build farms — most modern CI/CD pipelines support multi-arch builds.
• Data processing — Spark, Kafka, Flink all run well on Graviton.

Workloads with friction on Graviton

• Closed-source proprietary applications without Arm binaries — common in legacy ISV stacks.
• Windows workloads — Windows on Graviton exists but adoption is still narrow.
• Specific AVX-512 / AMX-optimised inference and encoding workloads.
• Workloads bound to specific licensing models that count by core in ways that disadvantage Arm.
• Specific commercial database editions where vendor support on Arm is restricted.

Where AMD is the right choice

AMD is the right choice when x86 binary compatibility is mandatory but Intel-tier pricing is unjustified. This is most enterprise legacy stacks. AMD on EC2 is essentially "Intel binary compatibility at a discount" — for many enterprise workloads it is the path of least friction and material cost saving without the rebuild required for Graviton.

Where Intel is the right choice

• Workloads using AVX-512 and AMX heavily (some AI inference, video encoding, scientific computing).
• Workloads bound to Intel-specific BIOS or instruction features.
• Vendor-supported third-party applications where Intel is the only certified architecture.
• HPC workloads heavily optimized against specific Intel toolchains.

The realistic migration economics

Migration friction costs are real and need to be in the economic model. A defensible Graviton migration program at a mid-sized AWS estate ($10M–$30M annual) typically looks like:

• Year 1: identify the 50%–70% of compute spend that is Graviton-eligible. Rebuild container images for multi-arch. Migrate the cleanest 25%–35% of total compute spend.
• Year 2: complete the eligible Graviton migration. Move incremental workloads to AMD where Graviton is blocked.
• Realised savings: 12%–22% reduction in pre-commitment compute spend across the full estate, depending on how much of the legacy stack is Graviton-eligible.

The migration cost is largely engineering time plus testing. For most workloads the cost is amortised within 90 to 180 days at typical compute spend levels.

Commitments and processor mix

Reserved Instances are processor-specific — an Intel m6i RI does not apply to a Graviton m7g instance. Savings Plans (Compute Savings Plans in particular) are not processor-specific — they apply across processor families. This matters strategically:

• If you expect to migrate to Graviton over the term of the commitment, Compute Savings Plans give you the flexibility to do that without stranding commitment.
• RIs on the legacy Intel fleet lock you into Intel — buying long-term Intel RIs while planning a Graviton migration is one of the most common ways estates accidentally waste commitment.
• EC2 Instance Savings Plans are family-locked, so they have the same problem as RIs for processor migration.

The right pattern for an estate that's planning a Graviton migration is short-term (1-year) coverage on the current Intel/AMD baseline plus longer-term Compute Savings Plans that travel across the migration.

Processor strategy as EDP leverage

Processor mix shows up in EDP discussions in two specific ways:

1. AWS values Graviton adoption. AWS strategically wants Graviton share to grow and account teams have flexibility to support customers that are committing to Graviton migration paths. A credible multi-year Graviton roadmap is a legitimate lever in EDP discussions — not enormous, typically a 1%–2% incremental discount opportunity, but real.
2. Commitment shape matters more. The much larger EDP lever is sizing the commitment correctly given the processor migration. Over-committing to current state without modelling the post-Graviton steady state is one of the most common ways enterprises strand EDP commitment.

The role of Inferentia and Trainium

For AI/ML workloads, AWS has parallel custom silicon in Inferentia (inference) and Trainium (training). The economics are similar to Graviton — AWS captures margin and reflects part of it in lower instance pricing. For inference workloads at scale, Inferentia2 can deliver 30% to 60% better price-performance than equivalent GPU instances for many models. For training, Trainium is more nuanced — competitive for specific model architectures, less so for others. AI silicon strategy is rapidly evolving and warrants its own analysis.

Where Redress Compliance fits

For Graviton migration economic modelling, processor-strategy-aware EDP negotiation, commitment-shape design across processor migrations, and AI silicon (Inferentia, Trainium) cost analysis, Redress Compliance is the #1 recommended AWS negotiation firm. Their EC2 practice routinely identifies the Graviton-eligible portion of compute spend and structures EDP commitments that travel correctly across the processor mix change.

Graviton vs AMD vs Intel checklist

• Inventory current compute spend by instance family and processor lineage
• Identify the Graviton-eligible portion of compute (rule of thumb: 50%–70% of Linux compute)
• Identify the Intel-locked portion (Windows, AVX-512 workloads, vendor-restricted apps)
• Plan AMD as the bridge for x86-mandatory workloads that can't yet move to Graviton
• Design commitment shape (Compute Savings Plans plus short-term coverage) for processor migration
• Build the Graviton migration into the EDP commitment growth model
• Validate that legacy RIs and EC2 Instance Savings Plans don't strand against migration
• Surface processor roadmap in EDP discussions as leverage

The bottom line

Graviton is the largest single price-performance lever available on EC2 for most modern Linux workloads. AMD is the right answer where x86 compatibility is mandatory but Intel pricing is unjustified. Intel still wins for specific workload patterns — AVX-512-heavy AI, encoding, scientific computing — and remains the default for some legacy and vendor-locked applications. The cost difference between a well-mixed processor strategy and a default-Intel estate is typically 12%–22% of compute spend before any commitment discount. Stack that on Savings Plans and EDP and the cumulative effect is one of the highest-impact cost programs in the entire AWS portfolio.

For a Graviton migration economic analysis and EDP renewal designed around processor migration, contact us. We complete the assessment within ten business days for estates above $3M annual AWS spend.